Tensorrt llm performance benchmark.

Tensorrt llm performance benchmark 969 ms. 11 MIN READ LLM Inference Benchmarking Guide: NVIDIA GenAI-Perf and NIM Nov 26, 2024 · Another notable difference between vLLM and TensorRT-LLM on A100 GPUs was the performance of PP at high request rates, especially as the request rate approached infinity. This post provides a closer look at these results. We used the Llama-3–8B (BF16) with Triton Inference Server, and measured throughput, TTFT, and TPOT on the sampled sentences using benchmarks/benchmark_serving. Jan 30, 2024 · We use the NVIDIA TensorRT-LLM library to quantize and serve our optimized Llama2-70B-Chat model. Sep 13, 2024 · These benchmarks show that TensorRT-LLM delivers substantial improvements in performance, particularly for longer sequences. 2 Brings Super Mode to NVIDIA Jetson Orin Nano and Jetson Orin NX Modules | NVIDIA Technical Blog, and it metion lots of LLM model can be run on Nano (see Table 4. Since all the GPUs I tested feature 4th-generation Tensor Cores, comparing the Tensor Core count per GPU should give us a reasonable metric to estimate the performance for each model. TensorRT-LLM (TRT-LLM) is an open-source library designed to accelerate and optimize the inference performance of large language models (LLMs) on NVIDIA GPUs. Mar 27, 2024 · TensorRT-LLM is a high-performance, open-source software library providing state-of-the-art performance when running the latest LLMs on NVIDIA GPUs. We also examined performance statistics using the TensorRT-LLM gptManagerBenchmark tool, focusing on the FP16 baseline and FP8 quantized engines for batch size Jan 21, 2024 · Large Language Model (LLM) and Vision-Language Model (VLM) are the most interesting ones. Network Throughput GPU TensorRT-LLM: NVIDIA B200: Attention: Tensor Parallelism = 8 You signed in with another tab or window. Early KV cache reuse. NVIDIA’s TensorRT-LLM was introduced as part of the previous LMI DLC release (0. Mar 27, 2024 · Fine-tuning on TensorRT-LLM has been ongoing ever since the AI Software suite was released last year. 5 days ago · For inference, the NeMo Framework provides a path that leverages TensorRT-LLM, a specialized library for accelerating and optimizing LLM inference on NVIDIA GPUs. Dec 9, 2024 · This technique is implemented in TensorRT-LLM as Chunked Context. For vLLM, we have turned on multistep scheduling via setting --num-scheduler-steps 10. Even though TensorRT is the fastest inference engine, it’s really a pain to set up and fix the errors. Dec 4, 2023 · TensorRT-LLM optimizes the performance of a range of well-known models on NVIDIA GPUs. Oct 11, 2024 · In our previous article, we compared vLLM and TensorRT-LLM under default configurations and specific constraints, providing insights into their baseline performance. 63 tokens/sec with 20 Input tokens and 200 Output tokens. 25. When the recent pipeline parallelism improvements in TensorRT-LLM were applied to MLPerf Llama 2 70B scenario, throughput on an HGX H100 8-GPU system increased by 21% compared to our MLPerf Inference v4. Dec 14, 2023 · AMD’s implied claims for H100 are measured based on the configuration taken from AMD launch presentation footnote #MI300-38. 14 and 0. However, relying on default settings or adjusting just a single parameter is not enough to fully exploit the capabilities of these frameworks, especially in complex real-world environments. Output tokens/second is inclusive of time to generate the first token – tok/s =total generated tokens / total latency. Aug 28, 2024 · Table 3. Each benchmark runs an inference engine that provides some sort of optimizations either through just quantization or device-specific optimizations like custom cuda kernels. TensorRT-LLM requires models to be compiled into efficient engines before deployment. Table 1. DGX H200, TP8, batch size = 1, TensorRT Model Optimizer version 0. Mar 20, 2025 · This is the second post in the LLM Benchmarking series, which shows how to use GenAI-Perf to benchmark the Meta Llama 3 model when deployed with NVIDIA NIM. TensorRT-LLM continues LLM-specific optimizations with many new models, features, and performance improvements. Breaking down the traditionally sequential prefill phase into smaller, more manageable chunks, enables better parallelization, with the decode phase, reducing bottlenecks and accelerating query completion. OpenAI had figured out they couldn't manage in sense of performance 2T model splitted on several gpus, so they invented GPT-4 moe architecture, but it was a decision forced by limited time. SGLang Overview Jan 8, 2025 · These considerations motivated our decision to choose SGLang as our LLM inference system as it has a performance-oriented design and easy-to-modify Python code base, instead of other production-ready ML systems like vLLM and TensorRT-LLM. ai on our public benchmarks. 