Understanding Inference Runtimes for LLM Serving
Large language models (LLMs) are becoming essential in various applications, but their efficiency in serving tokens under real traffic conditions is critical. This article explores the top inference runtimes for LLM serving, highlighting their designs, performance metrics, and ideal use cases.
Overview of Inference Runtimes
We will compare six popular inference runtimes that are frequently used in production environments:
- vLLM
- TensorRT LLM
- Hugging Face Text Generation Inference (TGI v3)
- LMDeploy
- SGLang
- DeepSpeed Inference / ZeRO Inference
1. vLLM
Design
vLLM employs PagedAttention, which breaks the KV cache into fixed-size blocks. This design minimizes KV fragmentation and maximizes GPU utilization through continuous batching.
Performance
vLLM boasts a throughput that is 14–24 times higher than Hugging Face Transformers, making it a robust choice for general LLM serving.
Where it Fits
This engine is ideal for organizations seeking a high-performance solution that offers flexibility across hardware.
2. TensorRT LLM
Design
TensorRT LLM is built on a compilation-based architecture that generates optimized kernels for specific models. This includes features like a paged KV cache and quantized options.
Performance
It excels in low latency situations, especially when tuned for specific models, making it suitable for latency-sensitive applications.
Where it Fits
This runtime is perfect for environments that rely heavily on NVIDIA hardware and need precise control over latency.
3. Hugging Face TGI v3
Design
TGI v3 introduces a Rust-based server that supports continuous batching and is optimized for long context inputs through its chunked prefill technique.
Performance
This engine processes approximately three times more tokens and is up to 13 times faster than vLLM for long prompts, making it a standout choice for chat applications.
Where it Fits
Organizations using Hugging Face frameworks will find TGI v3 particularly useful for managing long conversational histories.
4. LMDeploy
Design
LMDeploy is part of the InternLM ecosystem and utilizes high-performance CUDA kernels to enhance throughput.
Performance
It can achieve up to 1.8 times higher request throughput than vLLM, especially under high concurrency scenarios.
Where it Fits
This toolkit is best suited for NVIDIA-centric environments focused on maximizing throughput.
5. SGLang
Design
SGLang features a domain-specific language for structured LLM programs, implementing RadixAttention for efficient KV reuse.
Performance
It achieves up to 6.4 times higher throughput and significantly lower latency for structured workloads, making it valuable for specific use cases.
Where it Fits
This runtime is ideal for applications where KV reuse is crucial, such as agentic systems.
6. DeepSpeed Inference / ZeRO Inference
Design
DeepSpeed employs optimized transformer kernels and offloading techniques to enable large models to run on limited GPU memory.
Performance
In targeted configurations, it can achieve impressive throughput, particularly when fully offloading to CPU.
Where it Fits
This runtime is best for scenarios where the model size is more critical than latency, such as offline or batch inference.
Choosing the Right Runtime
Selecting the appropriate runtime for your production system involves assessing your specific needs:
- For a default engine with good performance: Start with vLLM.
- If latency is a priority: Choose TensorRT LLM.
- For long chat applications: Opt for TGI v3.
- For maximum throughput with quantized models: Use LMDeploy.
- For agentic systems: Select SGLang.
- If handling large models on limited GPUs: Consider DeepSpeed Inference.
Ultimately, effective KV cache management is essential in LLM serving. The best runtimes optimize KV usage through various strategies, ensuring high performance and efficiency.
FAQ
1. What is an inference runtime?
An inference runtime is a software environment that executes machine learning models, optimizing their performance for specific hardware and usage scenarios.
2. Why is KV cache management important?
KV cache management is crucial because it directly impacts the efficiency of model serving, affecting latency and throughput.
3. How do I choose the right inference runtime for my application?
Consider factors like hardware compatibility, performance needs, and specific use cases when selecting an inference runtime.
4. Are these runtimes compatible with all types of LLMs?
Most runtimes are optimized for specific models or frameworks, so it’s essential to verify compatibility based on your LLM choice.
5. Can I switch runtimes later if my needs change?
Yes, while it may require some adjustments, many applications can switch runtimes as their performance needs evolve.
6. What are common mistakes when implementing LLM serving?
Common mistakes include underestimating latency requirements, neglecting KV cache management, and choosing a runtime without thorough testing.
In conclusion, understanding the intricacies of inference runtimes is vital for optimizing LLM performance in real-world applications. By carefully evaluating each option and aligning it with your specific needs, you can significantly enhance the efficiency and effectiveness of your AI systems.
