Exactly. vLLM doesn’t optimize for latency-first scenarios as it focuses on throughput, i.e. batching. This particular blog post instead focuses particular on latency, i.e. the fastest you could possible get with those many GPUsz
Regarding batching, it is coming pretty soon, and we will have another blog post on this.
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[ 0.29 ms ] story [ 37.9 ms ] threadFor Llama2-70B, it runs 4-bit quantized Llama2-70B at:
- 34.5 tok/sec on two NVIDIA RTX 4090 at $3k
- 29.9 tok/sec on two AMD Radeon 7900XTX at $2k
- Also it is scales well with 8 A10G/A100 GPUs in our experiment.
Details:
- Blog post: https://blog.mlc.ai/2023/10/19/Scalable-Language-Model-Infer...
- Project: https://github.com/mlc-ai/mlc-llm
exLlama is blazing fast. Even if they just benched exllamav1, exllamav2 is only a bit faster, at least on my single 3090 in a similar environment.
vLLM is focused more on batching performance, but even then MLC/TVM looks like its putting up a fight without batching.
I am a bit fatigued with llama backends myself, and it looks like this won't help me run 70B in a single 3090, but I need to dig into mlc again.
Regarding exllama-V2, MLC/TVM does benchmark against it:
- Single GPU: https://github.com/mlc-ai/llm-perf-bench#int4-quantized-sing...
- Multi GPU: Figure 2 in the blog: http://blog.mlc.ai/2023/10/19/Scalable-Language-Model-Infere...
> vLLM focuses more on batching performance
Exactly. vLLM doesn’t optimize for latency-first scenarios as it focuses on throughput, i.e. batching. This particular blog post instead focuses particular on latency, i.e. the fastest you could possible get with those many GPUsz
Regarding batching, it is coming pretty soon, and we will have another blog post on this.
Also saw a report earlier today on MLC’s discord about AMD MI-100:
GPU Count | Model Size | Prefill Speed | Decode Speed
1 | 33b | 102.2 | 22.3
2 | 33b | 112.3 | 33.0
4 | 33b | 144.8 | 41.2