# Qwen3.8

> Source: https://aiwiki.ai/wiki/qwen3_8
> Updated: 2026-09-23
> Fact-checked: 2026-09-23
> Categories: AI Models, Chinese AI, Large Language Models, Reasoning Models
> License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) - attribute to "AI Wiki (aiwiki.ai)"
> Cite as: AI Wiki. "Qwen3.8." aiwiki.ai, 23 Sept 2026. https://aiwiki.ai/wiki/qwen3_8
> From AI Wiki (https://aiwiki.ai), the free encyclopedia of artificial intelligence. Reuse freely with attribution.

**Qwen3.8** is the name [Qwen](https://aiwiki.ai/wiki/qwen) uses for a model generation that includes hosted services and downloadable checkpoints. [Alibaba Cloud](https://aiwiki.ai/wiki/alibaba_cloud) introduced the mutable `qwen3.8-max-preview` service on July 19, 2026, released [Qwen3.8-Max](https://aiwiki.ai/wiki/qwen3_8_max) as a hosted production model on August 3, and subsequently published open weights for a 2.4-trillion-parameter mixture-of-experts checkpoint and the smaller Qwen3.8-27B.[1][2][12][14][20]

The names do not identify one interchangeable model. Alibaba describes the Max system as having 2.4 trillion total parameters and 95 billion active parameters. The Qwen3.8-27B repository contains a dense, native vision-language model with exactly 27,781,427,952 parameters in its BF16 safetensors metadata.[15] The large open checkpoint and the 27B checkpoint also differ in modalities, licenses, and hardware requirements.

The release history changed the status documented in early versions of this article. No Qwen3.8 checkpoint, license, or [model card](https://aiwiki.ai/wiki/model_card) was public by July 25, and the promised weights were still absent on August 5.[9][12] Qwen's GitHub organization still highlighted Qwen3.6 rather than Qwen3.8 during the July review.[10] Qwen's 2.4T-A95B repository was public by August 12 under a custom Qwen3.8-Max license, and Qwen3.8-27B downloads followed on August 14 under Apache License 2.0.[14][15][20][21]

Repository history separates staging from public release. An initial placeholder was created August 5, weight shards and core files were uploaded August 13, and the Apache 2.0 license file was added at 04:15 UTC August 14. Qwen announced the downloadable 27B release at 15:02 UTC that day.[22][23]

## Release and preview status

Qwen announced `qwen3.8-max-preview` during the 2026 [World Artificial Intelligence Conference](https://aiwiki.ai/wiki/world_artificial_intelligence_conference) in Shanghai. Alibaba's corporate report the next day confirmed availability through Token Plan, Qoder, and QoderWork. It repeated the 2.4T parameter figure, an internal claim that the model ranked second only to Fable 5, and the plan to publish open weights for Qwen3.8-Max.[1][2]

The preview did not remain fixed. On July 21, Qwen said another build had gone live and claimed better frontend and WebDev performance. The announcement supplied no score, benchmark definition, or build identifier. Qwen also said the model was changing daily and invited feedback through Token Plan, Qoder, QoderWork, and Qwen Studio.[8] QwenCloud warns that preview models may be improved, taken offline, or replaced, and its model table did not expose dated Qwen3.8 snapshots.[3][4] Results from the preview therefore need both a test date and a description of the client or endpoint.

On August 3, Alibaba released Qwen3.8-Max as a hosted production model. It reported 95 billion active parameters, pricing of $2 per million input tokens and $6 per million output tokens, and said weights for Qwen3.8-Max and a smaller Qwen3.8-27B would appear on Hugging Face and ModelScope the following week.[12][13] The downloadable 2.4T-A95B repository and 27B repository fulfilled that promise on different terms rather than as identical copies of the hosted service.[14][17][20]

Two further Qwen3.8-branded releases followed and have their own articles. On August 26, Qwen published [Qwen3.8-Flash-Next](https://aiwiki.ai/wiki/qwen3_8_flash_next), which it presents as an architecture preview for a later Qwen4 family rather than a member of the Qwen3.8 line proper; its Hugging Face repositories were created on August 24.[52][53] On September 2, Qwen announced the dated hosted snapshot `qwen3.8-max-0902`, described in its post as "further post trained on Coding & Cowork" at the same $2 per million input and $6 per million output token prices.[54] Neither release changes the 27B checkpoint documented below.

## Size, architecture, and open weights

| Release | Architecture and size | Input and output | Context | License |
| --- | --- | --- | --- | --- |
| Qwen3.8-2.4T-A95B | Mixture of experts, 2.4T total and 95B active | Text input, text output; thinking required | 262,144 native, extensible to 1,010,000 | Custom Qwen3.8-Max license |
| Qwen3.8-27B | Dense, 27,781,427,952 parameters | Text, image, and video input; text output; thinking optional | 262,144 native, extensible to 1,000,000 | Apache 2.0 |

The 2.4T-A95B checkpoint has 512 experts and activates 10 routed experts plus one shared expert. Its model card describes it as text-only and requires thinking mode for every response. The hosted Qwen3.8-Max service is based on this checkpoint but adds vision input, non-thinking support, a 1M default context, and hosted tools. Those product features should not be assumed when serving the checkpoint independently.[20]

Qwen3.8-27B is a separate dense model. Its configuration identifies `Qwen3_5ForConditionalGeneration`, and Qwen says the architecture builds on Qwen3.5. The language stack has a hidden width of 5,120 and 64 layers arranged as 16 groups, each with three Gated DeltaNet blocks followed by one full-attention block. This yields 48 linear-attention blocks and 16 full-attention blocks. The card also lists multi-token prediction training with multiple steps.[14]

The official Qwen3.8-27B repository is the post-trained model, not a repository labeled as a base checkpoint. It includes 18 BF16 safetensor shards, tokenizer and chat-template files, and separate image and video preprocessing configurations. Hugging Face reports 55,575,816,096 bytes of stored BF16 weights and supporting files, approximately 55.6 GB in decimal units.[14][15]

Qwen released more than one representation of the 27B model:

| Artifact | Publisher | Precision or format | Approximate stored size |
| --- | --- | --- | ---: |
| Qwen3.8-27B | Qwen | BF16 safetensors | 55.6 GB |
| Qwen3.8-27B-FP8 | Qwen | Mixed FP8 and BF16 safetensors | 30.9 GB |
| Qwen3.8-27B | Qwen on ModelScope | BF16 mirror | 55.6 GB |
| Qwen3.8-27B-GGUF | Unsloth | Third-party GGUF quantizations | About 9 GB to 55 GB |

