Vllm vs tgi It offers a simple API and compatibility with various models from the Hugging Face hub. Benchmarks by BentoML show that this engine reaches 600-650 tokens per second at 100 concurrent users for Llama 3 70B Q4 on an A100 80GB GPU. Mar 25, 2024 · NOTE: Because of constraints, we could only benchmark TGI on H100s and vLLM on A100. TGI 4xH100. Let’s break down the unique offerings, key features, and examples for each tool. But as you will see, vLLM still competes with TGI when running on less powerful hardware, thus reducing cost. vLLM delivers up to 24x higher throughput than Hugging Face Transformers, without requiring Dec 1, 2023 · The choice between TGI and vLLM depends on the specific requirements of the project, including factors like performance needs, resource availability, and the desired level of customization. In summary, while both vLLM and TGI have their strengths, vLLM's focus on throughput, latency reduction, and efficient resource utilization positions it as a strong contender in the To help developers make informed decisions, the BentoML engineering team conducted a comprehensive benchmark study on the Llama 3 serving performance with vLLM, LMDeploy, MLC-LLM, TensorRT-LLM, and Hugging Face TGI on BentoCloud. 😐 Text Generation Inference is an ok option (but nowhere near as fast as vLLM) if you want to deploy HuggingFace LLMs in a standard way. Sep 24, 2023 · Both Text Generation Interface (TGI) and vLLM offer valuable solutions for deploying and serving Large Language Models. Performance is under-optimized. The choice between the two depends on your specific requirements and priorities. vLLM 4xA100. , Key Features of TGI. Use Case Recommendations - Choose vLLM for cloud-based, high-throughput needs (e. TensorRT-LLM: Supports quantization via modelopt, and note that quantized data types are not implemented for all the models. - Opt for OLLama when privacy/local development is paramount. vLLM offers LLM inferencing and serving with SOTA throughput, Paged Attention, Continuous batching, Quantization (GPTQ, AWQ, FP8), and Jan 31, 2025 · 6. Tensor Parallelism; Mar 17, 2025 · By focusing on user interest and impact, vLLM ensures that resources are allocated effectively, enhancing overall performance compared to TGI. - Select TGI for seamless Dec 16, 2023 · vLLM — Maximum speed is required for batched prompt delivery. Developed by researchers at UC Berkeley, it utilizes PagedAttention, a new attention algorithm that effectively manages attention keys and values. Text Generation Inference (TGI)Overview: Developed by Hugging Face, TGI (Text Generation Inference) is a specialized inference tool for serving large language models (LLMs Apr 8, 2025 · Hugging Face TGI performs similarly to vLLM, providing a balance of performance and ease of use. vLLM is an open-source library designed for fast LLM inference and serving. vLLM Introduction. Nov 15, 2024 · As AI applications become more selecting the right tool for model inference, scalability, and performance is increasingly important. TGI: Supports AWQ, GPTQ and bits-and-bytes quantization Apr 10, 2025 · **TGI框架** HuggingFace官方优化方案，支持连续批处理 [^1]: Ollama和vLLM各有千秋，选择哪种方案取决于具体需求 : GGUF格式专为优化大模型的本地加载和推理效率设计 [^3]: Llama 3. vLLM: Easy, fast, and cheap LLM serving for everyone. TGI has some nice features like telemetry baked in (via OpenTelemetry) and integration with the HF ecosystem like inference endpoints. vLLM Apr 17, 2024 · Hugging Face TGI: A Rust, Python and gRPC server for text generation inference. vLLM and TGI are popular choices for serving large language models (LLMs) due to their efficiency and performance. We are redoing the TGI benchmarks on A100 and have an update soon! Goliath 120 req/min. It seems to suggest that all three are similar, with TGI marginally faster at lower queries per second, and vLLM fastest at higher query rates (which seems server related). Ex Falcon, LLAMa, T5,etc. Comparison Analysis Performance Metrics. g. , enterprise APIs). Jun 17, 2024 · vLLM: Not fully supported as of now. TGI 8xH100. Conclusion. Latency is decent: 50-70ms on a good GPU. LLM 高并发部署是个难题，具备高吞吐量的服务，能够让用户有更好的体验（比如模型生成文字速度提升，用户排队时间缩短）。本文对 vllm 和 TGI 两个开源方案进行了实践测试，并整理了一些部署的坑。 测试环境：单卡… Jan 17, 2025 · vLLM might be the sweet spot for serving very large models. vLLM TGI from huggingface TensorRT from Nvidia The screenshot below is from a Run AI Labs report (testing was with Llama 2 7B). 2-Vision在视觉识别方面表现出色. Users need to quantize the model through AutoAWQ or find pre-quantized models on Hugging Face. TGI suitable for deploy NLP based LLMS. Jul 27, 2023 · LLM 高并发部署是个难题，具备高吞吐量的服务，能够让用户有更好的体验（比如模型生成文字速度提升，用户排队时间缩短）。本文对 vllm 和 TGI 两个开源方案进行了实践测试，并整理了一些部署的坑。 测试环境：单卡 4090 + i9-13900K。限制于设备条件，本文仅对单卡部署 llama v2 7B 模型进行了测试 Jul 11, 2024 · 小伙伴们端午安康！ 开源的大模型推理引擎比较热门的的分别为：vLLM、LMDeploy、MLC-LLM、TensorRT-LLM 和 TGI。一个好的推理引擎，一方面是可以更快的生成速度，提高用户体验，另一方面还能提高资源利用率，获得更高的成本效益。 Jul 6, 2024 · Comparison of Latency and Throughput 2. What are vLLM and TGI? vLLM. I have run a couple of benchmarks from the OpenAI /chat/completions endpoint client point of view using JMeter on 2 A100 with mixtral8x7b and a fine tune llama70b models. Mixtral 180 req/min. My questions: Nov 7, 2024 · TGI, created by Hugging Face, is a production-ready library for high-performance text generation. rcecqsybvqvettodvzyfhxlwffgaivykxtlrvrtquapkqjtllg