7x speed-up in generated Oct 18, 2024 · Since TensorRT-LLM C++ API benchmark tool originally does not support sampling options, we adopted the measurement approach used in vLLM benchmark. Despite its impressive performance, vLLM was incredibly user-friendly. Sep 9, 2023 · The following benchmarks show performance improvements brought by TensorRT-LLM on the latest NVIDIA Hopper architecture. 9x on NVIDIA HGX H200 This document summarizes those implementations and how they are optimized in TensorRT-LLM. It supports any LLM inference service conforming to the OpenAI API specification, a widely accepted de facto standard in the industry. The open-source library — which was not ready in time for August submission to MLPerf — enables customers to more than double the inference performance of their already Dec 17, 2024 · In this post, we show how the NVIDIA HGX H200 platform with NVLink and NVSwitch, as well as TensorRT-LLM, achieve great performance when running the latest Llama 3. It is important to keep chunks large enough to still be able to reach compute-boundness. 7. TensorRT-LLM supports INT4 or INT8 weights (and FP16 activations; a. 86, respectively, using the Meta official FP8 recipe. NVIDIA TensorRT-LLM offers three key features that specifically address these areas. You can read more from their initial paper here. Graph Rewriting TensorRT-LLM uses a declarative approach to define neural networks and contains techniques to optimize the underlying graph. For ease of use, TensorRT-LLM provides Docker images to create a controlled environment for building and running models. You switched accounts on another tab or window. Large NVLink domains: The NVIDIA GH200 NVL32 system, powered by 32 NVIDIA GH200 Grace Hopper Superchips connected using the NVLink Switch system, and with TensorRT-LLM improvements, delivers up to 3x faster TTFT for Llama Jan 13, 2025 · Introduction to TensorRT-LLM. Learn more about TensorRT. 1 benchmark, hosted by MLCommons, we showcased the performance of NVIDIA Triton on a TensorRT-LLM optimized Llama-v2-70B model. 7 MIN READ Benchmarking Agentic LLM and VLM Reasoning for Gaming with NVIDIA NIM Oct 19, 2023 · Learn more about NVIDIA NeMo, which provides complete containers (including TensorRT-LLM and NVIDIA Triton) for generative AI deployments. Nvidia is also working on a TensorRT-LLM tool that will allow the use of Llama 2 as the Jul 6, 2024 · TensorRT-LLM is another inference engine that accelerates and optimizes inference performance for the latest LLMs on NVIDIA GPUs. Feb 21, 2024 · The latest benchmarks clearly illustrate the remarkable strides made possible by TensorRT LLM, particularly when it comes to reducing inference latency for real-time performance. Dec 16, 2023 · AMD made three performance runs using Nvidia's TensorRT-LLM, the last notable one having measured latency results between MI300X and vLLM using the FP16 dataset against H100 with TensorRT-LLM. In this case, the ResNet-50 model with batch size 4 can run with a throughput of 507 inferences per second (2028 images per second since the batch size is 4) and a median latency of 1. 1 405B using MMLU and MT-Bench. In this scenario, PP delivered surprisingly strong performance in TensorRT-LLM, but vLLM failed to scale. We used the TensorRT-LLM pip version of 0. Throughput performance using four NVIDIA H200 Tensor Core GPUs with TensorRT-LLM internal measurements. This section includes a step-by-step walkthrough Aug 13, 2024 · vLLM — Llama3–70B-FP8 on 50% vRAM of H100 (Sequential Request) For sequential requests of Llama3-70B-F8, SGLang shows slightly higher performance for sequential requests, achieving 38 tokens Jan 20, 2025 · To effectively evaluate the serving performance of vLLM and TensorRT-LLM, we designed experiments that reflect common use cases of Vision-Language Models (VLMs). 16. dev, TensorRT version 10. Performance table taken from the TensorRT-LLM website. 7x in Llama-2-70B inference performance (2048 input length and 128 output length) running on TensorRT-LLM relative to A100. 0 in our experiments. 1 70B and Llama 3. Mar 19, 2024 · In our benchmarking of three LLMs, the results are as follows: Mistral 7Bn, in conjunction with TensorRT-LLM, achieved the highest performance, reaching a maximum of 93. Feb 14, 2025 · Figure 1. TensorRT-LLM v0. Dec 18, 2024 · In benchmarking a tens-of-billions parameter production model on NVIDIA GPUs, using the NVIDIA TensorRT-LLM inference acceleration framework with ReDrafter, we have seen 2. Apr 30, 2024 · Welcome to our benchmarking repository! This organized structure is designed to simplify benchmark management and execution. 0, and lmdeploy v0. 