Uniform Attention Models

Recipes for standard (uniform) attention transformer architectures validated end-to-end with LMCache, with a recipe page per architecture covering only the LMCache-specific configuration that diverges from defaults.

These models use a single attention type across all layers, so vLLM serves them with one KV cache group. Models that interleave multiple attention types (sliding-window + full, or Mamba / linear-attention + full) are covered under Hybrid Attention Models.

Recipe page contents

Each recipe page is intentionally minimal:

• Validated models – exact HF repo IDs that have been tested.

• Engine tabs – one tab per serving engine (vLLM, SGLang, TRT-LLM). Each tab links to the engine’s own documentation for the model and shows the exact lmcache server and engine launch commands. Tabs for engines that are not yet validated state so explicitly.

• CacheBlend support – validation status (may be empty).

• Compression support – table of compression methods (CacheGen, etc.) with per-method validation status. Extensible: new methods get a row.

• Caveats – known limitations, if any.

For the generic LMCache + engine wiring (ports, remote hosts, sending a first request), see Quickstart. Recipes assume that page as a prerequisite.

Supported architectures

Model	Example HF model	vLLM	SGLang	TRT-LLM	Recipe
MiniMax M2 series	MiniMaxAI/MiniMax-M2	✓	—	—	MiniMax M2 series
Mistral / Devstral	mistralai/Devstral-2-123B-Instruct-2512	✓	—	—	Mistral / Devstral
Qwen3 MoE	Qwen/Qwen3-235B-A22B	✓	—	—	Qwen3 MoE
Llama	meta-llama/Meta-Llama-3.1-70B-Instruct	✓	—	—	Llama
Phi-3 / Phi-4	microsoft/Phi-4-mini-instruct	✓	—	—	Phi-3 / Phi-4
Mixtral	mistralai/Mixtral-8x7B-Instruct-v0.1	✓	—	—	Mixtral

Legend: ✓ validated, — not validated. The Model column is the model family; each recipe page lists the exact vLLM architecture class it covers.

Contributing a recipe

To add a new uniform-attention architecture:

1. Copy an existing page (e.g. minimax_m2.rst) to recipes/<architecture_snake_case>.rst.

2. Fill in Validated models, Engines, LMCache configuration, and Caveats. Keep each section terse – if a field has nothing to say, say so in one line rather than padding it.

3. Add a row to the table above and an entry to the hidden toctree below.

(For models that interleave attention types, add the page under Hybrid Attention Models instead.)