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What to measure before you tune inference
Start with a representative baseline, not a single convenient prompt. Record the model and version, hardware, runtime and serving engine, input and output lengths, request concurrency, batch policy, warm-up method, and measurement window. Define how you measure latency: time to first token, per-token latency, and end-to-end response time answer different questions.
Set the constraints that determine whether an optimization is useful:
- Quality floor: the minimum acceptable task accuracy or evaluation score relative to the baseline.
- Latency objective: the service-level objective (SLO) requests must meet, including any batching delay.
- Throughput target: tokens or requests completed per second under a stated request mix and concurrency.
- Memory budget: peak device memory for model weights, activations, and the KV cache at the context lengths and batch sizes you expect.
Compare each configuration on the same workload. A throughput result without its concurrency, input lengths, output lengths, and latency is not enough to predict production behavior.
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How quantization changes the tradeoffs
Quantization represents some model values at lower numerical precision. Depending on the model, kernels, runtime, and hardware, formats such as INT8, INT4 weight-only, or FP8 may reduce memory pressure, improve inference speed, or leave room for a larger batch. Lower precision can also affect output quality, and it does not necessarily make a particular deployment faster. PyTorch Serve’s Model Inference Optimization Checklist describes quantization as a potential optimization while warning about accuracy loss and hardware-dependent speedups.
Test only formats supported by the model operations, kernels, and hardware path you intend to serve. PyTorch Serve lists dynamic quantization, static quantization, and quantization-aware training (QAT) among approaches to explore, particularly for CPU inference. These are alternatives to evaluate, not a ranking in which one bit width is universally best.
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Evaluate quality alongside speed and memory
For every precision option, run the same task-quality evaluation as the baseline, then record throughput, latency, and peak memory. A change that improves tokens per second but falls below the quality floor or violates the latency objective is not a successful optimization.
If post-training quantization reduces quality too much, QAT may be an option when a fine-tuning workflow is feasible. QAT adapts model weights toward the representation used after quantization, but requires training or fine-tuning rather than a simple serving-time switch. TorchAO’s 2026 QAT article reports integration-specific outcomes: a 1.73× inference speedup versus BF16 for an INT4 QAT result, and 1.35× for a prototype NVFP4 QAT result on B200 GPUs. Those figures describe the article’s integrations and experiments, not expected gains for other models or deployments.
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How batching affects throughput and latency
Batching processes multiple inputs together and can improve throughput by using the serving system more efficiently. Larger batches can also consume more memory and make individual requests wait longer. PyTorch Serve’s guidance is to increase batch size only while meeting the latency SLO; the largest possible batch is not automatically the best production setting.
Choose a batch size by testing the SLO
Sweep batch sizes under the request concurrency and input mix you expect in production. For each size, track throughput, latency, and memory. Keep the largest batch that meets the service’s latency and quality requirements only if it also fits the available memory budget.
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Dynamic batching combines incoming requests at serving time. It can improve throughput when requests may wait briefly to form a batch, but that queueing delay counts toward end-to-end latency. PyTorch and IBM’s Llama 2 serving article notes that compilation alone is not sufficient for production serving in its described path: dynamic batching and warm-up for bucketized sequence lengths are also needed to realize high throughput.
Use sequence bucketing for variable-length inputs
When requests contain sequences of different lengths, batching them together can waste work on padding. Sequence bucketing groups inputs of similar lengths to reduce that waste. PyTorch Serve says bucketing could potentially improve throughput by up to 2× for batch processing on variable-length sequences. Treat that as a possible outcome in the documented guidance, not a guarantee for every model or request distribution.
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A practical tuning workflow
- Establish the baseline. Run representative prompts or inputs at realistic concurrency. Log model and version, hardware, software stack, input and output lengths, batching policy, warm-up method, measurement window, quality, throughput, latency, and peak memory.
- Set pass/fail limits. Define the task-quality floor, latency objective, throughput target, and device-memory ceiling before comparing optimizations.
- Test compatible precision options. Compare supported formats on the same workload. Measure quality, throughput, latency, and memory; do not infer a speedup from a lower bit width alone.
- Sweep batch sizes. Increase batch size while checking latency and memory against the production limits. Include dynamic batching if the serving engine supports it, and count any queueing time in end-to-end latency.
- Compare bucketing where lengths vary. Use the real request-length distribution to compare ordinary batching with sequence buckets. Include warm-up for the bucketized lengths in the serving test.
- Benchmark the combined configuration. Precision and batching interact: a quantized model may permit larger batches, but the combined result must be measured rather than assumed from separate tests.
- Repeat in the production path. Test with the actual serving engine, request pattern, and deployment configuration. Keep an optimization only if it meets quality, latency, throughput, and memory requirements in that setting.
What published benchmarks can—and cannot—tell you
Published results show why configuration details matter; they are not forecasts for a different model or machine. A 2025 PyTorch, Mobius Labs, and SGLang report measured Llama 3.1-8B decode on an 8×H100 machine. Its table reports the following tokens-per-second results for the listed precision, batch, and tensor-parallel (TP) configurations:
| Configuration | Batch size 1, TP size 1 | Batch size 32, TP size 1 | Batch size 32, TP size 4 |
|---|---|---|---|
| BF16 compiled baseline | 131 tokens/sec | 2,799 tokens/sec | 5,575 tokens/sec |
| INT4 weight-only | 255 tokens/sec | 3,241 tokens/sec | 6,334 tokens/sec |
| FP8 dynamic quantization | 166 tokens/sec | 3,586 tokens/sec | 6,159 tokens/sec |
The values are reported measurements from that article’s setup, and the relative results vary across batch and TP configurations. The authors also note that quantization may affect accuracy. See Accelerating LLM Inference with GemLite, TorchAO and SGLang for the experiment context.
A separate 2023 PyTorch and IBM Research article reported 29 ms/token for Llama 2 70B on 8 NVIDIA A100 GPUs, described as 2.4× better than its unoptimized inference baseline. That path used compilation, scaled dot-product attention (SDPA), and tensor parallelism; the figure should not be attributed to quantization or batching. The article is a historical experiment, not a current performance guarantee.
Check engine and hardware compatibility
Optimization depends on the complete path: model operations, precision kernels, hardware, runtime, and serving engine all need to support the configuration. NVIDIA describes TensorRT as an inference optimization SDK for NVIDIA GPUs, with support for multiple precision formats and dynamic shapes. Its supported capabilities and platforms can change, so consult the current documentation and support matrix before choosing a deployment path, then benchmark your workload on the intended setup.
No single precision format or batch size is a universal winner. The useful configuration is the one that improves the metric you need without crossing your quality, latency, or memory limits.
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