11. TensorRT-LLM offers incredible performance for embedding models through optimized inference engines. Benchmarks for Mixtral 8x7B with TensorRT-LLM. Oct 10, 2024 · TensorRT-LLM（TensorRT for Large Language Models）是NVIDIA推出的一个高性能深度学习推理优化库，专门针对大型语言模型（LLM）进行优化。。模型优化：通过层融合、内核选择和精度调整等技术，显著提升模型的推理速度和效 Jul 25, 2024 · Publication of benchmarks Published per-commit performance tracker at perf. 1 results & now with MLPerf Oct 24, 2024 · While vLLM and TensorRT-LLM have several differences, one of the most notable distinctions is in their schedulers. With these upgrades, you can effortlessly access state-of-the-art tooling to optimize large language models (LLMs) on SageMaker and achieve price-performance benefits – Amazon SageMaker LMI TensorRT-LLM DLC reduces This Best Practices Guide covers various performance considerations related to deploying networks using TensorRT 8. May 6, 2025 · This is the second post in the LLM Benchmarking series, which shows how to use GenAI-Perf to benchmark the Meta Llama 3 model when deployed with NVIDIA NIM. To get started you need to download the models Sep 13, 2024 · These benchmarks show that TensorRT-LLM delivers substantial improvements in performance, particularly for longer sequences. Serving engines. 4. TensorRT-LLM version 0. Let’s also benchmark the model’s performance through vLLM Mar 20, 2025 · This is the first post in the LLM Benchmarking series, which shows how to use GenAI-Perf to benchmark the Meta Llama 3 model when deployed with NVIDIA NIM. Optimized request batching and management are the key to improving performance and lowering costs, especially with the constantly changing demands on computations and memory. 0 Performance Benchmarks Offline Scenario, Closed Division. 10% in tokens per second. 0 against TensorRT-LLM r24. TensorRT-LLM and SGLang perform equally well and can sustain an RPS > 10, while the latency of vLLM increases significantly at a high request rate. Jun 17, 2024 · TensorRT-LLM was the most challenging to set up in our benchmark test. Feb 16, 2024 · Based on the name alone, it’s safe to assume that TensorRT-LLM performance benchmarks will scale closely with Tensor Core performance. Output tokens/second is inclusive of time to generate the first token – tok/s = total generated tokens / total latency. The dynamic and evolving LLM ecosystem, with the continuous introduction of new models and technologies, requires high-performance and flexible solutions to optimize LLMs for production deployments. Reload to refresh your session. Sep 4, 2024 · Main steps to serve LLMs with TRT-LLM and BentoML; Benchmark client; Key Findings. NVIDIA JetPack 6. Hands-On: Installing and Building TensorRT-LLM Step 1: Create a Container Environment. As part of the process, we've run some benchmarks, to see how TensorRT-LLM fares on consumer hardware (e. LLM-Profiler 是一个测试 llm 性能（速度和吞吐量）的工具，适配了 TensorRT-LLM、vLLM、TGI 等常见的 LLM 推理框架。 与 vLLM 等推理框架的性能测试不同，这些推理框架在测试性能的时候，主要测试的是离线场景下系统的极限吞吐量，比较适合跑 benchmark 显示自己的性能极限，但是这些框架的测试方法并不 This article provides a comparative analysis of vLLM and TensorRT-LLM frameworks for serving LLMs, evaluating their performance based on key metrics like throughput, TTFT, and TPOT to offer insights for practitioners in optimizing LLM deployment strategies. The goal is to identify gaps in performance and close them. Output Throughput(High is Better) Mean Latency(Low is Better) Median Latency(Low is Better) Median TTFT(Low is Better) Oct 17, 2023 · TensorRT then boosts performance an additional 50~65 percent at 512x512, and 45~70 percent at 768x768. Why TensorRT and TensorRT-LLM improve H100 inference. Higher is better. Mar 27, 2024 · Here’s the TensorRT-LLM performance results showing nearly a three-fold improvement in performance on GPT-J (a smaller LLM) over the last six months since the compiler was released. 0 . 0. Jan 26, 2025 · Utilizing optimized frameworks and libraries can further enhance DeepSeek V3's performance on the RTX 4090: TensorRT-LLM: NVIDIA's TensorRT-LLM is specifically designed to optimize large language models for inference on NVIDIA GPUs, enhancing t/s rates through efficient kernel implementations and memory management. vllm. As for TensorRT-LLM I think it is more about effectiveness of tensor cores utilization in LLM inference. Our benchmark tests demonstrate a jump from 19 tokens per second with standard Dec 4, 2023 · TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. Oct 19, 2023 · We do not plan to publish performance numbers that compare TensorRT-LLM with vLLM. Future Outlook - vLLM: Expanding hardware Nov 27, 2023 · Today, Amazon SageMaker launches a new version (0. NVIDIA TensorRT is a high-performance deep learning inference library focused on optimizing and deploying AI models on NVIDIA GPUs. For more details, please refer to doc Benchmark TensorRT-LLM provides C++ and Python tools to perform benchmarking Aug 30, 2024 · Recommendation: For developers prioritizing tokens/sec performance, Qwen2-7B-Instruct with TensorRT-LLM is the top pick, especially for heavy workloads. 