Serving engine
TensorRT-LLM: what RunInfra measured
We measured throughput and TTFT for Llama 3.1 8B Instruct with TensorRT-LLM 1.2.1 on the NVIDIA H100 80GB at BF16, as of June 20, 2026. Derived cost configurations reference NVIDIA H100 80GB and NVIDIA L40S 48GB. Source absences render below.
0 published packages serve on this engine. Package rows keep their own engine versions and verified dates.
TensorRT-LLM's PyTorch-backend run makes backend scope part of every throughput and latency reading.
Source article and methodThe comparison evidence stays inside one measured scope
Measured throughput and TTFT remain separate from derived cost. Every comparison ratio shows both source values and its full condition.
| Concurrency | Measured throughput | Measured TTFT p50 | Full condition |
|---|---|---|---|
| 1 | 152 output tokens per second | 32 milliseconds | NVIDIA H100 80GB, BF16, concurrency 1, Llama 3.1 8B Instruct, as of June 20, 2026 |
| 8 | 1,027 output tokens per second | 56 milliseconds | NVIDIA H100 80GB, BF16, concurrency 8, Llama 3.1 8B Instruct, as of June 20, 2026 |
| 32 | 2,978 output tokens per second | 235 milliseconds | NVIDIA H100 80GB, BF16, concurrency 32, Llama 3.1 8B Instruct, as of June 20, 2026 |
| 64 | 4,029 output tokens per second | 504 milliseconds | NVIDIA H100 80GB, BF16, concurrency 64, Llama 3.1 8B Instruct, as of June 20, 2026 |
| 128 | 4,774 output tokens per second | 1,691 milliseconds | NVIDIA H100 80GB, BF16, concurrency 128, Llama 3.1 8B Instruct, as of June 20, 2026 |
| 256 | 4,813 output tokens per second | 2,848 milliseconds | NVIDIA H100 80GB, BF16, concurrency 256, Llama 3.1 8B Instruct, as of June 20, 2026 |
Pairwise readings at the highest measured concurrency
We measured higher output throughput for vLLM. vLLM recorded 5,333 output tokens per second versus TensorRT-LLM at 4,813 output tokens per second, under NVIDIA H100 80GB, BF16, concurrency 256, Llama 3.1 8B Instruct, as of June 20, 2026. The render-derived ratio is 1.11x.derived at render time from the two measured throughput values at concurrency 256, output tokens per second, mean of three timed repeats after warmup, unique prompts with prefix caching off
We measured lower TTFT p50 for vLLM. vLLM recorded 2,221 milliseconds versus TensorRT-LLM at 2,848 milliseconds, under NVIDIA H100 80GB, BF16, concurrency 256, Llama 3.1 8B Instruct, as of June 20, 2026. The render-derived ratio is 1.28x.derived at render time from the two measured latency values at concurrency 256, p50 time to the first streamed token, charted as the mean of three timed repeats after warmup, same request stream as the throughput column
We measured higher output throughput for SGLang. SGLang recorded 5,235 output tokens per second versus TensorRT-LLM at 4,813 output tokens per second, under NVIDIA H100 80GB, BF16, concurrency 256, Llama 3.1 8B Instruct, as of June 20, 2026. The render-derived ratio is 1.09x.derived at render time from the two measured throughput values at concurrency 256, output tokens per second, mean of three timed repeats after warmup, unique prompts with prefix caching off
We measured lower TTFT p50 for SGLang. SGLang recorded 2,543 milliseconds versus TensorRT-LLM at 2,848 milliseconds, under NVIDIA H100 80GB, BF16, concurrency 256, Llama 3.1 8B Instruct, as of June 20, 2026. The render-derived ratio is 1.12x.derived at render time from the two measured latency values at concurrency 256, p50 time to the first streamed token, charted as the mean of three timed repeats after warmup, same request stream as the throughput column
Derived cost stays on its own basis
| Configuration | Published value or absence | Full condition |
|---|---|---|
| NVIDIA H100 80GB BF16 | $0.228 USD per 1M output tokens, derived | NVIDIA H100 80GB, BF16, source-selected cost concurrency 256, Llama 3.1 8B Instruct, as of June 20, 2026 |
| NVIDIA H100 80GB FP8 | TensorRT-LLM at fp8 was not measured. The cost block publishes a single TensorRT-LLM configuration, bf16 on the H100. | NVIDIA H100 80GB, FP8, source-selected cost concurrency 256, Llama 3.1 8B Instruct, as of June 20, 2026 |
| NVIDIA L40S 48GB BF16 | TensorRT-LLM on the L40S was not measured. The cost block publishes a single TensorRT-LLM configuration, bf16 on the H100. | NVIDIA L40S 48GB, BF16, source-selected cost concurrency 128, Llama 3.1 8B Instruct, as of June 20, 2026 |
Prefix-cache evidence depends on the workload
No TensorRT-LLM prefix-cache result. Both prefix experiments ran vLLM and SGLang only.
One client drives all three engines with identical request streams, so the timing definitions are the same everywhere. Time to first token is the time to the first streamed token. Throughput is output tokens per second. Warm up, then three timed repeats per operating point, charted as the mean; the 95 percent confidence intervals are tight and live in the committed raw data. Prefix caching is off for the unique-prompt sweeps. Concurrency swept from 1 to 256 at 1,024 input and 256 output tokens, unique prompts, prefix caching off. Same weights and same request stream across engines, single GPU, tensor-parallel size 1. 0 published package rows retain their recorded engine version, concurrency, and verified date.