The official FP8 repository preserves the same total parameter count but does not store every parameter in FP8. Its metadata assigns 24,699,207,680 parameters to FP8 and 3,082,220,272 to BF16.[16] Qwen's official ModelScope mirror reports the same exact total parameter count for its BF16 checkpoint.[17] Unsloth's GGUF files are third-party conversions. Their language-model metadata counts 27,320,697,856 parameters because the visual projector is distributed separately; multimodal use also requires an `mmproj` file of about 0.93 GB.[18]

## Documented capabilities

The hosted preview's exact service identifier in QwenCloud documentation is `qwen3.8-max-preview`. It was listed as Token Plan only and recommended for the service's strongest reasoning tier.[3]

| Capability | Hosted preview documentation |
| --- | --- |
| Context | 1M tokens |
| Thinking | Supported |
| [Function calling](https://aiwiki.ai/wiki/function_calling) | Supported |
| Built-in tools | Supported |
| Structured output | Not listed |
| Availability | Token Plan only |

The 1M figure is a [context window](https://aiwiki.ai/wiki/context_window), not an output limit. QwenCloud's model table did not list a maximum output length or thinking budget for the preview even though it supplied those fields for older models.[3] Its OpenAI-compatible chat reference documented `preserve_thinking` as true by default and accepted `low`, `medium`, and `xhigh` reasoning effort. It mapped the default thinking budget to 131,072 tokens and `xhigh` to 262,144 tokens.[6]

Qwen3.8-27B uses similar controls in its released chat template. Thinking is on by default but can be disabled, reasoning effort can be set to `low`, `medium`, or `xhigh`, and historical reasoning can be retained with `preserve_thinking`.[14][24][25] The supplied template also formats tool calls, but actual tool execution depends on the serving framework and application.[25]

The self-hosted template makes `xhigh` the default. These levels are prompt-template behavior rather than fixed local compute or token budgets: `xhigh` inserts an instruction to validate assumptions and consider alternatives, `low` requests brief and focused reasoning, and `medium` adds no separate effort instruction. Unsupported values make template rendering fail. `preserve_thinking` defaults to true; setting it to false omits older reasoning blocks before the most recent user query while retaining reasoning in the active sequence after that query.[25] The QwenCloud budget values therefore do not establish corresponding budgets in a self-hosted backend.[6][25]

The 27B card calls the checkpoint a causal language model with a vision encoder. It provides examples for text, image, and video input and generates text responses. Qwen also describes hour-scale video understanding, but its card recommends changing the video preprocessor to permit as many as 224,000 video tokens for that use. This is an optional high-context configuration, not the repository default.[14]

The checkpoint natively supports 262,144 tokens. This is a total input-plus-output limit. Qwen documents extension to 1,000,000 tokens through YaRN, but warns that the static YaRN implementations in common serving frameworks can reduce performance on shorter text. Qwen advises enabling scaling only for workloads that need it and adjusting the scaling factor to the expected length.[14] Its 1M example uses a scaling factor of 4.0, while the guide recommends 2.0 when an application's typical total length is about 524,288 tokens.[24] The card said a future hosted Qwen3.8-27B service would use a 1M context by default, but labeled that service as coming soon at release.

## Access and pricing

QwenCloud offered the preview through Token Plan Personal and Team editions. Token Plan deducts Credits across models and tools. On July 25, the limited-time Personal Edition prices were $6 per month for Lite, $18 for Standard, and $68 for Pro. The plans carried five-hour limits of 700, 3,000, and 12,000 Credits and seven-day limits of 2,500, 10,000, and 40,000 Credits.[4]

Those subscription prices were not standalone Qwen3.8 prices per input or output token. QwenCloud says Credit use varies with model, token use, thinking, and tool calls. Personal Edition terms also restrict its key to interactive use in programming and agent tools. Automated scripts, application backends, and non-interactive batch processing are prohibited under that plan.[4]

The preview received separate promotional treatment in Qoder CN. Alibaba reduced its Credits coefficient from 0.5x to 0.05x during regular hours and 0.01x between 22:00 and 08:00 China Standard Time. The promotion began July 19 without a published end date, and Alibaba reserved the right to change or end it.[5] These temporary multipliers are distinct from the production Qwen3.8-Max API price announced August 3 and from self-hosting downloaded weights.[12]

## Local inference and quantization

[Unsloth](https://aiwiki.ai/wiki/unsloth) publishes third-party [GGUF](https://aiwiki.ai/wiki/gguf) conversions of Qwen3.8-27B for [llama.cpp](https://aiwiki.ai/wiki/llama_cpp) and compatible runtimes. On August 19, 2026, it refreshed the repository with **Dynamic V3**, a model-specific [post-training quantization](https://aiwiki.ai/wiki/quantization) scheme.[37][42] Unsloth says it uses a new importance-matrix calibration set spanning agentic coding, chat and multilingual material, changes precision by layer, and combines multiple GGUF quantization types. It says the calibration examples are not used to train the model and that the process uses neither quantization-aware training nor quantization-aware distillation.[37] These are Unsloth conversions and measurements, not official Qwen quantizations; the official Qwen3.8-27B BF16 checkpoint remains the upstream model.[14]

### Dynamic V3 artifact sizes and memory

The repository is mutable, so file figures need a revision and date. At Hugging Face revision `4ca720788d1e01f1bff70c033e0d0028fd02e502`, last modified August 20 at 12:04 UTC, the repository was public and ungated. Its API reported the following exact byte counts:[38]

| Artifact | Exact bytes | Decimal GB | What it contains |
| --- | ---: | ---: | --- |
| `UD-IQ1_S` | 6,192,222,208 | 6.19 | Smallest text-model GGUF; MTP omitted |
| `UD-IQ1_M` | 6,729,166,848 | 6.73 | Text-model GGUF; MTP omitted |
| `UD-IQ2_XXS` | 7,266,070,528 | 7.27 | Text-model GGUF; MTP omitted |
| `UD-IQ2_S` | 8,371,970,048 | 8.37 | Text-model GGUF; MTP omitted |
| `UD-Q2_K_XL` | 9,828,981,664 | 9.83 | Text-model GGUF |
| `UD-Q4_K_XL` | 17,559,178,144 | 17.56 | Text-model GGUF in Unsloth's recommended local tier |
| `mmproj-F16.gguf` | 927,607,488 | 0.928 | Optional vision projector |
| `mmproj-BF16.gguf` | 931,146,432 | 0.931 | Optional vision projector |
| Separate Q4_0 MTP module | 1,369,590,656 | 1.370 | Optional speculative-decoding helper |

Unsloth says it removed the MTP tensors from files at 8.37 GB and below; users who want speculative decoding with those files can add the separate Q4_0 MTP module.[37][38] Text-only inference does not need either vision projector. Image or video input needs one of them, while MTP remains optional for ordinary autoregressive generation.

