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[doc] Update perf_overview.md for release 0.21 (#6270)

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docs/source/performance/perf-overview.md
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@ -12,6 +12,8 @@ Tuning batch sizes, parallelism configurations, and other options may lead to im
For DeepSeek R1 performance, please check out our [performance guide](../blogs/Best_perf_practice_on_DeepSeek-R1_in_TensorRT-LLM.md)
For more information on benchmarking with `trtllm-bench` see this NVIDIA [blog post](https://developer.nvidia.com/blog/llm-inference-benchmarking-performance-tuning-with-tensorrt-llm/).
## Throughput Measurements
The below table shows performance data where a local inference client is fed requests at an infinite rate (no delay between messages),
@ -21,50 +23,64 @@ The performance numbers below were collected using the steps described in this d
Testing was performed on models with weights quantized using [ModelOpt](https://nvidia.github.io/TensorRT-Model-Optimizer/#) and published by NVIDIA on the [Model Optimizer HuggingFace Collection](https://huggingface.co/collections/nvidia/model-optimizer-66aa84f7966b3150262481a4).
### FP4 Models:
```
### Hardware
The following GPU variants were used for testing:
- H100 SXM 80GB (DGX H100)
- H200 SXM 141GB (DGX H200)
- GH200 96GB HBM3 (480GB LPDDR5X)
- B200 180GB (DGX B200)
- GB200 192GB (GB200 NVL72)
Other hardware variants may have different TDP, memory bandwidth, core count, or other features leading to performance differences on these workloads.
### FP4 Models
```text
nvidia/Llama-3.3-70B-Instruct-FP4
nvidia/Llama-3.1-405B-Instruct-FP4
```
#### Llama 3.3 70B FP4
| | GPU | B200 | | | |
|:------------------------|:--------|:----------|:----------|:----------|:----------|
| | TP Size | 1 | 2 | 4 | 8 |
| ISL, OSL | | | | | |
| | | | | | |
| 128, 128 | | 10,994.48 | 17,542.11 | 24,667.31 | 27,272.27 |
| 128, 2048 | | 9,580.46 | 15,432.35 | 23,568.12 | 31,174.31 |
| 128, 4096 | | 6,418.39 | 9,841.53 | 17,808.76 | 25,229.25 |
| 500, 2000 | | 7,343.32 | 11,850.57 | 20,709.67 | 28,038.78 |
| 1000, 1000 | | 6,752.53 | 10,815.88 | 16,413.04 | 20,060.66 |
| 1000, 2000 | | 6,670.07 | 9,830.73 | 15,597.49 | 20,672.37 |
| 1024, 2048 | | 6,636.75 | 9,807.13 | 15,519.23 | 20,617.28 |
| 2048, 128 | | 1,342.17 | 1,989.41 | 3,033.14 | 4,035.64 |
| 5000, 500 | | 1,429.67 | 2,419.67 | 3,686.84 | 5,182.96 |
| 20000, 2000 | | 629.77 | 1,177.01 | 2,120.66 | 3,429.03 |
| | GPU: | B200 | GB200 |
|:-----------------------------|:---|:----------|:--------------|
| | TP Size | 1 | 1 |
| ISL, OSL | | | |
| | | | |
| 128, 128 | | 10,613.84 | 11,100.97 |
| 128, 2048 | | 9,445.51 | 10,276.05 |
| 128, 4096 | | 6,276.85 | 7,351.12 |
| 500, 2000 | | 6,983.27 | 8,194.30 |
| 1000, 1000 | | 6,434.29 | 7,401.80 |
| 1000, 2000 | | 6,725.03 | 6,478.72 |
| 1024, 2048 | | 6,546.61 | 7,922.88 |
| 2048, 128 | | 1,330.35 | 1,418.47 |
| 2048, 2048 | | 4,528.48 | 5,326.77 |
| 5000, 500 | | 1,427.44 | 1,502.44 |
| 20000, 2000 | | 636.36 | 732.43 |
#### Llama 3.1 405B FP4
| | GPU | B200 | |
|:------------------------|:------- |:---------|:----------|
| | TP Size | 4 | 8 |
| ISL, OSL | | | |
| | | | |
| 128, 128 | | 6,163.81 | 9,002.90 |
| 128, 2048 | | 7,081.21 | 10,288.28 |
| 128, 4096 | | 6,028.37 | 8,713.77 |
| 500, 2000 | | 5,858.75 | 9,125.86 |
| 1000, 1000 | | 4,848.00 | 7,582.97 |
| 1000, 2000 | | 5,375.25 | 7,626.28 |