0) of Large Model Inference (LMI) Deep Learning Containers (DLCs) and adds support for NVIDIA’s TensorRT-LLM Library. 0a0. The latest TensorRT container is still compatible with Pascal GPUs. 2 inference software with NVIDIA DGX H100 system, Llama 2 70B query with an input sequence length of 2,048 and output sequence length of 128. Up to 6. We find that we can quantize Llama2-70B-Chat and achieve: (a) A 50% smaller model, lowering GPU memory requirements and allowing us to fit a 2x larger batch size on the same hardware. GenAI-Perf serves as the default benchmarking tool for assessing performance across all NVIDIA generative AI offerings, including NVIDIA NIM, NVIDIA Triton Inference Server, and NVIDIA TensorRT-LLM. Apr 24, 2025 · This is the first post in the LLM Benchmarking series, which shows how to use GenAI-Perf to benchmark the Meta Llama 3 model when deployed with NVIDIA NIM. TensorRT supports Pascal architecture up to TensorRT 9, but Nvidia recommend to use 8. 1 results published in August. With this throughput performance benchmark, I would not use Raspberry Pi 5 as LLMs inference machine Jul 25, 2024 · The online benchmark figure below shows a trend similar to the offline case. The first is GPT-J, which was introduced in the prior round of MLPerf, and the second is the newly added Llama 2 70B benchmark. Benchmark performance in tokens/sec for popular LLMs on Jetson Orin Nano 8GB in this topic) However, when Dec 18, 2024 · In benchmarking a tens-of-billions parameter production model on NVIDIA GPUs, using the NVIDIA TensorRT-LLM inference acceleration framework with ReDrafter, we have seen 2. 2. The following figures reflect article summarization using an NVIDIA A100 and NVIDIA H100 with CNN/Daily Mail, a well-known dataset for evaluating summarization performance. The impact of TensorRT-LLM on Copilot’s performance goes beyond mere anecdotes. Jan 30, 2024 · This document summarizes those implementations and how they are optimized in TensorRT-LLM. py script from the vLLM source. The company’s TensorRT-LLM is an open-source software library developed to double the speed of inferencing LLMs on its H100 GPUs. 0 recipe. 1. It facilitates easy comparisons Dec 4, 2023 · This document summarizes those implementations and how they are optimized in TensorRT-LLM. Here we have an official table showing the performance of this library using A100 GPUs running some models with FP16. Jul 25, 2024 · The online benchmark figure below shows a trend similar to the offline case. - forrestjgq/trtllm NVIDIA TensorRT-LLM provides an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. Feb 22, 2024 · Performance Benchmark. Sep 10, 2024 · The throughput numbers reported should not be considered peak performance, as they could be further improved using other features of TensorRT-LLM such as in-flight batching, for example. Hey r/nvidia folks, we've done a performance benchmark of TensorRT-LLM on consumer-grade GPUs, which shows pretty incredible speed ups (30-70%) on the same hardware. 2 can enhance the performance of the NX and Nano. Performance measurements at large batch sizes were taken to represent high-throughput scenarios. Mar 27, 2024 · Nvidia reports that its new Hopper H200 AI GPU combined with its performance-enhancing TensorRT LLM has broken the record in the latest MLPerf performance benchmarks. For shorter sequences, such as 1K or 2K, the Mar 18, 2025 · In this benchmark, we evaluate the performance of three inference backends—SGLang, vLLM, and TensorRT-LLM—on two hardware configurations: 8x NVIDIA H200 and 8x AMD MI300X. May 14, 2024 · It also includes Model Optimizer, a comprehensive library of post-training and training-in-the-loop model optimizations that deploy to TensorRT-LLM or TensorRT. If you need slightly better performance with smaller token counts, Llama-3. For example, if the plan file is saved as resnet50-v1-12-quantized. Sep 11, 2023 · TensorRT-LLM Supercharges Inference To cut through complex workloads of every size, NVIDIA developed TensorRT-LLM , generative AI software that optimizes inference. 