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What should you measure before tuning?
Start by defining the workload your deployment actually needs to serve. A benchmark with short prompts and one request at a time may tell you little about a service handling long contexts or concurrent users.
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- Model: Record the model and architecture.
- Input and output: Specify prompt length and generated-token length, including the mix of short and long requests if relevant.
- Load: Record batch size or concurrency and whether it changes during the test.
- Outcome: Choose the metric that matters: time to first token, per-token latency, or throughput. Report which metric you used rather than treating them as interchangeable.
- Environment: Note GPU model and memory, framework and runtime versions, precision or quantization, and the attention and matrix-multiplication backends.
Run a repeatable baseline before changing kernels or serving settings. Keep the software and hardware conditions consistent, and investigate unstable clocks or throttling before interpreting a result: they can distort both measurements and tactic selection. NVIDIA’s TensorRT performance benchmarking guide covers timing methods, profiling, and environment controls.
How do you find the bottleneck?
Begin with a broad timeline, not a guess about which kernel is slow. The first question is whether elapsed time is dominated by CPU work, dispatch and launch overhead, GPU execution, or data movement. A system-level view helps distinguish those cases before you spend time on instruction-level analysis.
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On CUDA
Use Nsight Systems to inspect CUDA activity and transfer timelines, then narrow the investigation to TensorRT layers or individual CUDA kernels where the evidence points. NVIDIA’s benchmarking documentation also describes TensorRT profiling and trtexec as part of its measurement toolkit.
On ROCm
AMD recommends starting with system-level profiling using rocprofiler-systems. If that timeline identifies a kernel bottleneck, move to rocprofiler-compute for kernel-level utilization and compute-versus-memory analysis. Tools including rocprofv3 and ROCprof Compute Viewer support deeper investigation when warranted. AMD’s ROCm profiler-selection guide explains how to choose the level of analysis.
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Which CUDA settings are worth testing?
For NVIDIA inference, TensorRT and TensorRT-LLM provide optimization paths, but the useful settings depend on the model, shapes, GPU, and build configuration. NVIDIA’s TensorRT performance guide emphasizes measuring first and discusses several tunable areas:
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- CUDA graphs and streams: Test whether graph use or multi-streaming helps your workload. Their effects depend on execution and workload characteristics; validate with the same benchmark rather than assuming a gain.
- Fusion and alignment: Layer fusion can reduce overhead, while Tensor Core performance depends in part on alignment. Check the built engine and measured workload rather than applying these ideas in isolation.
- Precision and tactics: Precision choices trade performance against numerical behavior. Tactic selection can affect the built engine. Preserve output correctness and task quality when changing precision, and benchmark the resulting engine.
- Build time: Timing caches can reduce repeated profiling work during engine builds. Lowering the builder optimization level can shorten builds at the possible cost of final engine performance.
TensorRT-LLM is documented as a Python API for defining LLMs, building optimized TensorRT engines, and running them through Python and C++ runtimes. Its documentation lists capabilities such as in-flight batching, paged KV cache, quantization options, and multi-GPU and multi-node support. Check the documentation for the specific release you plan to use before relying on a capability.
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How should you tune LLM inference on ROCm?
On ROCm, treat backend selection as model- and GPU-specific. AMD’s vLLM V1 guidance describes selecting attention backends for different model types, enabling AITER on supported paths, and tuning serving parameters to suit latency or throughput goals. It also discusses fallback paths, so an optimization should not be assumed to apply to every model family or installation.
Before testing a backend or tuning flag, verify its compatibility with the intended GPU, model family, and ROCm and vLLM releases in AMD’s vLLM V1 performance optimization guide. Change one relevant setting at a time, check that the model still produces acceptable results, and benchmark it against your baseline.
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AMD’s workload guidance for MI300 and MI350 systems likewise frames optimization as iterative profiling and validation rather than a single setting that works for every workload: MI300 and MI350 workload optimization.
How do CUDA and ROCm optimization approaches differ?
The practical difference is the toolchain and the supported model-specific paths—not a universal performance ranking. The table summarizes the documented starting points; exact support and outcomes depend on hardware, software release, and workload.
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| Area | CUDA | ROCm |
|---|---|---|
| Broad profiling | Nsight Systems for CUDA activity and transfer timelines; see NVIDIA’s benchmarking guide. | rocprofiler-systems as the system-level starting point; see AMD’s tool-selection guide. |
| Kernel and model tuning | Inspect TensorRT layers and CUDA kernels; consider TensorRT optimization options and TensorRT-LLM capabilities supported by the installed release. | Use rocprofiler-compute for kernel analysis; for vLLM V1, evaluate attention backends and AITER only on supported model and GPU paths. |
| What to verify | Engine build settings, tactics, precision, shapes, and runtime behavior. | Model-family and GPU compatibility, backend selection, fallback behavior, and release-specific flags. |
| Cross-platform winner | Not established by the cited vendor documentation as a controlled, general CUDA-versus-ROCm comparison. | |
How can you make the comparison fair?
- Use the same workload: Match model, prompt and output lengths, batch or concurrency, and request mix.
- Record each stack: Disclose GPU and memory, framework and runtime versions, precision or quantization, and attention and GEMM backends for each configuration.
- Measure the same outcome: Compare time to first token with time to first token, or throughput with throughput; report latency and throughput separately if both matter.
- Repeat the runs: Keep conditions stable and note clock or throttling issues that could make a run unreliable.
- Change one variable: Tune batching, graph use, backend, precision, memory layout, or kernel implementation only when profiling gives you a reason to test it. Validate correctness and task quality alongside speed.
- Publish the conditions with the result: A performance number without its workload, hardware, software, and measurement method cannot support a useful general conclusion.
Vendor documentation offers platform-specific guidance and examples, not a substitute for a controlled head-to-head benchmark on your target workload. A result from one model, backend, GPU, or concurrency level should not be generalized to other configurations.
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