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vLLM TT Plugin adds Tenstorrent accelerator support with mesh-compiled execution
The vLLM TT Plugin integrates Tenstorrent accelerators into vLLM via the out-of-tree platform plugin mechanism, automatically discovering hardware when TT-Metal is importable and preserving the existing OpenAI-compatible API surface.
This gives engineers a non-GPU path for LLM serving where parallelism is compiled into a single mesh program rather than configured as runtime ranks. The plugin demonstrates that vLLM's plugin interfaces are general enough to express hardware architectures that differ fundamentally from GPUs without modifying vLLM core.
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The plugin registers Tenstorrent hardware automatically when TT-Metal is importable, requiring no changes to client code or the OpenAI-compatible API.
Tenstorrent's mesh architecture compiles parallelism into a single traced program for the whole mesh, eliminating tensor-parallel and pipeline-parallel rank configuration.
Supported models include Llama 3.1/3.2/3.3, Qwen 2.5/3 variants, Mistral, Gemma 3/4, DeepSeek V3, and GPT-OSS, with multimodal coverage for several families.
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