2. Nov 8, 2024 · Optimizing these factors can lead to incremental performance improvements in KV cache reuse. Mar 10, 2025 · In addition to its user-friendly deployment process, the DriveOS LLM SDK provides a variety of C++ code examples for end-to-end LLM inference, performance benchmarking, and live chat implementations. To get started you need to download the models In this quick start, we will use GenAI-Perf to run performance benchmarking on the GPT-2 model running on Triton Inference Server with a TensorRT-LLM engine. Sep 5, 2024 · Upcoming TensorRT-LLM optimizations, including the improvement of a speculative decoding algorithm called Medusa, provide outstanding low latency performance on Llama 3. It is designed and optimized for NVIDIA GPUs by leveraging TensorRT, CUDA and cuDNN libraries to accelerate LLM inference. Nov 1, 2024 · To enhance inference performance in production-grade setups, we’re excited to introduce TensorRT-LLM Multi-shot, a new multi-GPU communication protocol that leverages the NVIDIA NVLink Switch to significantly increase communication speeds by up to 3x. We benchmarked Mistral 8x7B with TensorRT-LLM versus a baseline implementation on A100 GPUs. — - 7. Our internal measurements show that TensorRT-LLM’s in-flight batching and paged KV cache features work well and TensorRT-LLM can deliver great performance. 18 and MMLU benchmark accuracy score is 0. Jun 17, 2024 · 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 Aug 28, 2024 · At this year’s MLPerf Inf v4. Max Batch Size. The performance data was gathered following the benchmarks outlined in the respective folder, ensuring a standardised approach to measure and validate the performance of TensorRT-LLM. The following sections provide a list of supported GPU architectures as well as important features implemented in TensorRT-LLM. These sections assume that you have a model that is working at an appropriate level of accuracy and that you are able to successfully use TensorRT to do inference for your model. 60 with 20 input tokens and 500 output tokens, outperforming vLLM by about 6. Serve GPT-2 TensorRT-LLM model using Triton CLI# You can follow the quickstart guide in the Triton CLI Github repository to serve GPT-2 on the Triton server with the TensorRT-LLM backend. You can immediately try Llama 3 8B and Llama… Jan 24, 2025 · MLPerf Inference is a suite of industry-standard inference performance benchmarks developed by the MLCommons consortium. Let’s delve into the concrete data. Dec 4, 2023 · NVIDIA TensorRT-LLM provides optimizations for both peak throughput and memory optimization, delivering massive improvements in LLM inference performance. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines. 6 on Pascal. This blog outlines this new feature and how it helps developers and solution architects Aug 28, 2024 · First Llama 2 70B submissions using NVIDIA Triton Inference Server, delivering similar performance to NVIDIA TensorRT-LLM submissions. In this guide, I’ll walk through how to May 14, 2025 · It prints many performance metrics, but the most important are Throughput and median Latency. TensorRT does work only on a single GPU, while TensorRT-LLM support multi GPU hardware. However, if you're still interested in TensorRT-LLM, we have a tutorial available for you to read. We saw a major increase in performance with the previous MLPerf v3. 2x higher performance on the GPT-J benchmark in the edge category compared to the prior round using the NVIDIA Jetson AGX Orin platform. TensorRT-LLM consists of the TensorRT deep learning compiler and includes optimized kernels, pre- and post-processing steps, and multi-GPU/multi-node communication primitives for groundbreaking performance on NVIDIA GPUs. Published reproducible benchmark of vLLM compared to LMDeploy, TGI, and TensorRT-LLM. High Throughput. We believe in giving back to the community. TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and support state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. Oct 30, 2024 · Figure 2 illustrates the throughput comparison of Fixed and Dynamic dataset benchmarks in vLLM and TensorRT-LLM. 1 day ago · If you construct the TensorRT INetworkDefinition using TensorRT APIs and build the plan file in a separate script, you can still use trtexec to measure the plan file’s performance. 4090s (opens in a new tab), 3090s (opens in a new tab)) we commonly see in the Jan's hardware community (opens in a new tab). Without enough quality examples, we had to read through the documentation of TensorRT-LLM, _tensorrtllmbackend and Triton Inference Server, convert the checkpoints, build the TRT engine, and write a lot of configurations. Using vLLM v. Results NVIDIA GeForce RTX 4090 GPU Feb 28, 2025 · Evaluating the performance of LLM-serving frameworks such as vLLM, OpenAI, tensorrt-llm, and sglang is crucial for optimizing throughput and latency. Figure 1 reveals that TensorRT LLM models significantly outperform traditional models during the prefill phase. k. 3x in vector search time, and 5. For running Jan 29, 2025 · Optimizing LLM performance on GPUs is challenging due to diverse model needs, memory constraints, and balancing latency and throughput. 