Balance Serve backend (multi-concurrency) for ktransformers

KTransformers v0.2.4 Release Notes

We are excited to announce the official release of the long-awaited KTransformers v0.2.4! In this version, we’ve added highly desired multi-concurrency support to the community through a major refactor of the whole architecture, updating more than 10,000 lines of code. By drawing inspiration from the excellent architecture of sglang, we have implemented high-performance asynchronous concurrent scheduling in C++, including features like continuous batching, chunked prefill, and more. Thanks to GPU sharing in concurrent scenarios, overall throughput is also improved to a certain extent. The following is a demonstration:

https://github.com/user-attachments/assets/faa3bda2-928b-45a7-b44f-21e12ec84b8a

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🚀 Key Updates

Multi-Concurrency Support
- Added capability to handle multiple concurrent inference requests. Supports receiving and executing multiple tasks simultaneously.
- We implemented custom_flashinfer based on the high-performance and highly flexible operator library flashinfer, and achieved a variable batch size CUDA Graph, which further enhances flexibility while reducing memory and padding overhead.
- In our benchmarks, overall throughput improved by approximately 130% under 4-way concurrency.
- With support from Intel, we tested KTransformers v0.2.4 on the latest Xeon6 + MRDIMM-8800 platform. By increasing concurrency, the total output throughput increased from 17 tokens/s to 40 tokens/s. We observed that the bottleneck has now shifted to the GPU. Using a higher-end GPU than the 4090D could further improve performance.
Engine Architecture Optimization Inspired by the scheduling framework of sglang, we refactored KTransformers with a clearer three-layer architecture through an update of 11,000 lines of code, now supporting full multi-concurrency:
- Server：Handles user requests and serves the OpenAI-compatible API.
- Inference Engine：Executes model inference and supports chunked prefill.
- Scheduler：Manages task scheduling and requests orchestration. Supports continuous batching by organizing queued requests into batches in a FCFS manner and sending them to the inference engine.
Project Structure Reorganization All C/C++ code is now centralized under the /csrc directory.
Parameter Adjustments Removed some legacy and deprecated launch parameters for a cleaner configuration experience. We plan to provide a complete parameter list and detailed documentation in future releases to facilitate flexible configuration and debugging.

📚 Upgrade Notes

Due to parameter changes, users who have installed previous versions are advised to delete the ~/.ktransformers directory and reinitialize.
To enable multi-concurrency, please refer to the latest documentation for configuration examples.

What's Changed

Implemented custom_flashinfer @Atream @ovowei @qiyuxinlin Implemented balance_serve engine based on FlashInfer @qiyuxinlin @ovowei Implemented a continuous batching scheduler in C++ @ErvinXie release: bump version v0.2.4 by @Atream @Azure-Tang @ErvinXie @qiyuxinlin @ovowei @KMSorSMS @SkqLiao

Download the Docker image for testing v0.2.4

Visit the link to pull the image, using v0.2.4-AVX512 as an example.

bash

docker pull approachingai/ktransformers:v0.2.4-AVX512
docker run -it --gpus all --privileged --shm-size 64g --name ktrans --network=host -v /mnt:/mnt approachingai/ktransformers:v0.2.4-AVX512 /bin/bash
# Open a new terminal
docker exec -it ktrans bash

Installation Guide

⚠️ Please note that installing this project will replace flashinfer in your environment. It is strongly recommended to create a new conda environment!!!

2. Set Up Conda Environment

We recommend using Miniconda3/Anaconda3 for environment management:

bash

# Download Miniconda
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

# Create environment
conda create --name ktransformers python=3.11
conda activate ktransformers

# Install required libraries
conda install -c conda-forge libstdcxx-ng

# Verify GLIBCXX version (should include 3.4.32)
strings ~/anaconda3/envs/ktransformers/lib/libstdc++.so.6 | grep GLIBCXX

Note: Adjust the Anaconda path if your installation directory differs from ~/anaconda3

2. Install dependencies

bash

sudo apt install libtbb-dev libssl-dev libcurl4-openssl-dev libaio1 libaio-dev libfmt-dev libgflags-dev zlib1g-dev patchelf
pip3 install packaging ninja cpufeature numpy openai
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126

3. Build ktransformers

bash

# Clone repository
git clone https://github.com/kvcache-ai/ktransformers.git
cd ktransformers
git submodule update --init --recursive


# Install single NUMA dependencies
USE_BALANCE_SERVE=1  bash ./install.sh
# For those who have two cpu and 1T RAM（Dual NUMA）:
USE_BALANCE_SERVE=1 USE_NUMA=1 bash ./install.sh

Running DeepSeek-R1-Q4KM Models

1. Run for 24GB VRAM GPUs

Use our optimized configuration for constrained VRAM:

bash

python ktransformers/server/main.py \
  --port 10002 \
  --model_path <path_to_safetensor_config> \
  --gguf_path <path_to_gguf_files> \
  --optimize_config_path ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-serve.yaml \
  --max_new_tokens 1024 \
  --cache_lens 32768 \
  --chunk_size 256 \
  --max_batch_size 4 \
  --backend_type balance_serve \
  --force_think # useful for R1

It features the following arguments:

--max_new_tokens: Maximum number of tokens generated per request.
--cache_lens: Total length of kvcache allocated by the scheduler. All requests share a kvcache space.
--max_batch_size: Maximum number of requests (prefill + decode) processed in a single run by the engine. (Supported only by balance_serve)
--chunk_size: Maximum number of tokens processed in a single run by the engine. corresponding to 32768 tokens, and the space occupied will be released after the requests are completed.
--backend_type: balance_serve is a multi-concurrency backend engine introduced in version v0.2.4. The original single-concurrency engine is ktransformers.
--model_path: Path to safetensor config path (only config required, not model safetensors).
Please note that, since ver 0.2.4, the last segment of ${model_path} directory name MUST be a local directory that contains the model's configuration files. Hugging Face links (e.g., deepseek-ai/DeepSeek-R1) are not supported at the moment.
--force_think: Force responding the reasoning tag of DeepSeek R1.

The relationship between max_batch_size, cache_lens, and max_new_tokens should satisfy: cache_lens > max_batch_size * max_new_tokens, otherwise the concurrency will decrease.

2. access server

curl -X POST http://localhost:10002/v1/chat/completions \
  -H "accept: application/json" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "hello"}
    ],
    "model": "DeepSeek-R1",
    "temperature": 0.3,
    "top_p": 1.0,
    "stream": true
  }'