:loudspeaker: Note: The below data is using TensorRT-LLM v0.5. There have been significant improvements in v0.6 & later. Please see updated Llama performance here.

H200 achieves nearly 12,000 tokens/sec on Llama2-13B with TensorRT-LLM

TensorRT-LLM evaluation of the new H200 GPU achieves 11,819 tokens/s on Llama2-13B on a single GPU. H200 is up to 1.9x faster than H100. This performance is enabled by H200’s larger, faster HBM3e memory.

H200 FP8 Max throughput

Model	Batch Size⁽¹⁾	TP⁽²⁾	Input Length	Output Length	Throughput (out tok/s/GPU)
llama_13b	1024	1	128	128	11,819
llama_13b	128	1	128	2048	4,750
llama_13b	64	1	2048	128	1,349
llama_70b	512	1	128	128	3,014
llama_70b	512	2	128	2048	1,654
llama_70b	64	1	2048	128	341
llama_70b	32	1	2048	128	303

_{Preliminary measured performance, subject to change. TensorRT-LLM v0.5.0, TensorRT v9.1.0.4 | H200, H100 FP8.}

^{(1) Largest batch supported on given TP configuration by power of 2.} ^{(2) TP = Tensor Parallelism}

Additional Performance data is available on the NVIDIA Data Center Deep Learning Product Performance page, & soon in TensorRT-LLM’s Performance Documentation.

H200 vs H100

H200’s HBM3e larger capacity & faster memory enables up to 1.9x performance on LLMs compared to H100. Max throughput improves due to its dependence on memory capacity and bandwidth, benefitting from the new HBM3e. First token latency is compute bound for most ISLs, meaning H200 retains similar time to first token as H100.

For practical examples of H200’s performance:

Max Throughput TP1: an offline summarization scenario (ISL/OSL=2048/128) with Llama-70B on a single H200 is 1.9x more performant than H100.

Max Throughput TP8: an online chat agent scenario (ISL/OSL=80/200) with GPT3-175B on a full HGX (TP8) H200 is 1.6x more performant than H100.

_{Preliminary measured performance, subject to change.
TensorRT-LLM v0.5.0, TensorRT v9.1.0.4. | Llama-70B: H100 FP8 BS 8, H200 FP8 BS 32 | GPT3-175B: H100 FP8 BS 64, H200 FP8 BS 128}

Max Throughput across TP/BS: Max throughput⁽³⁾ on H200 vs H100 varies by model, sequence lengths, BS, and TP. Below results shown for maximum throughput per GPU across all these variables.

_{Preliminary measured performance, subject to change.
TensorRT-LLM v0.5.0, TensorRT v9.1.0.4 | H200, H100 FP8.}

^{(3) Max Throughput per GPU is defined as the highest tok/s per GPU, swept across TP configurations & BS powers of 2.}

Latest HBM Memory

H200 is the newest addition to NVIDIA’s data center GPU portfolio. To maximize that compute performance, H200 is the first GPU with HBM3e memory with 4.8TB/s of memory bandwidth, a 1.4X increase over H100. H200 also expands GPU memory capacity nearly 2X to 141 gigabytes (GB). The combination of faster and larger HBM memory accelerates performance of LLM model inference performance with faster throughput and tokens per second. These results are measured and preliminary, more updates expected as optimizations for H200 continue with TensorRT-LLM.