02. INT4/INT8 weight-only) as well as a complete implementation of the SmoothQuant technique. . Part 1: LLM Inference Benchmarking: Fundamental Concepts; When building LLM-based applications, it is critical to understand the performance characteristics of these models on a given hardware. Sep 5, 2024 · And it reaches state-of-the-art performance according to our performance benchmarks. TensorRT-LLM engines have two parameters called max_batch_size: LLM Inference benchmark. Jetson Benchmarks. The following figures reflect article summarization using an NVIDIA A100 and NVIDIA H100 GPUs with CNN/Daily Mail, a well-known dataset for evaluating summarization performance. 9; Input tokens = 2048; output tokens = 512. We’d be happy to provide you with performance numbers for relevant cases. In this report, we’ll review our benchmarks for Mistral 7B and Stable Diffusion XL and discuss why TensorRT/TensorRT-LLM offer such excellent performance for model inference on H100 GPUs. 0), enabling state-of-the-art GPU performance and optimizations like SmoothQuant, FP8, and continuous batching for LLMs when using NVIDIA GPUs. | Tech, vLLM vs TRT LLM Apr 28, 2024 · We’re excited to announce support for the Meta Llama 3 family of models in NVIDIA TensorRT-LLM, accelerating and optimizing your LLM inference performance. Jun 14, 2024 · LLM-Benchmarks is an easy-to-use toolbox for benchmarking Large Language Models (LLMs) performance on inference and evalution. May 2, 2024 · Introducing Benchmarks v2. These examples enable developers to evaluate the accuracy and performance of different models on DRIVE platforms, using static batch sizes and Inference Performance: In our LLM quantization benchmark, we prioritize the importance of selecting a quantization approach that enhances inference performance. Therefore, TensorRT-LLM can be used only to accelerate LLMs on NVIDIA GPUs. For more details, please refer to doc Benchmark TensorRT-LLM provides C++ and Python tools to perform benchmarking Feb 5, 2025 · Recently, I saw in Nvidia’s press release that Jetpack 6. This document examines how hardware utilization, memory and communication bandwidth and scaling, contribute to inference performance, detailing optimal configurations for AMD Instinct™ MI300X GPUs. Apr 8, 2024 · TensorRT-LLM backend. It's built on top of NVIDIA's TensorRT, which is already a powerhouse for deep learning inference. Data measured on 11/18/2024. These results were obtained using a version of the performance Dec 18, 2023 · This includes an increase of 2. We benchmark the vLLM v0. 1-8B-Instruct with TensorRT-LLM is your best bet. a. 6. 7x speed-up in generated tokens per second for greedy decoding (see Figure 1). , TensorRT-LLM, lmdeploy and vLLM,) under different batch sizes and generation lengths. 1 405B of 268 tokens/second/user and 108 tokens/second/user, respectively on HGX H200. For more details, please refer to doc Benchmark TensorRT-LLM provides C++ and Python tools to perform benchmarking MLPerf Inference v5. Data measured on 11/4/2024. Devices. 92%. All performance numbers are tested with TensorRT-LLM or TensorRT. We see at least a 15% speedup from enabling TensorRT-LLM into the stack, jointly with minimizing latency between the Rust frontend and TensorRT-LLM runtime. 3. The latest TensorRT-LLM enhancements on NVIDIA H200 GPUs deliver a 6. Feb 8, 2024 · Comparing Copilot performance with and without TensorRT-LLM. Researchers from the University College London (UCL) Deciding, Acting, and Reasoning with Knowledge (DARK) Lab leverage NVIDIA NIM microservices in their new game-based benchmark suite, Benchmarking Agentic LLM and VLM Reasoning On Games Apr 26, 2024 · LLama-2-13b, using TensorRT-LLM, recorded the highest tokens per second at 52. Just quick notes: TensorRT-LLM is NVIDIA's relatively new and (somewhat) open source Inference Engine, which uses NVIDIA’s proprietary optimizations beyond the open source cuBLAS Feb 16, 2024 · Based on the name alone, it’s safe to assume that TensorRT-LLM performance benchmarks will scale closely with Tensor Core performance. We are Nov 19, 2024 · TensorRT optimized NIM for VLMs version 1. The process of selecting a response time budget requires a careful balancing of throughput and user interactivity, as increases in one translate into reductions in the other. We wanted to demonstrate that enterprises can use the advanced production-grade capabilities of NVIDIA Triton without incurring the high latency and throughput overhead typically Oct 9, 2024 · The TensorRT-LLM software improvements also benefit smaller models. Performance Summary for Large Language Models# Below are performance benchmarks for various large language models. Sorry but nope Tensor in TensorRT-LLM doesn't stand for tensor core. Our goal is to compare throughput, latency, and overall efficiency to determine the optimal backend and hardware pairing for DeepSeek-R1's demanding requirements. So today we introduce Prem Benchmarks. 