| 1024, 2048 | | 5,345.70 | 7,464.03 |
| 2048, 128 | | 693.55 | 1,086.56 |
| 5000, 500 | | 947.49 | 1,532.45 |
| 20000, 2000 | | 641.11 | 1,097.84 |
| | GPU: | B200 | GB200 |
|:-----------------------------|:---|:---------|:--------------|
| | TP Size | 4 | 4 |
| ISL, OSL | | | |
| | | | |
| 128, 128 | | 6,218.89 | 6,598.97 |
| 128, 2048 | | 7,178.10 | 7,497.40 |
| 128, 4096 | | 5,890.89 | 5,898.19 |
| 500, 2000 | | 5,844.37 | 6,198.33 |
| 1000, 1000 | | 4,958.53 | 5,243.35 |
| 1000, 2000 | | 4,874.16 | 4,905.51 |
| 1024, 2048 | | 4,833.19 | 4,686.38 |
| 2048, 128 | | 737.95 | 761.58 |
| 2048, 2048 | | 4,024.02 | 4,326.56 |
| 5000, 500 | | 1,032.40 | 1,078.87 |
| 20000, 2000 | | 667.39 | 649.95 |
### FP8 Models:
```
### FP8 Models
```text
nvidia/Llama-3.1-8B-Instruct-FP8
nvidia/Llama-3.3-70B-Instruct-FP8
nvidia/Llama-3.1-405B-Instruct-FP8
@ -73,61 +89,65 @@ nvidia/Llama-4-Maverick-17B-128E-Instruct-FP8
#### Llama 3.1 8B FP8
| | GPU | H200 141GB HBM3 | H100 80GB HBM3 |
|:-----------------------------|:---|:------------------|:-----------------|
| | TP Size | 1 | 1 |
| ISL, OSL | | | |
| | | | |
| 128, 128 | | 27,970.14 | 27,688.36 |
| 128, 2048 | | 23,326.38 | 21,841.15 |
| 128, 4096 | | 17,508.51 | 13,730.89 |
| 500, 2000 | | 21,390.41 | 17,833.34 |
| 1000, 1000 | | 17,366.89 | 15,270.62 |
| 1000, 2000 | | 16,831.31 | 13,798.08 |
| 1024, 2048 | | 16,737.03 | 13,385.50 |
| 2048, 128 | | 3,488.03 | 3,414.67 |
| 5000, 500 | | 3,813.69 | 3,394.54 |
| 20000, 2000 | | 1,696.66 | 1,345.42 |
| | GPU: | GH200 | H100 | H200 |
|:-----------------------------|:---|:--------------|:-----------------|:------------------|
| | TP Size | 1 | 1 | 1 |
| ISL, OSL | | | | |
| | | | | |
| 128, 128 | | 27,304.25 | 26,401.48 | 27,027.80 |
| 128, 2048 | | 24,045.60 | 21,413.21 | 23,102.25 |
| 128, 4096 | | 15,409.85 | 13,541.54 | 17,396.83 |
| 500, 2000 | | 20,123.88 | 17,571.01 | 19,759.16 |
| 1000, 1000 | | 16,352.99 | 14,991.62 | 17,162.49 |
| 1000, 2000 | | 15,705.82 | 13,505.23 | 16,227.11 |
| 1024, 2048 | | 16,102.52 | 13,165.91 | 16,057.66 |
| 2048, 128 | | 3,573.85 | 3,275.55 | 3,390.69 |
| 2048, 2048 | | 10,767.05 | 9,462.43 | 11,822.14 |
| 5000, 500 | | 3,584.74 | 3,276.47 | 3,758.08 |
| 20000, 2000 | | 1,393.31 | 1,340.69 | 1,705.68 |
#### Llama 3.3 70B FP8
| | GPU | H200 141GB HBM3 | | | | H100 80GB HBM3 | | | |
|:-----------------------------|:---|:------------------|:---------|:----------|:----------|:-----------------|:---------|:----------|:----------|
| | TP Size | 1 | 2 | 4 | 8 | 1 | 2 | 4 | 8 |
| ISL, OSL | | | | | | | | | |
| | | | | | | | | | |
| 128, 128 | | 3,605.47 | 6,427.69 | 10,407.42 | 15,434.37 | 3,128.33 | 6,216.91 | | |
| 128, 2048 | | 4,315.80 | 8,464.03 | 13,508.59 | 20,759.72 | 756.42 | 5,782.57 | 11,464.94 | 17,424.32 |
| 128, 4096 | | 2,701.17 | 5,573.55 | 11,458.56 | 16,668.75 | | 3,868.37 | 8,206.39 | 12,624.61 |
| 500, 2000 | | 3,478.76 | 6,740.06 | 12,200.18 | | | 4,684.06 | 9,903.53 | 14,553.93 |
| 1000, 1000 | | 2,744.32 | 5,119.72 | 8,685.44 | 12,744.51 | 742.14 | 4,247.19 | 7,435.65 | 11,018.81 |
| 1000, 2000 | | 2,896.44 | 5,847.26 | 9,031.21 | 13,141.17 | 533.74 | 3,866.53 | 7,611.12 | 11,139.22 |
| 1024, 2048 | | 2,874.18 | 5,568.61 | 8,946.71 | 13,082.62 | 530.16 | 3,796.68 | 7,575.24 | 11,004.31 |
| 2048, 128 | | 435.90 | 772.67 | 1,264.76 | | | 736.89 | 1,213.33 | 1,839.22 |
| 2048, 2048 | | | | | 10,412.85 | | | | |
| 5000, 500 | | 545.96 | 997.15 | 1,698.22 | 2,655.28 | 204.94 | 862.91 | 1,552.68 | 2,369.84 |
