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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Jev (AI model)
Jev is an AI model released in early access on 15 September 2026 by TypeSafe AI, a San Francisco startup founded in 2024 by former OpenAI researcher Diogo Almeida with Erik Gafni and Sasha Sheng.
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.
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.
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 a subset of its own parameters at inference time