That distinction changes the meaning of the headline sizes. The 6.19 GB `UD-IQ1_S` text file plus the F16 projector is 7,119,829,696 bytes, or 7.12 decimal GB. Adding the separate MTP module raises the stored artifacts to 8,489,420,352 bytes, or 8.49 GB, before any runtime allocation.[38] None of those sums is a RAM or VRAM measurement.

Unsloth's own total-memory guidance was not internally uniform on August 20:[19][39]

| Quantization tier | Full guide: total RAM + VRAM or unified memory | Separate product page | Current text-model file range |
| --- | ---: | ---: | ---: |
| 1-bit | 7 to 8 GB | Not listed | 6.19 to 6.73 GB |
| 2-bit | 9 to 11 GB | 11 to 13 GB | 7.27 to 9.83 GB |
| 3-bit | 12 to 14 GB | 13 to 16 GB | 10.93 to 13.15 GB |
| 4-bit | 16 to 19 GB | 17 to 19 GB | 14.25 to 17.56 GB |
| 6-bit | 23 to 26 GB | 24 GB | 21.98 to 25.30 GB |
| 8-bit | 31 GB | 31 GB | 28.05 to 31.46 GB |
| BF16 | 56 GB | 56 GB | 54.66 GB |

The first two columns are publisher planning guidance, not measured universal minima. The pages do not specify one shared backend, context length, cache precision, concurrency, vision setting or buffer policy that reconciles their low-bit ranges.[19][39] A model file is only the static weight artifact. Runtime memory also includes compute buffers and backend overhead; a vision request adds a projector; MTP can add its own weights; and KV or recurrent-state cache depends on context, cache format, active sequences and concurrency. Qwen documents a native 262,144-token total context, but configuring that maximum does not establish that it fits any particular memory figure.[14] CPU or disk offload may let a configuration start when faster memory is insufficient, at a potentially substantial speed cost.[19]

### What top-1 agreement means

Unsloth's August 19 social launch copy said the one-bit quants retained 77 percent "accuracy."[42] As accessed August 20, its current Dynamic V3 technical page instead says the 6.2 GB `UD-IQ1_S` retains **around 72 percent top-1 accuracy**.[37] In this test, "top-1" is next-token agreement: whether the quantized model's highest-probability next token matches BF16's argmax on Unsloth's evaluation inputs. It is not 72 percent retained intelligence, task accuracy, answer correctness or downstream capability. Unsloth itself says a one-step argmax comparison is not an effective gauge of actual inference.[37]

Unsloth also reports a vendor-designed **Divergence-300 @32** suite. It uses 300 held-out prompts drawn from Terminal-Bench 2.1, DeepSWE, Harbor, MathArena 2025-26, non-Latin material and long documents, then compares 32-token greedy trajectories. Its KL-divergence, top-1 and "more than 10 percent better at the same size" results were produced by Unsloth, not an independent lab.[37]

Peer-reviewed work explains why that boundary matters. A NeurIPS 2024 study found that compressed and baseline models could have similar aggregate benchmark accuracy even while individual answers flipped in both directions and free-form generation degraded; it recommended also measuring behavioral distance.[40] A Findings of ACL 2024 evaluation treated knowledge, alignment and efficiency as separate dimensions and found that quantization's speed effects depended on engineering and hardware support.[41] Neither paper evaluated Qwen3.8-27B or Dynamic V3. No independent reproduction of Unsloth's Qwen3.8 suite or controlled downstream evaluation of the released `UD-IQ1_S` file was located by August 20, 2026.

### Serving recipes and measured configurations

Qwen links dedicated deployment material for vLLM, SGLang, TokenSpeed, and Unsloth.[14][24] The vLLM recipe lists vLLM 0.17.0 as its minimum and `transformers>=5.8.0` for the multimodal processor classes. It marks GB300 as the only end-to-end verified hardware entry and gives sizing estimates of 67 GB for Qwen's BF16 checkpoint, 38 GB for its official FP8 checkpoint, and 32 GB for a third-party NVFP4 build. The recipe derives those thresholds from stored model bytes plus 20 percent headroom, so they are not measured universal runtime minima. Context cache, vision processing, concurrency, and backend buffers require additional memory.[26]

The SGLang cookbook publishes single-GPU launch cells for H200, RTX PRO 6000, RTX 5090, and DGX Spark, but its configuration notes say the SM121 path was not yet validated. It also says the official FP8 weights are not serviceable beyond batch size two on 32 GB cards and recommends a smaller third-party NVFP4 build for RTX 5090-class hardware.[27] TokenSpeed's reference launches the official FP8 checkpoint at world size one with a 262,144-token maximum and same-checkpoint multi-token prediction, but names no GPU and publishes no throughput for that command.[28] These recipes establish software pathways, not one cross-platform hardware requirement or performance guarantee.

Published measurements are similarly configuration-specific. The developer of the NInfer-3090 fork tested a 16.96 GiB third-party groupwise artifact on one 24 GB RTX 3090 with MTP3, 29-to-34-token prompts, 1,024 generated tokens, and an 8,192-token maximum context. The report gave 70.19 end-to-end tokens per second and 19,641 MiB peak VRAM at concurrency one.[29] Its release note says the raw benchmark logs remained local rather than being included with the source, so those numbers are developer-reported, not an independent replication.[29] A separate DGX Spark user running Qwen's official FP8 checkpoint through vLLM in text-only mode, with MTP disabled and a 262,144-token configured maximum, reported 8.13 aggregate tokens per second at concurrency one and 61.61 at concurrency eight for 512-token outputs.[30] These results are not directly comparable because the runtime, quantization, speculative decoding, prompt length, context allocation, hardware, and concurrency all differ. Neither establishes general Qwen3.8-27B speed or quality.