| 20000, 2000 | | 276.66 | 620.33 | 1,161.29 | 1,985.85 | | 416.13 | 903.66 | 1,554.10 |
| | GPU: | H100 | H200 |
|:-----------------------------|:---|:-----------------|:------------------|
| | TP Size | 2 | 2 |
| ISL, OSL | | | |
| | | | |
| 128, 128 | | 6,092.28 | 6,327.98 |
| 128, 2048 | | 5,892.94 | 7,467.36 |
| 128, 4096 | | 3,828.46 | 5,526.42 |
| 500, 2000 | | 4,654.74 | 6,639.15 |
| 1000, 1000 | | 4,181.06 | 4,773.33 |
| 1000, 2000 | | 3,708.93 | 5,790.36 |
| 1024, 2048 | | 3,785.04 | 5,480.44 |
| 2048, 128 | | 723.40 | 747.55 |
| 2048, 2048 | | 2,785.53 | 3,775.80 |
| 5000, 500 | | 865.55 | 978.28 |
| 20000, 2000 | | 411.85 | 609.42 |
#### Llama 3.1 405B FP8
| | GPU | H200 141GB HBM3 | H100 80GB HBM3 |
|:-----------------------------|:---|:------------------|:-----------------|
| | TP Size | 8 | 8 |
| ISL, OSL | | | |
| | | | |
| 128, 2048 | | 5,567.87 | |
| 128, 4096 | | 5,136.85 | |
| 500, 2000 | | 4,787.61 | 3,673.91 |
| 1000, 1000 | | 3,286.30 | 3,012.22 |
| 1000, 2000 | | 3,636.76 | 3,262.20 |
| 1024, 2048 | | 3,618.66 | 3,109.70 |
| 2048, 128 | | 443.10 | 449.02 |
| 5000, 500 | | 645.46 | |
| 20000, 2000 | | | 372.12 |
| | GPU: | H100 | H200 |
|:-----------------------------|:---|:-----------------|:------------------|
| | TP Size | 8 | 8 |
| Runtime Input/Output Lengths | | | |
| | | | |
| 128, 128 | | | 3,705.18 |
| 128, 2048 | | 4,517.39 | 4,715.13 |
| 128, 4096 | | 2,910.31 | 4,475.91 |
| 500, 2000 | | 3,664.62 | 4,804.10 |
| 1000, 1000 | | 2,955.50 | 3,208.25 |
| 1000, 2000 | | 2,884.69 | 3,630.29 |
| 1024, 2048 | | 3,237.41 | 3,609.50 |
| 2048, 128 | | 433.47 | 441.35 |
| 2048, 2048 | | 2,216.55 | 2,840.86 |
| 5000, 500 | | 579.05 | 645.26 |
| 20000, 2000 | | 363.27 | 509.87 |
#### Llama 4 Maverick FP8
| | GPU | H200 141GB HBM3 | H100 80GB HBM3 |
Note: Performance for Llama 4 on sequence lengths less than 8,192 tokens is affected by an issue introduced in v0.21. To reproduce the Llama 4 performance noted here, please use v0.20
| | GPU | H200 | H100 |
|:-----------------------------|:---|:------------------|:-----------------|
| | TP Size | 8 | 8 |
| ISL, OSL | | | |
@ -140,7 +160,6 @@ nvidia/Llama-4-Maverick-17B-128E-Instruct-FP8
| 2048, 128 | | 4,364.06 | 3,832.38 |
| 2048, 2048 | | 12,800.89 | |
| 5000, 500 | | 5,128.60 | |
| 20000, 2000 | | 1,764.27 | 1,400.79 |
## Reproducing Benchmarked Results
@ -216,7 +235,7 @@ a model name (HuggingFace reference or path to a local model), a [generated data
trtllm-bench --model $model_name throughput --dataset $dataset_file --backend pytorch --extra_llm_api_options $llm_options
```
The data collected for the v0.20 benchmarks was run with the following file:
The data collected for the v0.21 benchmarks was run with the following file:
`llm_options.yml`
```yaml
@ -240,7 +259,7 @@ cuda_graph_batch_sizes:
- 8192
```
In a majority of cases, we also use a higher KV cache percentage by setting `--kv_cache_free_gpu_mem_fraction 0.95` in the benchmark command. This allows us to obtain better performance than the default setting of `0.90`. We fall back to `0.90` if we hit an out of memory issue.
In many cases, we also use a higher KV cache percentage by setting `--kv_cache_free_gpu_mem_fraction 0.95` in the benchmark command. This allows us to obtain better performance than the default setting of `0.90`. We fall back to `0.90` or lower if out-of-memory errors are encountered.
The results will be printed to the terminal upon benchmark completion. For example,