We report package measurements here, and each package remains subject to its listed license.
Citation: RunInfra (2026). Measured open-model serving benchmarks. https://runinfra.ai/catalog.
Published packages keep their own measured conditions
These rows come from the published catalog, not the comparison sweep. Each package retains its own model, engine version, concurrency, and verified date.
No published package currently serves on this engine.
The limitations bound every result
- We benchmarked TensorRT-LLM on its PyTorch backend, which has no ahead-of-time engine compile, so its peak throughput here is not the ceiling a compiled TensorRT engine would reach. And our single-prefix cache test is not the many-distinct-prefix case SGLang's RadixAttention is designed for, so we ran that separately too. We did not let either feed the headline.
- One H100 80GB and one L40S 48GB, single GPU, tensor-parallel size 1.
- Same Llama-3.1-8B-Instruct weights and same request stream across engines. Three timed repeats; the 95 percent confidence intervals are tight and live in the committed raw data.
- Latency here is TTFT and per-request percentiles, not goodput at a fixed SLO. Whether 514 ms first-token at high load is acceptable depends on your use case; an SLO-goodput sweep is future work.
- Numbers are self-reported on our harness. We do not cross-calibrate against an external suite like MLPerf, so the open harness is the check: re-run it and compare.
- TensorRT-LLM ran the PyTorch backend (no engine compile), so there is no compile time to report and its peak number may differ from a built TensorRT engine.
- So the 4,813 tok/s we measured is the PyTorch-backend peak, not TensorRT-LLM's ceiling, and we got no compile-time number.
- The L40S sweep stopped at concurrency 128 (its saturation), the H100 at 256.
- Engines move weekly. These numbers are vLLM 0.23.0, SGLang 0.5.13, TensorRT-LLM 1.2.1, as of June 20 2026.
What is not measured
- No other model is measured in this comparison dataset. It covers Llama 3.1 8B Instruct.
- No GPU outside NVIDIA H100 80GB and NVIDIA L40S 48GB is represented in the dataset.
- No engine version newer than TensorRT-LLM 1.2.1 is measured for this page's comparison evidence.
- No fp8 throughput and no fp8 latency. The source publishes fp8 only as a derived cost per 1M output tokens at saturation, so no fp8 tokens-per-second, first-token latency, or speedup may be rendered from this dataset.
- No L40S throughput and no L40S latency. The L40S appears only as a derived cost cell. Every sweep row here is the H100 at bf16.
- No TensorRT-LLM prefix-cache result. Both prefix experiments ran vLLM and SGLang only.
- No compiled TensorRT engine. Every TensorRT-LLM number here is its PyTorch backend, which the source states is not TensorRT-LLM's throughput ceiling, and no engine compile time was recorded.
- No prefix-cache latency values. The source states one engine had lower first-token latency on the many-prefix workload but publishes no latency numbers for either prefix experiment, so that comparison stays qualitative.
- No hardware or precision label for the single-shared-prefix sweep. Its caption names only the concurrency, so those rows must not be presented under a GPU or a precision the source does not state.
- No goodput at a fixed service-level objective, and no accuracy or output-quality comparison between engines. This dataset is throughput, first-token latency, and derived cost only.
- No cross-engine or cross-precision ratio is stored. Pages derive every multiplier and percentage from the absolute pair at render time and print both absolute values beside it.
- Not cross-calibrated against an external benchmark suite. These are self-reported numbers from an open harness, and the harness is the check.
- No claim that these results still hold. The dataset is pinned to one as-of date and one engine version triple, and engines change on a weekly cadence.
The same evidence answers common engine questions
- How did TensorRT-LLM compare with vLLM on throughput at the highest measured concurrency?
- We measured higher output throughput for vLLM. TensorRT-LLM recorded 4,813 output tokens per second; vLLM recorded 5,333 output tokens per second. Condition: NVIDIA H100 80GB, BF16, concurrency 256, Llama 3.1 8B Instruct, as of June 20, 2026.
- How did TensorRT-LLM compare with SGLang on TTFT p50 at the highest measured concurrency?
- We measured lower TTFT p50 for SGLang. TensorRT-LLM recorded 2,848 milliseconds; SGLang recorded 2,543 milliseconds. Condition: NVIDIA H100 80GB, BF16, concurrency 256, Llama 3.1 8B Instruct, as of June 20, 2026.
- How did TensorRT-LLM compare with vLLM on derived cost in the shared published configuration?
- We derived lower cost for vLLM. TensorRT-LLM was $0.228; vLLM was $0.206 USD per 1M output tokens. Condition: NVIDIA H100 80GB, BF16, source-selected cost concurrency 256, Llama 3.1 8B Instruct, as of June 20, 2026. Both values are derived from the recorded GPU rate and measured saturation throughput.
If you need custom optimization for a specific model, describe what you need
End-to-end encryption
Isolated GPU infrastructure
No training on your data
SOC 2 Type II
RunInfraby RightNow© 2026 RunInfra. All rights reserved.
Ask AI about RunInfra
