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The fastest way to raise AI-inference throughput is not to chase a 100% GPU-utilization reading. Define a repeatable workload, measure throughput and latency together, then increase useful parallel work—usually through batching, token-budget tuning, scheduling, and execution overlap—until you reach the highest point that still meets your service-level objectives (SLOs) and memory limits.
The right setting depends on whether you run fixed-shape neural-network inference, autoregressive large-language-model (LLM) serving, or delayed offline jobs. A batch size that helps one model can hurt another, and aggregate tokens per second can rise while each user receives tokens more slowly. Treat optimization as a measured search for the best throughput-latency-memory trade-off.
What GPU utilization should you optimize?
GPU utilization is a symptom, not the objective. A low reading can mean requests are arriving too slowly, batches are too small, the CPU or input pipeline is the bottleneck, or the model has synchronization gaps. A high reading can coexist with excessive queueing, poor time to first token (TTFT), or a nearly full KV cache that causes preemptions.
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| Dimension | Measure | Why it matters |
|---|---|---|
| Aggregate work | Requests per second; output tokens per second; for LLMs, prompt/prefill tokens per second and generation tokens per second | Shows how much useful inference the GPU completes. |
| User responsiveness | TTFT, inter-token latency (ITL or TPOT), end-to-end latency | Captures when a response starts, how smoothly it streams, and when it finishes. |
| Backlog | Waiting and running requests, queue time, pending requests, preemptions | Distinguishes productive load from requests accumulating behind a saturated system. |
| Memory pressure | GPU memory used, KV-cache usage, allocation failures | Shows whether a larger batch or token budget is consuming capacity needed for concurrency. |
| Fairness of service | Per-user output speed and latency percentiles, not only averages | Aggregate throughput can improve while individual users experience slower generation. |
Set limits for these metrics before changing runtime settings. For example, define a maximum p95 TTFT, a p95 end-to-end latency, a minimum per-user generation speed, and a maximum queue time. Then select the highest-throughput configuration that remains inside every limit.
Step 1: Freeze the workload before changing settings
Every comparison must use the same model, inputs, traffic pattern, and software stack. Record:
- Model name and exact checkpoint, including whether it is encoder-only, decoder-only, multimodal, or another architecture.
- Precision and quantization format, such as FP16, INT8, FP8, or another supported mode.
- GPU model and count, host CPU, memory, interconnect, driver, CUDA version, and inference-runtime version.
- Input or prompt-length distribution, output-length distribution, sequence-length limits, and any padding policy.
- Concurrency for closed-loop tests or request-arrival rate for open-loop tests.
- Whether traffic is latency-sensitive online serving or delayed offline processing.
- Warm-up duration, number of measured iterations, random seeds where applicable, and whether compilation or engine-building time is excluded.
Use representative distributions rather than a single convenient prompt length. An LLM that looks efficient with 128-token prompts may behave very differently with 8,000-token contexts and long generations. Keep a copy of the workload definition so a later driver, model, or runtime change can be retested exactly.
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Step 2: Establish a baseline and success criteria
Run the unmodified deployment at several concurrency or arrival-rate levels. Capture averages and percentiles, especially p50, p95, and p99 for latency. Plot throughput against concurrency instead of reporting one utilization percentage.
For LLMs, separate prompt processing from generation. Record prompt/prefill tokens per second, generation tokens per second, TTFT, ITL or TPOT, total latency, and output tokens per second. A system can process prompts rapidly yet generate tokens slowly, or generate efficiently while long prompts create unacceptable TTFT.
NVIDIA’s 2025 benchmark article uses about 50 output tokens per second—roughly 20 milliseconds between tokens—as an illustrative per-user target for its own scenario. It also reports that, in a specific Llama 3.1 8B comparison, an FP16 checkpoint handled about 256 concurrent users at approximately 72 tokens per second per user before violating that 50-token target, while an FP8 checkpoint handled 512 users at approximately 66 tokens per second per user. Those are vendor-reported results for that model, hardware, software, traffic, and constraint; they are not a general capacity promise.
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Step 3: Diagnose the bottleneck before tuning the GPU
Look at metrics together. A short queue and low utilization usually indicate insufficient incoming work or an upstream bottleneck, not a need to increase batch capacity. A growing queue, high memory use, and rising latency indicate capacity pressure. In LLM serving, frequent preemptions or a nearly full KV cache can limit concurrency even when compute utilization is moderate.
- Low utilization, low queue time: check request arrival, CPU preprocessing, data transfer, input decoding, and synchronization. Increase offered load only if the service objective allows it.
- Low utilization, growing queue: inspect CPU or host-side bottlenecks, serialization, network transfer, and scheduling gaps. The GPU may be waiting for work.