1 (opens in a new tab) and build on Windows; For TensorRT-LLM, we used Mistral-7b-int4 AWQ; We ran TensorRT-LLM with free_gpu_memory_fraction to test it with the lowest VRAM consumption; Note: We picked AWQ for TensorRT-LLM to be a closer comparison to GGUF's Q4. These benchmark results indicate this tech could significantly reduce latency users may May 23, 2024 · The benchmarks were optimized with NVIDIA TensorRT-LLM. TensorRT-LLM also contains components to create Python and C++ runtimes that execute… Dec 2, 2024 · Table 1. Oct 31, 2024 · It is designed and optimized for NVIDIA GPUs by leveraging TensorRT, CUDA and cuDNN libraries to accelerate LLM inference. Lookahead decoding workflow with (W, N, G) = (5, 3, 2). g. The goal of this is to track performance enhancement and regressions. Mar 27, 2024 · Nvidia has set performance records on both new workloads, providing the highest performance across all MLPerf Inference workloads in the data center category. Jan 31, 2025 · - Performance: Benchmarks show 24x higher throughput than Hugging Face Transformers, - Use TensorRT-LLM for peak NVIDIA GPU performance. 21 (prerelease), TensorRT-LLM version 0. Nevertheless, we plan to conduct inference system comparative benchmarking in the future. The new benchmark uses the largest version of Llama 2, a state-of-the-art large language model packing 70 billion parameters. We are working with the NVIDIA team to correctly benchmark the performance of TensorRT-LLM on this model. TensorRT-LLM is rigorously tested on the following GPUs: H100; L40S; A100; A30; V100 (experimental) Mar 27, 2024 · TensorRT-LLM running on NVIDIA H200 Tensor Core GPUs — the latest, memory-enhanced Hopper GPUs — delivered the fastest performance running inference in MLPerf’s biggest test of generative AI to date. TensorRT-LLM Small LLM (SLM) API Examples For running Riva benchmarks, see ASR Performance and TTS Performance . Concepts Llama 3 Jul 25, 2024 · As the 405B model just came out, some of the latest optimizations in TensorRT-LLM have not been included in the pre-built Docker image, so we omitted the performance of TensorRT-LLM here. These scenarios help us analyze how different factors, such as the number of image inputs and the scaling of output complexity, impact key performance metrics like Throughput , FPS Sep 8, 2023 · While the H100 is four times the performance of the previous A100, based on benchmarks for the GPT-J 6B LLM inferencing, the new TensorRT-LLM can double that throughput to an 8X advantage for JPT Feb 3, 2025 · Normalized throughput on Mixtral-8x7B models with tensor parallelism. 07, SGLang v0. Performance benchmark of the NVIDIA TensorRT Model Optimizer FP8 and INT4 AWQ compared to FP16 baseline for Llama 3 7B and 70B models at different batch sizes (BS) on NVIDIA H100. For other benchmarks, we use their default setting. TensorRT-LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in performant way. Traditional reuse algorithms require the entire KV cache computation to be completed before any portions of it can be reused with new user prompts. Image credit: Break the Sequential Dependency of LLM Inference Using Lookahead Decoding Lookahead performance greatly depends on the base model, hardware, batch size, sequence length, and the dataset. Inference Performance: Benchmarking LLMs service deployed with inference frameworks (e. Output Throughput(High is Better) Mean Latency(Low is Better) Median Latency(Low is Better) Median TTFT(Low is Better) What are some other good benchmarking studies on production inference? TensorRT-LLM is was released later than the previous two and is still catching up. Medusa boosts token generation by up to 1. With TensorRT-LLM, our Copilot scales to handle over 2x tokens per second. 7x speedup on the Llama 2 70B LLM, and enable huge models, like Falcon-180B, to run on a single GPU. With larger batches, TensorRT offers Oct 10, 2024 · Its efficiency and flexibility make it an excellent choice for low-latency, high-throughput LLM applications. Posted by u/Few_Hair8180 - 3 votes and 11 comments If the quantized model’s quality is acceptable, we package it for production use and serve it in production with TensorRT-LLM for optimized inference. This study, executed on the Llama 3 8B and 70B 4-bit quantization models on an A100 80GB This is the second post in the LLM Benchmarking series, which shows how to use GenAI-Perf to benchmark the Meta Llama 3 model when deployed with NVIDIA NIM. 7x in embedding generation, 2. TensorRT-LLM is a high-performance inference library designed specifically for large language models. This is a fully open-source project with its primary objective being to benchmark popular LLM inference engines (currently 13 + engines) like vLLM, TensorRT LLM, HuggingFace Transformers, etc on different precisions like float32, float16, int4, and int8. You signed out in another tab or window. Contribute to ninehills/llm-inference-benchmark development by creating an account on GitHub. MLPerf Inference v4. Jetson is used to deploy a wide range of popular DNN models, optimized transformer models and ML frameworks to the edge with high performance inferencing, for tasks like real-time classification and object detection, pose estimation, semantic segmentation, and natural language processing (NLP). 