Llama, SGLang, vLLM, code assistant, inference frameworks, LiteLLM, Devstral-Small-2-24B, GPUs H100/L40S, llm-grill, open source evaluation ## Introduction In the rapidly evolving landscape of artificial intelligence, the selection of an appropriate inference framework for large language models (LLMs) is crucial, especially for developers looking to implement code assistants. With several frameworks available, including Llama.cpp, SGLang, and vLLM, each offering unique advantages and functionalities, understanding their architectures and performance is vital for making an informed choice. This article explores the capabilities of a self-hosted architecture using LiteLLM combined with vLLM, SGLang, and llama.cpp, evaluated on powerful GPUs H100 and L40S, specifically with the Devstral-Small-2-24B model. Additionally, we will consider performance metrics gathered from our open-source evaluation tool, llm-grill, for up to 200 simultaneous users. ## Understanding LLM Inference Frameworks ### What Are LLM Inference Frameworks? LLM inference frameworks serve as the backbone for deploying large-scale language models efficiently. They facilitate the processing of input data, execute model inference, and deliver responses. Each framework comes with its own set of optimizations, compatibility criteria, and performance benchmarks, which can significantly impact the efficiency and effectiveness of code assistants. ### Key Features of Llama.cpp, SGLang, and vLLM 1. **Llama.cpp**: Known for its flexibility and ease of integration, Llama.cpp enables developers to build customized LLM applications with minimal overhead. Its open-source nature encourages community contributions, which continuously enhance its capabilities. 2. **SGLang**: This framework emphasizes speed and efficiency, leveraging advanced techniques to reduce latency during model inference. SGLang is designed specifically for environments where response time is critical, making it an excellent choice for real-time applications like coding assistants. 3. **vLLM**: Offering robust scalability, vLLM is engineered to handle high throughput, making it suitable for scenarios involving multiple concurrent users. Its architecture simplifies the management of resources, allowing for better performance under load. ## The Power of a Self-Hosted Architecture ### LiteLLM and its Integration with Other Frameworks LiteLLM serves as a lightweight foundation that can be paired with frameworks like Llama.cpp, SGLang, and vLLM to create a powerful self-hosted infra for LLMs. By leveraging LiteLLM, developers can deploy models on local GPUs, significantly reducing latency and enhancing privacy. The combination of LiteLLM with vLLM or SGLang can optimize both speed and resource management, paving the way for efficient code assistants. ### Performance Evaluation Using llm-grill To assess the capabilities of these frameworks, we utilized llm-grill, our open-source evaluation tool specifically designed for benchmarking LLMs. This tool allows us to simulate testing environments with up to 200 concurrent users, providing insights into response times, throughput, and overall system performance. The conducted tests demonstrated that the integration of LiteLLM with vLLM achieved remarkable scalability, effectively managing user requests without significant degradation in response times. Meanwhile, SGLang showcased its capacity for rapid inference, particularly under high-demand scenarios, making it a strong candidate for applications requiring immediate feedback. ## GPU Performance Analysis: H100 vs. L40S ### H100 GPUs The H100 GPUs, equipped with advanced tensor cores and high memory bandwidth, are ideal for handling large-scale LLMs. During our evaluation, the H100 GPUs exhibited exceptional performance, particularly in contexts that demanded intensive computation. They facilitated faster training times and improved inference speeds, making them well-suited for deploying code assistants that require real-time interactions. ### L40S GPUs In contrast, the L40S GPUs, while slightly less powerful than their H100 counterparts, still offered robust performance for deploying LLMs. Their efficiency in power consumption combined with solid processing capabilities made them an attractive option for developers looking to balance cost and performance. Our tests revealed that L40S GPUs could effectively serve smaller-scale applications without compromising user experience. ## Conclusion: Choosing the Right Framework for Your Code Assistant Selecting the right LLM inference framework for a code assistant hinges on several factors, including the specific use case, user demand, and available resources. Llama.cpp, SGLang, and vLLM each bring unique strengths to the table, and their integration with LiteLLM can create a tailored solution that meets varying needs. If rapid response times and real-time user interaction are paramount, SGLang paired with LiteLLM on H100 GPUs may be the ideal choice. Conversely, for applications requiring high scalability and stability under load, vLLM demonstrates exceptional performance. Ultimately, the decision should be informed by performance metrics, such as those obtained through llm-grill, and the specific requirements of your application. By carefully evaluating these frameworks and leveraging the right hardware, developers can create powerful, efficient code assistants that significantly enhance productivity and user experience in coding environments. Source: https://blog.octo.com/llama.cpp-sglang-vllm--quel-framework-d'inference-llm-choisir-pour-votre-assistant-de-code