- High utilization, acceptable latency: sweep batch or token capacity upward in small steps and stop when throughput flattens or an SLO is crossed.
- High utilization, unacceptable latency: reduce batching delay, token capacity, or concurrency; separate latency-sensitive traffic; or add GPU capacity.
- KV-cache pressure or preemptions: reduce the active token budget, shorten retained context, use a memory-saving precision where quality permits, or add memory/capacity.
For Triton deployments, the ratio nv_inference_count / nv_inference_exec_count approximates average batch size, while pending-request count exposes backlog. For vLLM and other LLM engines, use the runtime’s own counters for waiting and running requests, KV-cache usage, preemptions, and scheduling delay. NVIDIA AIPerf’s reference table treats vLLM KV-cache usage above 0.9 as near full capacity; use that as the reference’s warning indicator, not as a universal safe limit for every engine.
Step 4: Increase useful batching without violating latency limits
Conventional neural-network inference
For CNNs, feed-forward networks, and other non-autoregressive models, batching lets matrix operations amortize launch and per-layer overhead. Test a range of batch sizes under the real input-shape distribution. TensorRT guidance says larger batches are generally more efficient and notes that batch sizes that are multiples of 32 often perform well for supported FP16 and INT8 matrix-multiply workloads. That observation is not a rule: newer GPUs can sometimes achieve higher throughput with smaller batches because more of the working set fits in L2 cache.
If requests arrive independently, use dynamic or opportunistic batching so the runtime collects compatible work for a short window. The waiting window adds fixed latency, so measure the gain against your TTFT and end-to-end limits. If shapes vary, configure suitable dynamic-shape optimization profiles and test whether excessive padding wastes compute.
Online LLM serving
LLM engines use continuous or in-flight batching: new requests can join a batch while existing requests are still decoding. This keeps the GPU supplied as requests finish at different times, but it also means scheduler policy, token budgets, and cache availability determine the effective batch.
Increase capacity gradually. A larger active token budget often improves matrix utilization, but once the GPU is saturated, additional tokens mainly increase waiting and TTFT. Measure output throughput, TTFT, ITL, total latency, queue time, and cache pressure at every step.
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NVIDIA’s TensorRT-LLM documentation summarizes the trade-off as selecting a reasonably high max_num_tokens for token throughput and GPU math utilization, but not so high that TTFT and TPOT SLOs are missed. The documentation notes that max_num_tokens defaults to 8192 starting with TensorRT-LLM v0.11; this is a version-specific default, not a universal recommendation.
Offline bulk inference
When completion can be delayed, maximize aggregate throughput within a defined time and resource budget. Larger batches and higher concurrency may be appropriate because per-request latency is not the primary objective. Still record total job time, tokens or samples processed, GPU-hours, failures, and memory headroom. Do not reuse an offline setting for an interactive endpoint without rerunning latency and queue tests.
Step 5: Tune LLM prefill, decode, and KV-cache behavior
Autoregressive serving contains two different workloads:
- Prefill (context processing): processes the prompt and creates the initial key-value (KV) cache. Long prompts can consume substantial compute and memory in bursts.
- Decode (generation): produces output one token at a time while reading the KV cache. It is often limited by memory bandwidth, scheduling, and the number of active sequences.
In-flight batching interleaves these phases, but a long prefill can delay generation for existing users. Chunked prefill divides large contexts so generation work can be scheduled between chunks. Test chunk size and scheduler behavior with your actual prompt-length mix; a setting that helps long-context traffic can add overhead for short prompts.
The KV cache consumes GPU memory for every active sequence and token. A token budget that is needlessly generous can reserve memory that could hold more concurrent requests. Conversely, an overly small budget can force preemption, truncation, or repeated work. Track cache occupancy, preemptions, waiting time, and successful concurrency together.
Separate traffic classes when their SLOs differ. A short interactive request should not wait behind a very long context if the scheduler can enforce priorities or separate pools. Validate that any isolation policy does not leave one pool idle while another is overloaded.
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Step 6: Remove execution gaps and overlap independent work
CUDA streams can overlap kernels, data transfers, or independent portions of a model when dependencies and hardware resources permit. Use overlap only after confirming that the model and runtime expose independent work; adding streams to a dependency-bound graph can increase synchronization overhead instead of improving throughput.
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Asynchronous scheduling is another way to avoid gaps. Current vLLM benchmark documentation describes asynchronous scheduling as a mechanism that can reduce GPU idle periods and improve latency and throughput. Its command-line options and defaults can change, so record the exact runtime version and setting in every benchmark. Verify that asynchronous execution preserves output correctness and does not worsen tail latency.
Also inspect host-side work: tokenizer throughput, request serialization, memory copies, CPU thread pools, and network ingress. Pinning or batching host operations may matter more than a kernel-level change when the GPU is waiting for inputs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Step 7: Compare precision and runtimes at fixed quality
Precision changes can alter both compute cost and memory capacity. TensorRT-LLM lists hardware-specific kernels and optimizations such as FP8 and NVFP4 quantization, tensor and pipeline parallelism, disaggregated serving, and speculative decoding. Availability and benefit depend on GPU architecture, model layers, calibration, and runtime version.
Benchmark FP16, FP8, INT8, or other candidates only after defining an acceptable quality check. Compare task accuracy, perplexity or calibration metrics where appropriate, refusal and safety behavior, output-format compliance, and numerical stability. A faster configuration that fails quality validation is not an optimization.
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| Axis | Questions to answer |
|---|---|
| Compatibility | Does it support the GPU architecture, model family, operators, sequence lengths, and required parallelism? |
| Throughput | What are requests per second and tokens per second at target concurrency and at the required per-user generation speed? |
| Latency | What are TTFT, ITL/TPOT, end-to-end percentiles, and queue behavior under burst and steady traffic? |
| Memory | How much model memory is used, how much KV-cache capacity remains, and how often do preemptions occur? |
| Scheduling | Are continuous batching, chunked prefill, asynchronous scheduling, priorities, or disaggregated serving supported? |
| Optimization | Are quantization and speculative decoding available for the exact model and GPU? |
| Operations | How stable are APIs and defaults, and how difficult are upgrades, rollback, observability, and debugging? |
TensorRT-LLM is customized for NVIDIA platforms, while NVIDIA’s vLLM guidance emphasizes continuous batching and memory management as throughput mechanisms. These are vendor descriptions, not an independent ranking. Measure the engines on your deployment.
How to run a disciplined tuning sweep
- Warm the deployment. Load the model, complete compilation or engine building, fill ordinary caches, and discard warm-up samples.
- Run the baseline. Use fixed input and output distributions at several concurrency or arrival-rate points. Capture all throughput, latency, queue, and memory metrics.
- Change one control. Adjust batch capacity, dynamic-batching delay, LLM token budget, chunked-prefill policy, precision, or scheduling mode—but not several at once.
- Repeat long enough. Include steady-state and burst tests. Report sample count, warm-up policy, and percentile confidence or variability.
- Plot the frontier. Mark configurations that violate TTFT, ITL, end-to-end latency, queue-time, memory, or quality limits. Among the remaining points, choose the highest useful throughput.
- Retest at neighboring loads. A setting that wins at one concurrency can lose at another. Confirm behavior below, at, and above expected production load.
- Record the complete configuration. Save model hash, runtime and driver versions, GPU inventory, precision, scheduler settings, token limits, batch settings, workload files, and metric definitions.
Common failure modes and fixes
“GPU utilization is low, so increase the batch size.”
First verify that requests are available and that preprocessing is not limiting the device. If queue time is near zero, a larger batch may simply make each request wait longer without adding useful work. Increase offered load or remove the upstream bottleneck before changing GPU limits.
“The largest token budget gives the highest throughput.”
Only until the device is saturated. Beyond that point, extra queued tokens can worsen TTFT, TPOT, total latency, and cache occupancy. Sweep the budget and select the highest point inside the SLO envelope.
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It can be a sign of good memory utilization, but it leaves little room for bursts and can trigger preemptions. Establish a headroom policy from observed traffic, not from a universal percentage.
“A published benchmark predicts our capacity.”
Vendor figures are conditional on a particular model, GPU, precision, software build, input and output lengths, traffic pattern, and SLO. Use them to understand trade-offs, then benchmark your own workload.
“Changing precision is free throughput.”
Quantization can change quality, supported operators, memory layout, and numerical behavior. Validate outputs and measure latency tails as well as aggregate speed.
Production checklist
- Workload distributions and arrival pattern are documented and reproducible.
- Throughput, TTFT, ITL/TPOT, end-to-end latency, queue time, and percentiles are collected.
- Memory use, KV-cache occupancy, pending requests, and preemptions are visible.
- Batch size, batching delay, token budget, chunked prefill, and asynchronous scheduling have been swept rather than guessed.
- Model quality has been checked after precision or runtime changes.
- Capacity is tested at normal load, peak load, and burst conditions with explicit headroom.
- Runtime, driver, GPU, model, or traffic changes trigger a repeat benchmark.
Bottom line
Optimize GPU utilization by keeping the accelerator supplied with the right amount of parallel work—not by maximizing a dashboard percentage. Start with a fixed workload and explicit SLOs, measure throughput alongside user latency and queueing, then tune batching, LLM token budgets, KV-cache capacity, scheduling, overlap, and precision one variable at a time. The best configuration is the point on your measured throughput curve that meets quality, latency, and memory limits for the workload you actually serve.
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