3 70B with TensorRT-LLM. Sep 11, 2023 · The following benchmarks show performance improvements brought by TensorRT-LLM on the latest NVIDIA Hopper architecture. TTFT is important for This document summarizes performance and accuracy measurements of TensorRT Model Optimizer for a few popular models. Explore sample code, benchmarks, and TensorRT-LLM documentation on GitHub. 3 70B model. TensorRT. Nov 15, 2024 · Using TensorRT-LLM chunked prefill significantly improves both system performance and utilization. The benchmark in the following tables is provided as reference points and should not be considered as the peak performance that can be delivered by Model Optimizer. Before we dive into the nitty-gritty, let's get a clear picture of what TensorRT-LLM is all about. By benchmarking the model to understand its performance at different batch sizes, we can make appropriate tradeoffs between cost and performance and build optimized serving engines to target Below, we’ll share benchmarks for one language model (Mixtral 8x7B) and one image model (SDXL) as examples of the performance gains that are possible with TensorRT. 9x in index build, 3. Oct 10, 2024 · Based on the name alone, it’s safe to assume that TensorRT-LLM performance benchmarks will scale closely with Tensor Core performance. May 8, 2024 · Figure 1. 0 includes two LLM tests. TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and support state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. We describe the step-by-step setup to get speculating decoding working for Llama 3. (b) Up to 30% faster output token generation. Jun 9, 2024 · A recent benchmark study conducted by the BentoML engineering team offers valuable insights into the performance of various inference backends, specifically focusing on vLLM, LMDeploy, MLC-LLM, TensorRT-LLM, and Hugging Face TGI (Text Generation Inference). This surpassed vLLM by approximately 5. Performance benefits from TensorRT-LLM. This means our chosen setting should either increase throughput or decrease memory requirements, thereby optimizing the efficiency of the model during the inference phase. The pairing together has May 2, 2025 · LLM Inference performance drives real-time, cost-effective production deployment. 86, compared to 9. Inference accuracy results of Llama 3. Jul 2, 2024 · TensorRT-LLM supports in-flight batching, which enables completed requests to be replaced with new requests during LLM serving and helps to improve performance. Aug 1, 2024 · Facilitate standardized performance evaluation across diverse inference engines through an OpenAI-compatible API. plan , then you can run the trtexec command to measure the performance using this plan file: We've been excited for TensorRT-LLM for a while, and had a lot of fun implementing it (opens in a new tab). Hardware and software for test scenario 1 Dec 4, 2023 · NVIDIA TensorRT-LLM provides optimizations for both peak throughput and memory optimization, delivering massive improvements in LLM inference performance. The MT-Bench accuracy score with the new PTQ technique and measured with TensorRT-LLM is 9. Similar to the previous blog post, we evaluated TensorRT-LLM serving performance with two key metrics: Time to First Token (TTFT): Measures the time from when a request is sent to when the first token is generated, recorded in milliseconds. 11 MIN READ LLM Inference Benchmarking Guide: NVIDIA GenAI-Perf and NIM Dec 4, 2023 · To maximize performance and reduce memory footprint, TensorRT-LLM allows the models to be executed using different quantization modes (see examples/gpt for concrete examples). TRT-LLM offers users an easy-to-use Python API to build TensorRT engines for LLMs, incorporating state-of-the-art optimizations to ensure efficient inference on NVIDIA GPUs. May 14, 2025 · Using GenAI-Perf to Benchmark# NVIDIA GenAI-Perf is a client-side LLM-focused benchmarking tool, providing key metrics such as TTFT, ITL, TPS, RPS and more. The H100 isn’t just an A100 with more cores and faster memory. hnuy fqpfxa eocrl xmhymw ziuz rgp ayquoh czeb lsqrox jnfz