### AMD local deployment demonstrations

On August 14, 2026, [AMD](https://aiwiki.ai/wiki/amd) published preliminary Qwen3.8-27B generation measurements for two local systems using llama.cpp on Windows 11 Pro 25H2 with the Vulkan backend. AMD reported up to 24.5 tokens per second on a GMKtec EVO X2 AI Mini PC with a Ryzen AI Max+ 395 processor, 128 GB of system memory, 64 GB assigned as variable graphics memory, MTP set to four draft tokens, Adrenalin 26.7.1, and AMD chipset driver 8.05.04.516. It reported up to 51.8 tokens per second on one Radeon AI PRO R9700 in a Ryzen 9 9950X system with 64 GB of system memory and MTP set to two draft tokens. Each figure was the average token-generation throughput from at least three AMD runs.[43]

AMD labeled both results preliminary and did not identify the exact quantized artifact, prompt lengths, generated-token counts, context allocation, sampling settings, or whether the displayed maximum occurred in every run. The two figures therefore describe separate AMD test configurations rather than a platform comparison or a general Qwen3.8-27B speed guarantee. AMD also estimated that LM Studio deployments need about 24 GB of variable graphics memory or VRAM and advised disabling "Try mmap", but this was vendor setup guidance rather than a universal minimum.[43]

In a separate August 27 demonstration, AMD said it ran two Qwen3.8-27B model instances on one 128 GB Ryzen AI Max+ processor-based computer through the Windows Hermes Agent desktop bot mode.[44] The post showed that AMD had demonstrated this two-instance arrangement, but it did not disclose the quantization, context per instance, memory allocation, workload, throughput, or duration. It does not establish that two full-precision checkpoints fit, or that two-instance serving will have the same behavior under other runtimes and workloads.

### NVIDIA Jetson and the NVFP4 plus DFlash2 recipe

[NVIDIA](https://aiwiki.ai/wiki/nvidia) treated the 27B checkpoint as a launch-day target for its own local hardware. A rolling NVIDIA blog post dated August 11, 2026 and extended through August 25 said that "NVIDIA Jetson brings day-zero support for Qwen3.8-27B to the edge," crediting "speculative decoding through MTP" for responsive local inference; the same post reported 131 tokens per second on a single GeForce RTX 5090 with MTP (an NVIDIA measurement on a Core Ultra 9 285K system with 64 GB of RAM under Windows 11) and said NVFP4 support for its local devices was "coming soon."[45] NVIDIA Robotics posted on August 14, the day the weights went public, that the model was "ready for the edge" on [NVIDIA Jetson](https://aiwiki.ai/wiki/nvidia_jetson).[46] The Jetson AI Lab model page lists a llama.cpp container serving a Q4_K_M GGUF, with MTP enabled in its commands, for Jetson AGX Thor (T5000 and T4000), Jetson AGX Orin 64GB, Jetson Orin NX 16GB, and Jetson Orin Nano 8GB; the page is undated and publishes no throughput figures.[47]

The NVFP4 path that NVIDIA later recommended runs on checkpoints that Alibaba did not publish. Both NVIDIA-recommended checkpoints are third-party artifacts on Hugging Face under Apache 2.0; the community DSpark draft that Inco AI used as a comparison point is also listed:

| Artifact | Publisher | Created | What it is |
| --- | --- | --- | --- |
| Inferact/Qwen3.8-27B-NVFP4 | [Inferact](https://aiwiki.ai/wiki/inferact), the company formed by vLLM's maintainers | August 13, 2026 | NVFP4 quantization of Qwen/Qwen3.8-27B; the card says only "Quantized version of" the official repository, with no recipe or accuracy data [48] |
| incoai/Qwen3.8-27B-DFlash2 | [Inco AI](https://aiwiki.ai/wiki/inco_ai), which says its team released DFlash | August 18, 2026 | DFlash 2 block-diffusion draft model for speculative decoding; not a standalone language model; the same drafter also appears as z-lab/Qwen3.8-27B-DFlash2, created a few days earlier [49][50] |
| RadixArk/Qwen3.8-27B-DSpark | RadixArk (community) | August 14, 2026 | DSpark draft model, used as a comparison point in Inco AI's evaluation [49] |

Inco AI describes DFlash 2, its successor to [DFlash](https://aiwiki.ai/wiki/dflash), as a drafter that "predicts a whole block of tokens in a single pass and keeps the top candidates at every position," with a selector tracing one path through them, and says decoding is lossless: greedy output matches the target model and sampling preserves its distribution.[49][50] Its own evaluation, run with [SGLang](https://aiwiki.ai/wiki/sglang) on one H200 at seven draft tokens per verification step and `xhigh` reasoning effort, reports acceptance lengths of 5.46 on GSM8K, 5.28 on MATH-500, and 4.39 on HumanEval for DFlash 2, against 5.02, 4.72, and 3.91 for Qwen's built-in seven-token MTP and 4.36, 3.92, and 3.30 for the RadixArk DSpark draft. At concurrency 1 it reports 236.1 output tokens per second on GSM8K (3.43x autoregressive decoding); at concurrency 32 the advantage shrinks to 1.01x to 1.45x, and MTP and DSpark fall below autoregressive throughput on several tasks.[49] These are the publisher's numbers on data-center hardware, not Jetson measurements.

On September 4, 2026, NVIDIA's Technical Blog post "Frontier Reasoning Reaches the Edge," by Khalil BenKhaled, Aditya Sahu, and Chitoku Yato, used Qwen3.8-27B and [Nemotron 3.5 Lightning](https://aiwiki.ai/wiki/nemotron_3_5_lightning) as its two worked examples of 2026 open reasoning models on Jetson.[51] It contrasts the dense 27B model, which "activates all 27 billion parameters for each token," with Lightning's 30B mixture-of-experts design that activates 3 billion, and draws a workload distinction: "Qwen3.8-27B is a better fit for tasks that require fewer, harder decisions and allow the agent to spend more time generating each response," while Lightning suits "response-heavy workflows." NVIDIA Robotics restated this on X the same day: "Qwen3.8-27B suits fewer, more complex decisions."[51][55] The post names [Gemma 4](https://aiwiki.ai/wiki/gemma_4) E4B as the starting point for Jetson Orin Nano and Qwen3.8-27B or Lightning for Jetson AGX Orin and [Jetson AGX Thor](https://aiwiki.ai/wiki/jetson_thor).[51]

