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[Docs] Add supported quantization methods to docs (#2135)

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Woosuk Kwon
2023-12-15 13:29:22 -08:00
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parent 0fbfc4b81b
commit b81a6a6bb3
2 changed files with 4 additions and 2 deletions
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docs/source/index.rst
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@ -30,6 +30,7 @@ vLLM is fast with:
* State-of-the-art serving throughput
* Efficient management of attention key and value memory with **PagedAttention**
* Continuous batching of incoming requests
* Quantization: `GPTQ <https://arxiv.org/abs/2210.17323>`_, `AWQ <https://arxiv.org/abs/2306.00978>`_, `SqueezeLLM <https://arxiv.org/abs/2306.07629>`_
* Optimized CUDA kernels
vLLM is flexible and easy to use with:
@ -39,7 +40,7 @@ vLLM is flexible and easy to use with:
* Tensor parallelism support for distributed inference
* Streaming outputs
* OpenAI-compatible API server
* Support NVIDIA CUDA and AMD ROCm.
* Support NVIDIA GPUs and AMD GPUs.
For more information, check out the following: