To make AI inference more efficient, measure a representative baseline, then test supported precision formats and batch sizes against your quality, latency, throughput, and memory requirements. Quantization can reduce memory use and sometimes improve speed; batching can raise throughput but may increase latency and memory use. Neither is a universal win, so keep an optimization only if it meets your service objectives on your actual model and serving stack.
What to measure before tuning inference
Start with the workload users actually send, rather than an idealized prompt or a single short request. Record enough detail to reproduce the result and compare configurations fairly.
- Model: name, version, weights, and any model-specific settings.
- Hardware and software: accelerator or CPU, runtime, serving engine, kernel/library versions, and parallelism settings.
- Request mix: representative input and output lengths, concurrency, and the distribution of sequence lengths.
- Serving policy: batch size or dynamic batching policy, sequence bucketing, and warm-up method.
- Measurements: throughput in requests or tokens per second, time to first token, per-token latency, end-to-end latency, and peak device memory. State the measurement window and how latency is calculated.
- Quality: task accuracy or an evaluation score compared with the unoptimized baseline.
Define the quality floor, end-to-end latency objective, throughput target, and device-memory budget before changing settings. Otherwise, a faster configuration can appear successful while violating the requirement that matters most.
How quantization changes the tradeoffs
Quantization represents some model values at lower numerical precision. Depending on the model, hardware, kernels, and inference engine, lower precision may reduce memory pressure, make room for a larger batch, or improve speed. It can also change output quality, and some hardware configurations may show little or no speed improvement. PyTorch Serve describes quantization as a potential optimization, not a guaranteed speedup: Model Inference Optimization Checklist.
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Common formats and paths in the sources include INT8 and INT4 weight-only quantization, FP8, and BF16 or FP16 compute. These are not interchangeable settings: compatibility depends on the model operations, supported kernels, hardware, runtime, and engine. For NVIDIA GPU deployments, TensorRT is an inference optimization SDK that supports multiple precision formats and dynamic shapes; check its current documentation and support matrix for the exact deployment path: NVIDIA TensorRT Documentation.
Compare formats on both quality and performance
Test the formats your actual stack supports, and evaluate each on the same representative inputs. Include task quality, memory, throughput, and latency in the comparison. PyTorch Serve lists dynamic quantization, static quantization, and quantization-aware training among options to explore, particularly for CPU inference, while warning that quantization may reduce accuracy and may not deliver meaningful speed gains on all hardware (PyTorch Serve).
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If post-training quantization takes quality below your acceptable floor, quantization-aware training (QAT) is one possible mitigation. It adds a fine-tuning or training step that adapts weights toward the representation used after quantization; it is not simply an inference-time switch. The results reported for TorchAO QAT are tied to the integrations and experiments in that article: Quantization-Aware Training in TorchAO (II).
How to balance batching, throughput, and latency
Batching processes multiple inputs together and can improve throughput. Larger batches can also consume more memory and make individual requests wait longer, so the useful batch size is the largest one that still meets the latency objective and memory budget for the real request mix. PyTorch Serve advises trying larger batches while meeting the latency SLA, rather than treating batch size as a goal in itself (Model Inference Optimization Checklist).
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Use dynamic batching when requests can wait briefly
Dynamic batching combines incoming requests at serving time. It can increase processing efficiency when requests arrive independently, but the time spent waiting for a batch counts toward user-visible latency. Tune the batching policy under realistic arrival rates and concurrency, and check end-to-end latency rather than measuring only model execution.
Bucket variable-length sequences
When requests have different sequence lengths, batching them together can waste computation and memory 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× in the described variable-length sequence case; that is a potential result, not a guaranteed gain for every model or request distribution (PyTorch Serve).
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Evaluate buckets using the actual distribution of input and output lengths. Too many or poorly chosen buckets can complicate serving and warm-up; too few may leave substantial padding. Production configuration matters: a PyTorch and IBM Research article notes that compilation alone is not sufficient for production serving and describes dynamic batching and warm-up for bucketized sequence lengths as part of its high-throughput path (PyTorch compile to speed up inference on Llama 2).
A practical tuning workflow
- Establish a reproducible baseline. Run the current model and serving stack on representative inputs and request concurrency. Record the hardware, software versions, input/output lengths, batch policy, warm-up, measurement window, quality, latency, throughput, and memory.
- Set pass/fail constraints. Write down the minimum acceptable task quality, latency objective, throughput goal, and available device memory before comparing alternatives.
- Test compatible precision options. Change precision while holding the workload and other settings steady. Measure quality as well as speed and memory; reject any option that breaks a requirement.
- Sweep batch sizes. Increase batch size in controlled steps and track throughput, end-to-end latency, and peak memory. Stop when the latency objective or memory budget is exceeded.
- Test bucketing for variable lengths. Compare ordinary batching with sequence buckets sized for the real request-length distribution. Include the serving policy and any required warm-up in the evaluation.
- Benchmark combinations. Test the chosen precision and batching settings together. Their effects can interact, so a gain from either change in isolation does not establish that the combined configuration is better.
- Repeat in the production path. Use the intended serving engine, request mix, concurrency, and warm-up behavior. Keep a configuration only when it meets every required quality, latency, throughput, and memory threshold.
What published benchmarks can—and cannot—tell you
Published measurements are useful for identifying configurations worth testing, but their results belong to the reported model, hardware, and setup. For example, PyTorch, Mobius Labs, and SGLang teams reported the following Llama 3.1-8B decode throughput measurements on an 8×H100 machine in 2025. The BF16 compiled figures are the reported baseline for each matching batch and tensor-parallel setting; they are not general forecasts for other deployments (Accelerating LLM Inference with GemLite, TorchAO and SGLang).
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| Precision/configuration | Batch size | Tensor parallel size | Reported throughput |
|---|---|---|---|
| INT4 weight-only | 1 | 1 | 255 tokens/sec; BF16 compiled baseline: 131 tokens/sec |
| INT4 weight-only | 32 | 1 | 3,241 tokens/sec; BF16 compiled baseline: 2,799 tokens/sec |
| INT4 weight-only | 32 | 4 | 6,334 tokens/sec; BF16 compiled baseline: 5,575 tokens/sec |
| FP8 dynamic quantization | 1 | 1 | 166 tokens/sec; BF16 compiled baseline: 131 tokens/sec |
| FP8 dynamic quantization | 32 | 1 | 3,586 tokens/sec; BF16 compiled baseline: 2,799 tokens/sec |
| FP8 dynamic quantization | 32 | 4 | 6,159 tokens/sec; BF16 compiled baseline: 5,575 tokens/sec |
The reported gains vary with batch size and tensor-parallel configuration. They do not establish the same outcome for a different model, machine, request mix, or runtime, and the article warns that quantization may affect accuracy.
A separate PyTorch and IBM Research report measured 29 ms/token for Llama 2 70B on 8 NVIDIA A100 GPUs, describing it as 2.4× better than that article’s unoptimized inference baseline. Its path used compilation, scaled dot product attention (SDPA), and tensor parallelism; the reported figure should not be attributed to quantization or batching (PyTorch compile to speed up inference on Llama 2).
TorchAO’s 2026 QAT 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 figures describe the integrations and experiments in that article, not expected results for arbitrary models or deployments (Quantization-Aware Training in TorchAO (II)).
Choose the configuration that meets your service goals
There is no universal best precision or batch size. Compare candidates on output quality, throughput, defined latency measures, peak memory, compatibility, and operational complexity. Compatibility includes model operations, precision kernels, hardware, runtime, and engine versions; complexity can include calibration or fine-tuning, compilation, warm-up, and serving configuration. The right choice is the one that clears your requirements on the model and request distribution you actually serve.
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