Optimize for the highest throughput that still meets your latency, memory, quality, and reliability limits—not for a utilization percentage by itself. The repeatable method is to hold the workload constant, measure requests and tokens per second together with TTFT, inter-token latency, end-to-end latency, queueing, and memory pressure, then sweep batching, token budgets, concurrency, scheduling, and precision one change at a time.
The right setting depends on whether you run fixed-shape neural-network inference, autoregressive large language models (LLMs), or offline bulk jobs. A batch size that increases aggregate throughput can also make each user wait longer. For LLMs, prompt processing (prefill), token generation (decode), and the KV cache create additional scheduling and memory constraints.
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What “high GPU utilization” should mean
A busy GPU is not automatically an efficient or responsive service. A kernel can keep the device occupied while requests spend time waiting in a queue, while memory is close to exhaustion, or while individual users receive tokens too slowly. Conversely, a utilization graph can show gaps even when the service is healthy if requests arrive sporadically or the model cannot expose enough parallel work.
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Choose the primary objective
- Interactive serving: keep time to first token (TTFT), inter-token latency (also called TPOT or ITL), and end-to-end latency within their limits while increasing requests per second or output tokens per second.
- Bulk or offline inference: when delayed completion is acceptable, maximize aggregate inferences or tokens per second and report total job time and resource use.
- Per-user streaming quality: protect a minimum generation speed for each active user, even if that reduces total concurrent users.
NVIDIA’s benchmark guidance illustrates why these objectives can conflict: its stated target was about 50 output tokens per second, or roughly 20 milliseconds between tokens, in that article’s particular scenario. Treat that as an example, not a universal target.
1. Define a reproducible workload before changing settings
Record the conditions for every run. Otherwise, an apparent optimization may simply reflect shorter prompts, fewer output tokens, a different arrival pattern, or a changed software stack.
- Model name and revision, tokenizer, precision, quantization, and any adapters.
- GPU model and count, memory capacity, driver, CUDA version, and runtime version.
- Input-length and output-length distributions, including percentile values and maximums.
- Concurrency for closed-loop tests, or request arrival rate and burst pattern for open-loop tests.
- Whether requests stream results and whether prompts share prefixes.
- Latency and quality SLOs, plus an error, timeout, or out-of-memory policy.
Use the same request trace, random seeds where applicable, warm-up period, duration, and reporting method for every comparison. Include enough steady-state time to observe queueing and cache behavior rather than measuring only startup.
2. Measure the whole operating curve
Collect aggregate and per-request metrics at each concurrency, batch, or token-budget setting. Report percentiles, not averages alone; a mean can hide a small group of requests that miss the SLO.
| Metric | What it tells you | How to use it |
|---|---|---|
| Requests per second | Completed request capacity | Compare service capacity at the target concurrency and arrival rate. |
| Output tokens per second | Generation throughput for LLMs | Track aggregate capacity and, separately, per-user generation speed. |
| Prompt or prefill tokens per second | Context-processing capacity | Shows whether long prompts are consuming the available compute. |
| TTFT | Time until the first generated token | Detects queueing, oversized prefills, and excessive batching delay. |
| Inter-token latency (TPOT/ITL) | Spacing between streamed output tokens | Protects the perceived responsiveness of an active stream. |
| End-to-end latency | Total time from admission to completion | Checks the complete SLO, including queueing and generation. |
| Queue time and waiting requests | Backlog and admission pressure | Distinguishes insufficient arriving work from a saturated server. |
| Running requests and preemptions | Scheduler contention | Frequent preemption indicates memory or scheduling pressure. |
| GPU memory and KV-cache usage | Capacity headroom | Finds the limit before an increase in concurrency causes failures. |
Use the metric names exposed by your backend. AIPerf’s reference, for example, treats vLLM KV-cache usage above 0.9 as an indicator of near-full capacity; that threshold belongs to that reference and should not be treated as a universal safety limit. For Triton, nv_inference_count / nv_inference_exec_count approximates average batch size, while pending-request count reveals queue buildup.
3. Establish a baseline and identify the bottleneck
Run the unmodified deployment at several controlled concurrency levels. At each point, capture the metrics above and classify the limiting condition before tuning.
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Too little useful work
Low utilization with an empty queue usually means requests are arriving in small groups, the model is waiting on input or synchronization, or the workload is too small to fill the GPU. Improve request grouping, overlap transfers and execution where supported, or consolidate work upstream. Increasing a batch limit cannot help if the application never presents enough requests.
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Compute saturation
When the queue grows, matrix or compute activity remains high, and memory still has headroom, raise batch capacity or token budget gradually. Stop when throughput flattens or a latency SLO fails.
Memory or KV-cache pressure
High memory use, rising preemptions, and requests waiting for cache blocks indicate a capacity limit rather than a lack of arithmetic work. Reduce maximum tokens, reduce concurrency, shorten retained context, use a more memory-efficient precision, or add GPU capacity before increasing the batch.
Scheduling or synchronization gaps
Intermittent GPU idle periods with queued work can indicate host-side scheduling, data-transfer, or stream dependencies. CUDA streams can overlap independent work when the model layers and hardware permit it. Current vLLM benchmark documentation also describes asynchronous scheduling as a way to reduce utilization gaps; validate the feature with your exact model and deployment.
4. Tune batching for conventional neural-network inference
For CNNs, feed-forward networks, and other non-autoregressive models, batching amortizes per-request and per-layer overhead and gives matrix hardware more parallel work. Test static batches when input shapes are stable and dynamic or opportunistic batching when requests arrive independently.
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- Static batching: assemble a fixed number of inputs and execute them together. It is predictable, but a partially filled batch can waste capacity.
- Dynamic batching: collect requests for a bounded time or until a batch is full. It raises utilization for bursty traffic, but the waiting window adds fixed latency.
- Opportunistic batching: execute whatever compatible requests are available when the scheduler reaches a dispatch point. This reduces deliberate waiting but may produce smaller batches.
Sweep both maximum batch size and the batching delay. A larger batch is generally more efficient, yet TensorRT documentation advises empirical testing rather than assuming a single best value. For supported FP16 and INT8 matrix-multiply workloads, batches that are multiples of 32 often perform well; on NVIDIA Ada Lovelace or newer GPUs, L2-cache behavior can sometimes make a smaller batch faster. Treat these as starting points for a benchmark, not rules.
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Shape profiles and execution overlap
If inputs have variable dimensions, define optimization profiles that cover the actual shape distribution. Profiles that are too broad can produce less specialized kernels; profiles that exclude common shapes force fallback behavior or rejection. Measure representative shapes, not only the smallest or largest input.
Where the runtime permits it, use separate CUDA streams for independent transfers and execution. Verify that overlap is real in a profiler and that it does not introduce synchronization or memory contention. An extra stream is not automatically beneficial when layers are already saturating the device.
5. Tune LLM serving as two coupled workloads
Autoregressive serving alternates between prefill—processing the prompt or context—and decode—generating one or more new tokens per active sequence. Prefill is typically more parallel and compute-intensive; decode is incremental and often constrained by memory movement and the KV cache. A scheduler that optimizes only one phase can harm the other.
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Continuous or in-flight batching
In-flight batching admits new requests and removes completed sequences between decode steps instead of waiting for an entire batch to finish. This keeps the GPU supplied as individual requests progress at different speeds. Evaluate it with your real distribution of prompt and output lengths; homogeneous synthetic requests can overstate the benefit.
Chunked prefill
Chunked prefill divides a long context into pieces so context work can be interleaved with generation for existing users. It can prevent a long prompt from monopolizing a scheduling step and can improve responsiveness under mixed traffic. The chunk size and admission policy still trade prefill throughput against TTFT and decode latency, so sweep them under the target SLO.
Token budget and max_num_tokens
The token budget limits how many tokens a scheduler may process in an iteration or batch. TensorRT-LLM documentation notes that a reasonably high max_num_tokens can improve matrix utilization and token throughput, while pushing it beyond the point of saturation can worsen TTFT and total latency. Its documentation records a default of 8192 beginning with version 0.11; that is a version-specific default, not a recommendation.
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Raise the budget in measured steps. Watch prompt and output throughput, TTFT, TPOT, queue time, and memory after each step. If aggregate tokens per second rises but users miss their TTFT or TPOT limits, lower the budget or use chunked prefill rather than pursuing a higher utilization number.
KV-cache capacity
The KV cache stores attention keys and values for active sequences. Longer contexts and larger concurrent batches consume more cache blocks, reducing the number of requests that can remain resident. Realistic maximum token budgets can leave memory for additional concurrent requests; an unnecessarily large ceiling can reserve capacity that typical requests never use.
Monitor cache use, preemptions, waiting requests, and queue time together. A cache near full capacity can produce oscillating throughput as sequences are evicted or recomputed. Preserve headroom for length bursts and allocator overhead instead of operating at the edge of out-of-memory failure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Increase concurrency without violating the SLO
Concurrency is useful only while it creates productive parallel work. Increase it step by step and record the point where each latency percentile crosses its limit. Distinguish a closed-loop test, where a client sends a new request after completion, from an open-loop test, where arrivals continue at a specified rate; they stress queueing differently.
- Start below saturation and verify that the workload is stable.
- Increase concurrency or arrival rate by a fixed increment.