NVIDIA's Figure 2 stacks two optimizations on a BF16 baseline. For Qwen3.8-27B it reports a 2.33x decode-throughput gain from [NVFP4](https://aiwiki.ai/wiki/nvfp4) quantization alone and 6.28x from NVFP4 combined with DFlash2 [speculative decoding](https://aiwiki.ai/wiki/speculative_decoding), the largest gain in the post and the source of its "up to 6.28x" caption; NVIDIA says it tested the available draft methods and checkpoints for each model and found DFlash2 fastest for this one. With that configuration fixed across four SpeedBench workload categories (writing, reasoning, summarization, and retrieval-augmented generation), Qwen3.8-27B with DFlash2 produced 27.69 to 34.44 output tokens per second, with RAG and writing benefiting most.[51] The post's prerequisites are "a Jetson AGX Thor or Jetson AGX Orin" running JetPack 7.2, but it does not state which module produced the Figure 2 speedups or the Figure 3 token rates, so the figures are "on Jetson" without a module attached.[51]

| Measurement (NVIDIA-reported) | Qwen3.8-27B | Caveat |
| --- | ---: | --- |
| Decode throughput, NVFP4 vs BF16 | 2.33x | Figure 2; Jetson module not named [51] |
| Decode throughput, NVFP4 + DFlash2 vs BF16 | 6.28x | Figure 2; Jetson module not named [51] |
| SpeedBench output speed with DFlash2, four categories | 27.69 to 34.44 tokens/s | Figure 3; Jetson module not named [51] |
| RTX 5090 output speed with MTP | 131 tokens/s | NVIDIA desktop test, not Jetson [45] |

The reference command runs in the `vllm/vllm-openai:v0.28.0` container ([vLLM](https://aiwiki.ai/wiki/vllm) v0.28.0 was tagged on August 26, 2026) with the environment variable `VLLM_GDN_DECODE_KERNEL=triton`, serves `Inferact/Qwen3.8-27B-NVFP4` under the name `qwen38` with the `qwen3` reasoning parser and `qwen3_coder` tool-call parser, caps the model length at 50,000 tokens and concurrent sequences at 8, sets GPU memory utilization to 0.85, and enables a `dflash` speculative configuration pointing at `incoai/Qwen3.8-27B-DFlash2` with `num_speculative_tokens` set to 7.[51][56] The same blog notes that DFlash "currently supports the widest selection of compatible draft checkpoints" while DSpark "supports fewer checkpoints," and advises training a custom speculator with the vLLM Speculators guide, or re-tuning a quantized model with NVIDIA Model Optimizer, only when measurements on the developer's own prompts show a gap.[51]

The post's Figure 1, which NVIDIA Robotics also published as a standalone chart, plots [Artificial Analysis](https://aiwiki.ai/wiki/artificial_analysis) Intelligence Index 4.1 scores against release date and marks a green "Runs Real-time on Jetson" group: Qwen 3.8 27B sits at the top of that group, above Gemma 4 31B, Qwen 3.6 27B, Qwen 3.6 A3B 35B, Gemma 4 A4B 26B, the Qwen 3.5 4B, 9B, and 14B models, Muse Glimmer 30B, and Nemotron 3.5 Lightning 30B, while grey points for 2025 models such as Claude Opus 4.5, GPT-5, and DeepSeek R1 0528 all sit below Qwen 3.8 27B (Claude Opus 4.5 is plotted level with Gemma 4 31B and above the rest of the green group). The chart is NVIDIA's rendering of Artificial Analysis data and carries no numeric axis labels, so it shows relative placement rather than scores.[51][55]

### Apple silicon: Splash with a DFlash 2 draft

Inco AI's [Splash](https://aiwiki.ai/wiki/splash_inference_engine), announced in a post dated September 17, 2026 and published on GitHub under Apache-2.0 on September 18, is a local inference engine for Apple silicon Macs that supports two models at launch, Qwen3.8-27B and [Qwen3.6](https://aiwiki.ai/wiki/qwen3_6)-35B-A3B. Each ships as a package with its own DFlash 2 draft, kernels written for that model's shapes and a memory plan that Splash computes for the machine at startup, and speculative decoding is the engine's only decode path rather than an option.[59][60] The incoai/Qwen3.8-27B-Splash package (a 17.4 GB download) combines mlx-community's 4-bit conversion of Qwen3.8-27B (14.1 GiB) with a bf16 vision encoder (0.9 GiB) and a five-layer DFlash 2 draft (1.2 GiB) that reads the target's hidden states at five layers and proposes seven tokens per step. Splash requires an M3 or newer Mac on macOS 26.4 or later with at least 36 GB of unified memory; Inco recommends 48 GB or more. Reasoning defaults to `xhigh` in this package.[61] LM Studio's September 18 guide installs Splash as an experimental "Splash (Metal)" backend in LM Studio Bionic 1.1.5 or newer.[64]

Inco's comparison ran every engine on the same M5 Pro (16-core GPU, 48 GB) over HTTP at each engine's recommended settings, using SPEED-Bench coding prompts up to 32K tokens, a 1,024-token output limit and medium reasoning effort for the 27B; decode figures include reasoning tokens and single-request values are medians. Because each engine used its own settings, Inco describes the results as end-to-end comparisons rather than measurements of any one component. uzu's Qwen3.8-27B package has no draft model.[59][60][61]

| Qwen3.8-27B, single request (Inco-measured, M5 Pro 48 GB) | Short prompt | 8K | 16K | 32K |
| --- | ---: | ---: | ---: | ---: |
| Splash decode, tokens/s | 74 | 55 | 55 | 54 |
| oMLX decode, tokens/s | 38 | 33 | 29 | 28 |
| Ollama decode, tokens/s | 24 | 21 | 21 | 19 |
| uzu decode, tokens/s | 19 | 18 | 17 | 15 |

In the same setup Inco reported 170 tokens per second of combined decode for four concurrent short prompts on the 27B (oMLX: 43, a 3.9x ratio), 363 tokens per second of prefill on a 32K prompt (uzu: 313), and a 282 ms time to first token when a cached 32K prompt was replayed (oMLX: 2,049 ms). The replay test isolates the cached path; a real agent turn also pays for newly appended tokens.[59][61]

The figure most widely repeated at launch, 144 tokens per second, comes from a different chip. Inco's announcement on X cited "Qwen3.8-27B at 144 tok/s on an M5 Max MacBook Pro," LM Studio repeated "up to 144 tok/sec on M5 Max," and Zhijian Liu wrote that DFlash 2 had run the model at 70 tokens per second on an M5 Max a month earlier.[62][63] The launch post, README and model card report only the M5 Pro results above.[59][60][61] All of these are Inco's own measurements, not independent tests.

