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What to measure before tuning inference
Start with the workload you need to serve, not a best-case synthetic request. Record a baseline before changing precision or batching so you can tell whether a change improved the outcome that matters.
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- Throughput: tokens or requests completed per second, with concurrency and request mix stated.
- Latency: define whether you measure time to first token, per-token latency, end-to-end latency, or more than one. Compare against the actual service-level objective (SLO).
- Memory: peak device memory, including model weights and the key-value (KV) cache at the context lengths and batch sizes you expect.
- Output quality: task accuracy or an evaluation score compared with the original model and serving path.
- Compatibility and operations: supported model operations, precision kernels, hardware, runtime and engine versions, plus the work needed for calibration, compilation, warm-up, or fine-tuning.
Keep the model and version, hardware, software stack, input and output lengths, batch policy, warm-up method, request concurrency, and measurement window with every result. That context is necessary to make comparisons meaningful.
Set the limits that optimization must respect
Before testing, define a quality floor, an end-to-end latency objective, a throughput target, and the device-memory budget. These constraints determine what counts as an improvement: a faster configuration is not useful if it misses the latency target or damages task quality.
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How quantization changes inference
Quantization represents some model values at lower numerical precision. Depending on the model, engine, hardware, and available kernels, it may reduce memory pressure, make a larger batch possible, or speed inference. It may also reduce output quality, and it does not improve speed on every hardware configuration.
Formats and approaches to investigate can include INT8, INT4 weight-only quantization, FP8, and BF16 or FP16 compute paths. Availability depends on the full deployment path; a format supported by a GPU does not necessarily mean the chosen model and runtime can use it efficiently. PyTorch Serve’s Model Inference Optimization Checklist describes quantization as a potential optimization and cautions that accuracy can fall and speed gains are not assured.
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Compare supported precision choices on your workload
Use only formats supported by the model operations, kernels, hardware, and inference engine you plan to deploy. Evaluate quality alongside speed and memory rather than treating a lower bit width as automatically better. PyTorch Serve lists dynamic quantization, static quantization, and quantization-aware training as approaches to explore, particularly for CPU inference; their suitability depends on the deployment.
Consider quantization-aware training if post-training quality is inadequate
If post-training quantization falls below your quality floor, quantization-aware training (QAT) is one possible mitigation when a training or fine-tuning workflow is feasible. QAT adapts weights toward the representation used after quantization, but it adds training work and is not simply an inference-time switch. A 2026 TorchAO article reports an INT4 QAT result with 1.73× inference speedup versus BF16 and a prototype NVFP4 QAT result with 1.35× speedup on B200 GPUs; these are integration-specific results, not forecasts for other models or systems. See Quantization-Aware Training in TorchAO (II).
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How batching affects throughput, latency, and memory
Batching processes multiple inputs together and can improve throughput. Larger batches are not automatically better: they can increase latency and memory use, and may exceed the device budget. PyTorch Serve recommends trying larger batches while meeting the latency service-level objective, rather than maximizing batch size in isolation. Its inference checklist provides general guidance, not a model-specific performance guarantee.
Use dynamic batching when requests can wait briefly
Dynamic batching combines incoming requests at serving time. It can improve throughput when the service can hold requests briefly to form a batch, but that queueing delay counts against the latency budget. Test it under representative arrival patterns and concurrency, not only with a pre-filled batch.
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Bucket variable-length sequences
When input lengths vary, batching similarly sized sequences can reduce padding—the computation spent on empty positions added to make a batch uniform. PyTorch Serve says sequence bucketing could potentially improve throughput by up to 2× for variable-length sequences. Treat that as a possible result described by the checklist, not a typical or guaranteed gain; measure it using your actual request-length distribution.
Production serving also requires more than compiling a model. A PyTorch and IBM Research article notes that dynamic batching and warm-up for bucketized sequence lengths are needed in the production-serving path it describes. Its reported 29 ms/token for Llama 2 70B on eight NVIDIA A100 GPUs—2.4× better than that article’s unoptimized baseline—came from compilation, scaled dot-product attention (SDPA), and tensor parallelism. The authors identify quantization as an acceleration lever but did not use it for that result. See PyTorch compile to speed up inference on Llama 2.
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A practical tuning workflow
- Measure the baseline. Run representative prompts or inputs at realistic concurrency. Capture throughput, defined latency measures, peak memory, and task quality, along with the model, hardware, software, request lengths, batch policy, warm-up, and measurement window.
- Write down acceptance limits. Set the minimum acceptable quality, latency SLO, throughput goal, and memory ceiling before changing the configuration.
- Test compatible precision formats. Compare supported formats against the baseline. For each, measure speed and memory as well as quality; reject configurations that fail a limit even if one metric improves.
- Sweep batch sizes. Increase batch size in steps and track both throughput and latency. Stop when the latency objective or memory budget is breached, or when further batching no longer helps the target workload.
- Test length-aware batching where relevant. Compare ordinary batching with sequence bucketing using the production request-length distribution. Include any queueing delay from dynamic batching in latency.
- Benchmark combinations. Test the selected precision and batching settings together. Their effects can interact, so separate wins do not prove that the combined configuration will win.
- Repeat in the production serving path. Use the intended engine and runtime, including warm-up and request handling. Keep the change only if the combined result meets quality, latency, throughput, and memory requirements.
What published benchmarks can—and cannot—tell you
Published results are useful examples of how configuration affects performance, but they are not transferable guarantees. In a 2025 report, the PyTorch, Mobius Labs, and SGLang teams measured Llama 3.1-8B decode on an 8×H100 machine. Their reported throughput figures below are tied to that setup; the BF16 compiled baseline is shown for comparison.
| Configuration | Batch size and tensor parallelism | Reported throughput |
|---|---|---|
| INT4 weight-only vs. BF16 compiled baseline | 1; TP 1 | 255 vs. 131 tokens/sec |
| INT4 weight-only vs. BF16 compiled baseline | 32; TP 1 | 3,241 vs. 2,799 tokens/sec |
| INT4 weight-only vs. BF16 compiled baseline | 32; TP 4 | 6,334 vs. 5,575 tokens/sec |
| FP8 dynamic quantization vs. BF16 compiled baseline | 1; TP 1 | 166 vs. 131 tokens/sec |
| FP8 dynamic quantization vs. BF16 compiled baseline | 32; TP 1 | 3,586 vs. 2,799 tokens/sec |
| FP8 dynamic quantization vs. BF16 compiled baseline | 32; TP 4 | 6,159 vs. 5,575 tokens/sec |
These results vary by precision, batch size, and tensor-parallel configuration; they do not establish which choice will be faster for another model, machine, or serving workload. The authors also note that quantization may affect accuracy. Read the full report, Accelerating LLM Inference with GemLite, TorchAO and SGLang, for its experimental context.
Choose an inference engine that fits the deployment
Engine support is part of the optimization, not an afterthought. NVIDIA describes TensorRT as an inference optimization SDK for NVIDIA GPUs, with support for multiple precision formats and dynamic shapes. Verify current hardware, operator, and precision support in the TensorRT documentation and support matrix, then benchmark the actual model and request mix. Support and capabilities can change over time, so do not assume a format or optimization is available for every deployment.
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Keep a configuration only when its measured result clears every requirement you set: output quality, latency, throughput, and memory. Compare like with like, document the exact serving setup, and evaluate precision and batching together. There is no universal winning format or batch size without a specific model, device, request distribution, and service objective.
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