chitu 0.1.2

A high-performance inference framework for large language models, focusing on efficiency, flexibility, and availability.

Chitu

English | 中文

Chitu is a high-performance inference framework for large language models, focusing on efficiency, flexibility, and availability.

News

[2025/03/28] Provide FP8 to FP16 operators to support more GPUs.

[2025/03/21] Better support for QwQ-32B. QwQ-32B FP8 model will be available on Huggingface.

[2025/03/14] Initial release of Chitu, supports DeepSeek-R1 671B, and provides efficient operators with online FP8 to BF16 conversion.

Introduction

Chitu is a high-performance inference framework for large language models. Chitu supports various mainstream large language models, including DeepSeek, LLaMA series, Mixtral, and more. We focus on the following goals:

• Efficiency: We continue to develop and integrate latest optimizations for large language models, including GPU kernels, parallel strategies, quantizations and more.
• Flexibility: We not only focus on the polular NVIDIA GPUs, but pay special attention to all kinds of hardware environments, including legacy GPUs, non-NVIDIA GPUs and CPUs. We aim to provide a versatile framework to encounter the diverse deploying requirements.
• Availability: Chitu is ready and already deployed for real-world production.

Evaluation

Here we list Chitu's key results only. More comprehensive comparison and discussion will be given in our tech report.

Deploy DeepSeek-R1-671B on A800(40GB) cluster

Configuration	6 nodes	3 nodes
Framework+precision	chitu 0.1.0, BF16	Chitu 0.1.0, FP8
Use cuda graph	29.8 output token/s	22.7 output token/s
Do not use cuda graph	8.5 output token/s	7.0 output token/s

• Data in the table are all output throughput of single request (bs=1)
• For Chitu For example, the output speed of the FP8 model running with 3 nodes is comparable to the speed of the BF16 model running with 6 nodes
• Whether to use cuda graph has a significant impact on performance. The performance of the Chitu has been significantly improved after using cuda graph

Comparison of BF16 and FP8 models running with Chitu

Batchsize	6 nodes, BF16	3 nodes, FP8
1	29.8 token/s	22.7 token/s
4	78.8 token/s	70.1 token/s
8	129.8 token/s	108.9 token/s
16	181.4 token/s	159.0 token/s
32	244.1 token/s	214.5 token/s

• From the test data of different batch sizes, based on the Chitu engine, the output speed of the FP8 model running on 3 nodes is about 75%~90% of that of the BF16 model running on 6 nodes, that is, the output per unit computing power has been improved by 1.5x~1.8x
• We believe that this is because the decoding process mainly depends on memory bandwidth. Using half of the GPU to access half of the data (the weight size of FP8 is half of that of BF16) will not take longer, and the reduction in GPU computing power will only have a small impact

Deploy DeepSeek-R1-671B on the H20 (96G) cluster

Running on 2 nodes each with 8*H20

Output token/s	chitu 0.1.0, FP8
bs=1	22.1
bs=16	202.1
bs=256	780.3

Getting started

You can install Chitu from source.

Install from Source

git clone --recursive https://github.com/thu-pacman/chitu && cd chitu

pip install -r requirements-build.txt
pip install -U torch --index-url https://download.pytorch.org/whl/cu124  # Change according to your CUDA version
TORCH_CUDA_ARCH_LIST=8.6 CHITU_SETUP_JOBS=4 MAX_JOBS=4 pip install --no-build-isolation .

Quick Start

Single GPU Inference

torchrun --nproc_per_node 8 test/single_req_test.py request.max_new_tokens=64 models=DeepSeek-R1 models.ckpt_dir=/data/DeepSeek-R1 infer.pp_size=1 infer.tp_size=8

Hybrid Parallelism (TP+PP)

torchrun --nnodes 2 --nproc_per_node 8 test/single_req_test.py request.max_new_tokens=64 infer.pp_size=2 infer.tp_size=8 models=DeepSeek-R1 models.ckpt_dir=/data/DeepSeek-R1

Start a Service

# Start service at localhost:21002
export WORLD_SIZE=8
torchrun --nnodes 1 \
    --nproc_per_node 8 \
    --master_port=22525 \
    -m chitu \
    serve.port=21002 \
    infer.cache_type=paged \
    infer.pp_size=1 \
    infer.tp_size=8 \
    models=DeepSeek-R1 \
    models.ckpt_dir=/data/DeepSeek-R1 \
    infer.attn_type=flash_infer \
    keep_dtype_in_checkpoint=True \
    infer.mla_absorb=absorb-without-precomp \
    infer.soft_fp8=True \
    infer.do_load=True \
    infer.max_reqs=1 \
    scheduler.prefill_first.num_tasks=100 \
    infer.max_seq_len=4096 \
    request.max_new_tokens=100 \
    infer.use_cuda_graph=True

# Test the service
curl localhost:21002/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "What is machine learning?"
      }
    ]
  }'

Benchmarking

# Comprehensive performance testing with benchmark_serving tool
python benchmarks/benchmark_serving.py \
    --model "deepseek-r1" \
    --iterations 10 \
    --seq-len 10 \
    --warmup 3 \
    --base-url http://localhost:21002

Full Documentation

Please refer to here for more details.

FAQ (Frequently Asked Questions)

English | 中文

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Discussion

For any questions or concerns, you're welcome to create an issue. We also have an active WeChat group available for more detailed discussions. QR Code:

License

The Chitu Project is under the Apache License v2.0. - see the LICENSE file for details.

This repository also contains third_party submodules under other open source licenses. You can find these submodules under third_party/ directory, which contains their own license files.

Acknowledgment

We learned a lot from the following projects and adapted some functions when building Chitu:

• vLLM
• SGLang
• DeepSeek

Special thanks to our partners (Partners listed in no particular order): 中国电信、华为、沐曦、燧原、 etc.