- Wait for steady state, then record throughput, latency percentiles, queue time, memory, and errors.
- Stop when an SLO, memory headroom, or quality constraint fails.
- Choose the highest stable point below that boundary, not the first point with the highest instantaneous utilization.
Higher aggregate throughput can coexist with slower individual responses. NVIDIA’s 2025 benchmark article reported that, in its specified Llama 3.1 8B setup, an FP16 checkpoint handled about 256 concurrent users at approximately 72 tokens per second per user before violating a 50-token-per-second-per-user constraint; an FP8 checkpoint handled 512 concurrent users at approximately 66 tokens per second per user. Those are vendor-reported results for that model, hardware, software, and workload, not transferable expectations.
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7. Test precision and runtime choices at fixed quality
Precision changes can improve memory capacity and arithmetic throughput, but they must pass an output-quality check and the same latency SLO. TensorRT-LLM lists hardware-specific kernels and optimizations such as FP8 and NVFP4 quantization, parallelization, disaggregated serving, and speculative decoding. Availability and benefit depend on GPU architecture, model support, sequence lengths, and runtime version.
Compare a precision or runtime change only after fixing the workload and measuring quality. Keep a reference output set, define tolerances appropriate to the application, and test long-context and edge-case prompts. A faster kernel that changes answers beyond the accepted tolerance is not an optimization.
TensorRT-LLM is customized for NVIDIA platforms, while vLLM emphasizes continuous batching and memory management. These are vendor descriptions, not an independent ranking. Compare the runtimes on supported GPU architectures, model family, parallelism requirements, throughput at target concurrency, per-user generation speed, latency percentiles, cache behavior, feature support, operational complexity, and version stability.
8. Use a controlled tuning loop
- Freeze the workload definition. Save model, precision, GPU, runtime versions, request trace, length distributions, and arrival pattern.
- Write the SLO. Set limits for TTFT, TPOT/ITL, end-to-end latency, error rate, and memory headroom. Add a minimum per-user output speed when streaming.
- Baseline. Warm the service, run steady state, and record all aggregate, latency, queue, scheduling, and memory metrics.
- Choose one lever. Change batch size, batching delay, token budget, concurrency, chunked prefill, asynchronous scheduling, precision, or runtime—but not several at once.
- Sweep a bounded range. Use small increments and stop a run when it clearly violates memory or SLO limits.
- Check the bottleneck again. Confirm whether the change improved useful work or merely moved pressure into queueing, cache use, or host overhead.
- Validate quality and reliability. Test representative outputs, restarts, error handling, and burst behavior.
- Record the operating point. Keep the configuration, metrics, and workload trace so a later driver, model, GPU, or runtime change can be compared fairly.
9. Troubleshoot common failure patterns
GPU utilization is low and latency is high
Check request arrival gaps, host preprocessing, data transfers, synchronization, and batch formation. If the queue is empty, increase batching delay only within the latency budget or improve upstream grouping. If the queue is nonempty, profile scheduler and transfer overhead before raising batch limits.
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Reduce batching delay or token budget, cap concurrency, and test chunked prefill. Separate prompt-heavy traffic from decode-heavy traffic when the scheduler allows it. Do not accept a throughput gain that violates the stated TTFT SLO.
Decode speed falls as users are added
Inspect KV-cache use, preemptions, memory bandwidth, and TPOT percentiles. The service may have crossed its cache or memory-bandwidth boundary. Lower maximum tokens, reduce concurrent sequences, or use a supported lower-memory precision.
Increasing batch size changes nothing
Check whether requests actually form full batches, whether the model is already compute-saturated, and whether another resource—memory, host scheduling, or input processing—is limiting. For variable shapes, confirm that requests share compatible profiles.
Performance changes after an upgrade
Record the exact runtime, driver, CUDA, model, and CLI defaults. Mutable interfaces, including vLLM’s benchmark commands and TensorRT-LLM’s version-specific settings, can change behavior. Re-run the saved workload rather than comparing dashboard snapshots.
10. Select an operating point, not a headline number
Plot aggregate requests or output tokens per second against concurrency, while overlaying TTFT, TPOT, end-to-end latency, queue time, and memory. Select the knee of the curve that stays inside every SLO and leaves operational headroom. For online services, a slightly lower aggregate rate with stable tail latency is often preferable to a peak that causes queue explosions. For offline jobs, the same hardware may be deliberately run at a higher batch or token budget because per-request delay is not the objective.
Re-run the loop whenever the model, prompt mix, output limits, GPU, driver, runtime, precision, or traffic pattern changes. There is no universal utilization percentage, batch size, or token budget: the defensible answer is the measured configuration that meets this deployment’s workload and service limits.
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