vllm/docs/source/quantization/fp8_e5m2_kvcache.rst

.. _fp8_kv_cache:

FP8 E5M2 KV Cache
==================

The int8/int4 quantization scheme requires additional scale GPU memory storage, which reduces the expected GPU memory benefits.
The FP8 data format retains 2~3 mantissa bits and can convert float/fp16/bflaot16 and fp8 to each other.

Here is an example of how to enable this feature:

.. code-block:: python

    from vllm import LLM, SamplingParams
    # Sample prompts.
    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]
    # Create a sampling params object.
    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
    # Create an LLM.
    llm = LLM(model="facebook/opt-125m", kv_cache_dtype="fp8")
    # Generate texts from the prompts. The output is a list of RequestOutput objects
    # that contain the prompt, generated text, and other information.
    outputs = llm.generate(prompts, sampling_params)
    # Print the outputs.
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")


Note, current prefix caching doesn't work with FP8 KV cache enabled, forward_prefix kernel should handle different KV and cache type.