## Multimodality documentation conflict

Alibaba's July documentation did not give one consistent account of the hosted preview's inputs. The Token Plan model list labeled `qwen3.8-max-preview` as supporting visual understanding and text generation.[4] A Qwen Code article published July 21 instead said the preview lacked multimodal capability and described routing a screenshot through Qwen3.6-Plus before sending a text description to Qwen3.8.[7] Press reports also attributed image, video, and document processing to the launch, while the live text-generation table did not document video input.[3][9]

The open model cards clarify the released checkpoints but do not retroactively resolve every preview client. Qwen3.8-27B is documented as native image and video understanding with text output.[14] Qwen3.8-2.4T-A95B is documented as text-only, while the managed Qwen3.8-Max product adds vision.[20] Applications should still check the modalities of the exact endpoint or files they use.

## Evaluation and naming

Alibaba's launch claim that the preview ranked behind only Fable 5 came without benchmark names, scores, prompts, or sampling settings.[2][9] Qwen later published a broad vendor benchmark table for Qwen3.8-27B. A representative subset shows that different models lead on different tests:[14]

| Benchmark | Qwen3.8-27B | Qwen3.6-27B | Qwen3.7-Plus | Opus4.6 Max |
| --- | ---: | ---: | ---: | ---: |
| Terminal Bench 2.1 | 73.0 | 63.4 | 64.0 | 78.2 |
| SWE-bench Pro | 61.7 | 53.5 | 57.6 | 53.4 |
| NL2Repo-Bench | 42.3 | 36.2 | 41.1 | 47.6 |
| QwenSWEBench | 79.0 | 49.3 | 59.2 | 63.8 |
| CoWorkBench | 70.7 | 61.0 | 65.1 | 68.2 |
| GPQA Diamond | 89.2 | 87.8 | 90.3 | 91.3 |
| LiveCodeBench v6 | 90.3 | 83.9 | 89.6 | 88.8 |

The table does not establish that Qwen3.8-27B categorically outperforms Opus4.6 Max. Qwen3.8-27B leads the cited comparator on SWE-bench Pro, QwenSWEBench, CoWorkBench, and LiveCodeBench v6, while Opus leads on Terminal Bench 2.1, NL2Repo-Bench, and GPQA Diamond.[14]

The methodology is also mixed. For SWE-bench Pro, Qwen used the officially reported Opus score but re-evaluated the other models with a Claude Code harness, selected sampling settings, a 256K context, and corrected tasks. QwenSWEBench and CoWorkBench are in-house benchmarks. HLE uses GPT-4o as a judge, and several vision evaluations use corrected labels, differing prompts, or model judges.[14] These are useful vendor results, not a uniform independent comparison. Early runtime reports measured configuration-specific inference throughput, not Qwen's capability table.[29][30] Artificial Analysis later published separate, independently run evaluations, but it used different tasks and a separate harness rather than reproducing Qwen's table.[31][32][33][34] No controlled independent reproduction of that table or peer-reviewed Qwen3.8-27B paper was located by August 19, 2026.

Qwen's public materials also do not disclose the 27B model's training corpus, data cutoff, token count, training compute, detailed post-training data, or a complete safety evaluation.[14] Apache 2.0 permits broad reuse of the released 27B files, but it does not make the training data or process public.

### Independent benchmark snapshot

On an August 19, 2026 snapshot, Artificial Analysis reported an Intelligence Index score of 52.02 and an Agentic Index score of 50.877 for Qwen3.8-27B.[31][32] On the same Agentic leaderboard, its score was 0.68 points above GPT-5.6 Terra at max effort, 1.31 above DeepSeek V4 Pro 0813 at max effort, 1.57 above Muse Spark 1.2 at xhigh effort, and 3.39 below Kimi K3 at max effort.[32] These are dated leaderboard comparisons, not a permanent rank. The public Agentic page did not give a Qwen-specific confidence interval, so the score gaps do not demonstrate statistical separation; additions or reruns can also change the ordering.

The Agentic Index equally weights two evaluations. GDPval-AA v2 uses the 220-task public gold subset of GDPval, covering work from 44 occupations across nine major US economic sectors. Artificial Analysis runs each task once in its Stirrup agent harness, grades file deliverables through blind pairwise comparisons by judges sampled from a panel of three frontier language models, and derives an Elo score anchored to human experts at 1,000.[33][34][35] The other half is tau3-Banking: 97 banking-support tasks run five times each over about 700 interconnected policy documents. Artificial Analysis enables BM25 and grep retrieval, scores backend state with pass@1 averaged across repeats, and uses GPT-5.4 Mini as the user simulator and for natural-language assertions.[33][34][36]

This composite samples professional deliverable production and one banking-support environment. It excludes Artificial Analysis' separate Terminal-Bench v2.1, Global-MMLU-Lite, and MMMU Pro evaluations, so it is not a comprehensive test of terminal coding, multilingual performance, visual understanding, or every kind of planning and tool use.[32][33][34] GDPval itself can include reference files and gives vision-capable models a view-image tool, so that scope limit does not mean every Agentic Index task is text-only.[34] The GDPval paper also found that reasoning effort, task context, and scaffolding affected performance, while tau-Knowledge evaluates a single domain with its own retrieval and interaction design.[35][36] Artificial Analysis labels its tested Qwen3.8-27B configuration as `high`, but Qwen's local template accepts `xhigh`, `medium`, and `low`; the public model overview does not document how those labels were mapped.[25][31] The scores therefore describe Artificial Analysis' tested configuration, not every local quantization or effort setting, and they do not establish universal agent quality.

The August 19 figures were produced under Intelligence Index v4.1. On September 4, 2026, Artificial Analysis introduced an interim v4.2 that adds its private AA-Briefcase agentic knowledge-work evaluation and Surge AI's GDP.pdf document-reasoning benchmark, removes GPQA Diamond as saturated, re-anchors the GDPval-AA v2 Elo scale, and raises held-out test sets to 40 percent of the weighting.[57] As of September 6, 2026, the model page for "Qwen3.8 27B (xhigh)" showed a v4.2 Intelligence Index score of 41 across 10 evaluations, ranked first of the 140 models in the comparison class Artificial Analysis assigns to it, with a measured output speed of 46.6 tokens per second, listed pricing of $0.50 per million input and $3.00 per million output tokens, and 170 million output tokens used to run the index.[58] The 52.02 and 41 figures come from different index compositions and cannot be compared as a change in the model.

