Attention-FFN disaggregation
Attention-FFN disaggregation (AFD), also written attention-feedforward disaggregation or A/F disaggregation, is a large language model serving architecture that runs the attention sublayers and the…
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Showing 1-15 of 15 articles
Jev (AI model)
Jev is an AI model released in waitlisted early access on 15 September 2026, and opened to all developers on 20 September, by TypeSafe AI, a San Francisco startup founded in 2024 by former OpenAI researcher…
KV Cache
KV cache, short for key-value cache, is transient model state used during Transformer generation.
Knowledge Distillation
Knowledge distillation is a training method in which a student model learns from signals produced by a teacher model.
MXFP4
MXFP4 (Microscaling FP4) is a 4-bit block-scaled number format for quantization in machine learning, defined in the Open Compute Project (OCP) Microscaling Formats (MX) Specification v1.0 of September 2023.
Mixture of Experts (MoE)
A mixture of experts (MoE) is a machine-learning architecture that contains multiple component functions called experts and a gating or routing mechanism that determines how their outputs contribute to a…
Multi-token prediction
Multi-token prediction (often abbreviated MTP) is a language modeling training objective in which the model is trained to predict several future tokens at each context position rather than only the next token.
NOSA (Native and Offloadable Sparse Attention)
NOSA (Native and Offloadable Sparse Attention) is a trainable sparse attention mechanism designed so that most of a language model's KV cache can live in CPU memory during decoding without the CPU-to-GPU…
On-device AI
On-device AI is the practice of running machine learning models on the phone, laptop, watch, or embedded board a person is actually using, instead of sending the input to a remote data center.
Prompt Caching
Prompt caching is a large language model (LLM) inference optimization that stores the computed key-value (KV) state of a repeated prompt prefix so it can be reused across API calls, cutting both cost and…
Pruning
Pruning is a family of techniques used in machine learning and artificial intelligence to remove parts of a model or search space that are estimated to be unnecessary for accuracy or optimality.
Quantization
Quantization is the representation of values from a large or continuous set by values from a smaller, usually finite set.
Speculative Decoding
Speculative decoding is a lossless inference acceleration technique for autoregressive transformer models in which a small, fast draft model proposes several future tokens at once and the larger target model…
Test-Time Training (TTT)
Test-Time Training (TTT) is a family of machine learning techniques in which a model updates some of its own parameters at inference time
Zhijian Liu
Zhijian Liu is a computer scientist who works on efficient machine learning. He is an assistant professor at the University of California, San Diego, where he directs Z Lab, and a co-founder of Inco AI