ccInfer: A High-Performance LLM Inference Service Engine Based on C++23

ccInfer is a high-performance LLM inference framework developed using the modern C++23 standard, specifically designed for high-throughput inference services in production environments. It supports advanced technologies like PagedAttention, GQA, and BF16 quantization, making full use of C++'s memory control capabilities and modern features to strive for the ultimate performance and resource efficiency, providing an underlying optimization option to address the bottlenecks of Python solutions.

With the widespread deployment of LLMs in production environments, the bottlenecks of pure Python solutions in terms of performance and resource usage have become increasingly prominent. More and more developers are seeking underlying optimization solutions, and ccInfer is the result of technical exploration in this context, reflecting the demand trend for high-performance and low-resource-consumption solutions in the LLM inference field.

ccInfer integrates several cutting-edge technologies:

1. PagedAttention (reduces memory fragmentation and improves concurrency) and online Softmax (optimizes attention calculation);
2. Natively supports GQA (compatible with models like Llama2/3 and Qwen, reducing KV cache usage);
3. BF16+FP32 mixed-precision computation (uses Tensor Core acceleration while ensuring numerical stability);
4. Built-in GPT-2 BPE tokenizer;
5. SSE streaming response (pushes tokens in real time, enhancing interactive experience).

Build Environment Requirements: CUDA 11.8+, GCC 13+, CMake 3.20+, Boost 1.83+ and dependent libraries (nlohmann-json, fmt, spdlog). Compilation and Execution: Build via CMake (example: specify CUDA architecture 89). Models can be downloaded via HuggingFace CLI or Git LFS. Service Mode: After starting the service, it provides an HTTP interface compatible with the OpenAI API, supporting health checks and conversation completion. Graceful Shutdown: A two-stage mechanism to ensure no requests are lost.

ccInfer's performance optimizations include:

1. Memory management (uses C++23 smart pointers, move semantics, and memory pools to reduce allocation overhead);
2. CUDA kernel optimization (merged access, shared memory utilization, kernel fusion, Warp-level primitive acceleration);
3. The architecture reserves extension points for continuous batching to prepare for future throughput improvements.

Applicable Scenarios: High-throughput production environments, resource-constrained deployments, latency-sensitive applications, and teams using C++ tech stacks. Comparison with Mainstream Solutions:

Feature	ccInfer	vLLM	TensorRT-LLM	llama.cpp
Development Language	C++23	Python/C++	C++	C/C++
PagedAttention	Supported	Native	Supported	Partially supported
Quantization Support	BF16	Multiple	Multiple	Multiple
Usability	Medium	High	Medium	High
Hardware Support	CUDA	Multi-backend	NVIDIA	Multi-backend
ccInfer is positioned between the usability of vLLM and the ultimate performance of TensorRT-LLM.

ccInfer is open-sourced and hosted on GitHub, and the community can participate via Issues/PRs. Future directions include: multi-backend support (ROCm, Intel oneAPI), INT8/INT4 quantization, speculative decoding, distributed inference (tensor/pipeline parallelism), etc., to drive improvements in LLM inference efficiency and performance.