### Naming distinctions

Qwen3.8 is distinct from [Qwen3-Max](https://aiwiki.ai/wiki/qwen3_max) and Qwen3-8B. In `Qwen3-8B`, 8B denotes an approximately 8.2-billion-parameter checkpoint from the earlier Qwen3 family; it has downloadable files and an Apache 2.0 license.[11] The decimal in Qwen3.8 is a generation label. Qwen3-8B specifications do not carry over to Qwen3.8, and Qwen3.8-27B specifications do not carry over to the 2.4T-A95B checkpoint or hosted Max service.

## References

1. Qwen (@Alibaba_Qwen). Qwen3.8-Max-Preview launch post. X, July 19, 2026. https://x.com/Alibaba_Qwen/status/2078759124914098291
2. Alibaba Group. Alibaba Cloud Unveils Agent-Native Innovations at WAIC 2026. July 20, 2026. https://www.alibabagroup.com/en-US/document-2016703577908576256
3. QwenCloud. Text generation models. Accessed July 25, 2026. https://docs.qwencloud.com/developer-guides/getting-started/text-generation-models
4. QwenCloud. Token Plan Individual. Accessed July 25, 2026. https://docs.qwencloud.com/token-plan/personal/token-plan-personal-overview
5. Alibaba Cloud Help Center. Qwen3.8-Max-Preview limited-time offer. July 19, 2026. https://help.aliyun.com/zh/lingma/qwen3-8-max-preview-limited-time-offer
6. QwenCloud. OpenAI chat API reference. Accessed July 25, 2026. https://docs.qwencloud.com/api-reference/chat/openai-chat
7. Qwen Code Docs. Building a GPT-Style Cover Generation Skill with Qwen 3.8-max. July 21, 2026. https://qwenlm.github.io/qwen-code-docs/en/blog/cases/qwencode-bailian-skill-openai-cover-gen/
8. ITHome. 阿里千问 Qwen3.8-Max-Preview 最新版本已上线，前端表现获提升. July 21, 2026. https://www.ithome.com/0/979/756.htm
9. SiliconANGLE. Alibaba previews Qwen3.8, claims it is second only to Claude Fable 5. July 19, 2026. https://siliconangle.com/2026/07/19/alibaba-previews-qwen3-8-claims-second-claude-fable-5/
10. Qwen. GitHub organization. Accessed July 25, 2026. https://github.com/QwenLM
11. Qwen. Qwen3-8B model card. Hugging Face. Accessed July 25, 2026. https://huggingface.co/Qwen/Qwen3-8B
12. Qwen Team. "Qwen3.8-Max: A New Bar for Coding and Cowork." August 3, 2026. https://qwen.ai/blog?id=qwen3.8
13. Qwen (@Alibaba_Qwen). Qwen3.8-Max release thread. X, August 3, 2026. https://x.com/Alibaba_Qwen/status/2084100707423289643
14. Qwen. Qwen3.8-27B model card. Hugging Face. Accessed August 14, 2026. https://huggingface.co/Qwen/Qwen3.8-27B
15. Hugging Face. Qwen/Qwen3.8-27B repository metadata. Accessed August 14, 2026. https://huggingface.co/api/models/Qwen/Qwen3.8-27B
16. Qwen. Qwen3.8-27B-FP8 model card and repository. Hugging Face. Accessed August 14, 2026. https://huggingface.co/Qwen/Qwen3.8-27B-FP8
17. Qwen. Qwen3.8-27B model repository. ModelScope. Accessed August 14, 2026. https://modelscope.cn/models/Qwen/Qwen3.8-27B/summary
18. Unsloth. Qwen3.8-27B-GGUF model card and files. Hugging Face. Accessed August 14, 2026. https://huggingface.co/unsloth/Qwen3.8-27B-GGUF
19. Unsloth. Qwen3.8: How to Run Locally. Accessed August 19, 2026. https://unsloth.ai/docs/models/qwen3.8
20. Qwen. Qwen3.8-2.4T-A95B model card. Hugging Face. Accessed August 14, 2026. https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B
21. Hugging Face. Qwen/Qwen3.8-2.4T-A95B repository metadata. Accessed August 14, 2026. https://huggingface.co/api/models/Qwen/Qwen3.8-2.4T-A95B
22. Qwen. Qwen3.8-27B commit history. Hugging Face. Accessed August 15, 2026. https://huggingface.co/Qwen/Qwen3.8-27B/commits/main
23. Qwen (@Alibaba_Qwen). Qwen3.8-27B open-weight release post. X, August 14, 2026. https://x.com/Alibaba_Qwen/status/2088280182356611304
24. Qwen Developers (@QwenDevs). Qwen3.8-27B Practical Guide post and embedded article. X, August 14, 2026. https://x.com/QwenDevs/status/2088289608031510811
25. Qwen. Qwen3.8-27B chat template, revision 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0. Hugging Face, August 14, 2026. https://huggingface.co/Qwen/Qwen3.8-27B/blob/1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0/chat_template.jinja
26. vLLM Project. Qwen3.8-27B recipe. Added August 14, 2026. https://recipes.vllm.ai/Qwen/Qwen3.8-27B
27. SGLang. Qwen3.8-27B cookbook. Accessed August 15, 2026. https://docs.sglang.io/cookbook/autoregressive/Qwen/Qwen3.8-27B
28. LightSeek. TokenSpeed model recipes, Qwen3.8-27B section. Updated August 14, 2026. https://lightseek.org/tokenspeed/recipes/models#qwen3-8
29. Don-Chad. NInfer-3090 Qwen3.8-27B runtime and RTX 3090 results, tag v0.6.0-rtx3090, commit 2ae51915225d393e299a9d01b099e2c7103cd322. August 15, 2026. https://github.com/Don-Chad/ninfer-3090/tree/2ae51915225d393e299a9d01b099e2c7103cd322
30. r/LocalLLM user report. Qwen3.8-27B-FP8 on DGX Spark with vLLM. August 14, 2026. https://www.reddit.com/r/LocalLLM/comments/1voi148/qwen3827bfp8_on_dgx_spark_8_toks_single_stream_62/
31. Artificial Analysis. Qwen3.8 27B model analysis. Accessed August 19, 2026. https://artificialanalysis.ai/models/qwen3-8-27b
32. Artificial Analysis. Agentic Index leaderboard. Accessed August 19, 2026. https://artificialanalysis.ai/models/capabilities/agentic
33. Artificial Analysis. Capability Indices Methodology. Accessed August 19, 2026. https://artificialanalysis.ai/methodology/capability-indices
34. Artificial Analysis. Intelligence Benchmarking Methodology. Accessed August 19, 2026. https://artificialanalysis.ai/methodology/intelligence-benchmarking
35. Patwardhan, Tejal, et al. "GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks." arXiv:2510.04374, October 5, 2025. https://arxiv.org/abs/2510.04374
36. Shi, Quan, et al. "tau-Knowledge: Evaluating Conversational Agents over Unstructured Knowledge." arXiv:2603.04370, March 4, 2026. https://arxiv.org/abs/2603.04370

37. Unsloth. "Unsloth Dynamic 3.0 GGUFs." Accessed August 20, 2026. https://unsloth.ai/docs/basics/dynamic-3.0-ggufs
38. Hugging Face API. "unsloth/Qwen3.8-27B-GGUF" repository metadata at revision 4ca720788d1e01f1bff70c033e0d0028fd02e502. August 20, 2026. https://huggingface.co/api/models/unsloth/Qwen3.8-27B-GGUF/revision/4ca720788d1e01f1bff70c033e0d0028fd02e502?blobs=true
39. Unsloth. "Qwen3.8-27B" product page. Accessed August 20, 2026. https://unsloth.ai/models/qwen3.8-27b
40. Dutta, Abhinav, et al. "Accuracy is Not All You Need." Advances in Neural Information Processing Systems 37, 2024. https://proceedings.neurips.cc/paper_files/paper/2024/hash/e0e956681b04ac126679e8c7dd706b2e-Abstract-Conference.html
41. Jin, Renren, et al. "A Comprehensive Evaluation of Quantization Strategies for Large Language Models." Findings of ACL 2024, pages 12186-12215. https://aclanthology.org/2024.findings-acl.726/
42. Unsloth AI (@UnslothAI). Qwen3.8-27B Dynamic V3 launch post. X, August 19, 2026. https://x.com/UnslothAI/status/2090103470015828184

43. Syed Muhammad Usman Pirzada and Slobodan Josic. "Run Qwen 3.8 27B on AMD Ryzen AI Max Agentic PCs and Radeon GPUs." AMD. August 14, 2026. https://www.amd.com/en/blogs/2026/run-qwen-3-8-27b-on-amd-ryzen-ai-max-and-radeon-graphics-cards-day-0.html
44. AMD Ryzen (@AMDRyzen). Two-instance Qwen3.8-27B demonstration on a 128 GB Ryzen AI Max+ system. X. August 27, 2026. https://x.com/AMDRyzen/status/2093054574282105232

45. NVIDIA. "NVIDIA and Local AI Community Fuel Open Source Models and Intelligent Agents." NVIDIA Blog, August 11, 2026 (updated August 25, 2026). https://blogs.nvidia.com/blog/local-ai-open-source-models-agents-nemotron/
46. NVIDIA Robotics (@NVIDIARobotics). "The wait is over. Qwen3.8-27B is here, and it's ready for the edge." X, August 14, 2026. https://x.com/NVIDIARobotics/status/2088320498522534240
47. NVIDIA Jetson AI Lab. "Qwen3.8 27B." Model page, accessed September 6, 2026. https://www.jetson-ai-lab.com/models/qwen3-8-27b/
48. Inferact. "Inferact/Qwen3.8-27B-NVFP4" model card and repository metadata. Hugging Face, created August 13, 2026, accessed September 6, 2026. https://huggingface.co/Inferact/Qwen3.8-27B-NVFP4
49. Inco AI. "incoai/Qwen3.8-27B-DFlash2" model card. Hugging Face, created August 18, 2026, accessed September 6, 2026. https://huggingface.co/incoai/Qwen3.8-27B-DFlash2
50. Inco AI. "DFlash 2: Keep Drafting Parallel." Inco AI blog, August 18, 2026. https://inco.ai/blog/dflash2/
51. Khalil BenKhaled, Aditya Sahu, and Chitoku Yato. "Frontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson." NVIDIA Technical Blog, September 4, 2026. https://developer.nvidia.com/blog/frontier-reasoning-reaches-the-edge-how-to-deploy-and-optimize-models-on-nvidia-jetson/
52. Qwen Team. "Qwen3.8-Flash-Next: A New Architecture, Towards Ultimate Cost-Efficiency." August 26, 2026. https://qwen.ai/blog?id=qwen3.8-flash-next
53. Hugging Face. Qwen/Qwen3.8-Flash-Next repository metadata (created August 24, 2026). Accessed September 6, 2026. https://huggingface.co/api/models/Qwen/Qwen3.8-Flash-Next
54. Qwen (@Alibaba_Qwen). Qwen3.8-Max-0902 release post. X, September 2, 2026. https://x.com/Alibaba_Qwen/status/2094968708288680276
55. NVIDIA Robotics (@NVIDIARobotics). "More capable open models are becoming practical for real-time edge AI on NVIDIA Jetson..." X post with chart, September 4, 2026. https://x.com/NVIDIARobotics/status/2095919230247936270
56. vLLM Project. "v0.28.0." GitHub release, August 26, 2026. https://github.com/vllm-project/vllm/releases/tag/v0.28.0
57. Artificial Analysis. "Announcing Artificial Analysis Intelligence Index v4.2." September 4, 2026. https://artificialanalysis.ai/articles/artificial-analysis-intelligence-index-v4-2
58. Artificial Analysis. "Qwen3.8 27B (xhigh) - Intelligence, Performance & Price Analysis." Model page, accessed September 6, 2026. https://artificialanalysis.ai/models/qwen3-8-27b
59. Inco AI. "Splash: A Local Engine Built Around the Model." Inco AI blog, dated September 17, 2026. https://inco.ai/blog/splash/
60. Inco AI. incoai/splash repository, README and release 1.0 (September 18, 2026). GitHub. https://github.com/incoai/splash
61. Inco AI. "Qwen3.8-27B-Splash" model card. Hugging Face, accessed September 23, 2026. https://huggingface.co/incoai/Qwen3.8-27B-Splash
62. Inco AI (@inco_ai). Splash launch post. X, September 19, 2026 (00:07 UTC), quoted by LM Studio (@lmstudio), September 19, 2026 (00:45 UTC). https://x.com/inco_ai/status/2101100749623341513 https://x.com/lmstudio/status/2101110346954207521
63. Zhijian Liu (@zhijianliu_). Post on X, September 19, 2026 (00:15 UTC). https://x.com/zhijianliu_/status/2101102949158302001
64. LM Studio Team. "Splash Engine - the fastest local Qwen3.8 on Apple Silicon." LM Studio Blog, September 18, 2026. https://lmstudio.ai/blog/splash-engine