When you cannot add GPUs, the practical way to lower AI inference cost is to produce more useful output from the hardware you already have. Start by measuring a representative workload, then remove the specific bottleneck: idle compute, excessive queueing, KV-cache memory, prefill time, decode time, or quality-related rework. In most deployments, continuous batching and scheduling, disciplined KV-cache management, and a tested precision change are the first candidates.
There is no reliable universal saving percentage. A change that raises tokens per second can still increase tail latency, memory failures, or rejected outputs. Keep an optimization only when it improves your cost per accepted output at the latency, reliability, context-length, and quality targets your service actually promises.
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What “lower inference cost” should mean
With fixed GPU capacity, accounting cost may not change immediately: you still pay for the same machines. The useful objective is to increase the amount of accepted work they complete, or to serve the same work with fewer GPU-hours. Define the unit before changing settings. Common choices are cost per completed request, cost per generated token, or cost per useful token that passes your quality checks.
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- Throughput: sustained input and output tokens per second at the target load.
- Latency: time to first token, inter-token latency, and tail latency such as p95 or p99 when those are part of your service level.
- Capacity: peak and steady GPU memory, KV-cache occupancy, active sequences, and queue depth.
- Quality: task-specific accuracy, refusal behavior, factuality checks, or human review results after any model, precision, or decoding change.
- Reliability: out-of-memory events, preemptions, timeouts, cancelled requests, and retry rates.
Compare two configurations on the same model, hardware, software versions, request mix, and latency target. Otherwise, a “saving” can simply reflect an easier test.
Build a baseline before tuning
Run a stable test that resembles production rather than a single prompt benchmark. Record the model and weight precision, GPU type and count, serving engine and version, arrival rate or concurrency, prompt and output-length distributions, and the latency objective. Include representative long-context requests if customers send them; KV-cache behavior can be invisible in a short-prompt test.
- Capture utilization and memory over time. A one-minute average can hide allocation spikes or periods when the GPU is waiting for work. Save compute utilization, allocated and reserved memory, KV-cache usage, active sequences, and queue length.
- Measure both phases. Prefill processes the prompt; decode generates tokens. Record time to first token separately from per-token latency so a change that helps one phase does not conceal harm to the other.
- Measure accepted output. Count completed requests and tokens that meet your quality checks, not just raw kernel throughput.
- Repeat at several loads. Test below saturation, near the target operating point, and at the highest load you can sustain without violating the service objective. Include warm-up and steady-state intervals.
- Save a rollback point. Keep the exact configuration and model artifacts so every experiment can be reversed quickly.
Use this baseline to classify the constraint. Low GPU utilization with a growing queue suggests scheduling or request-shape problems. High utilization with stable memory suggests compute pressure. Frequent out-of-memory events, cache preemption, or a low number of active sequences indicates memory pressure. High time to first token points toward prefill; poor inter-token latency points toward decode or contention.
Use continuous or in-flight batching when work is available
Fixed batches often wait for the slowest sequence before admitting new work. Continuous, also called in-flight, batching allows requests to enter as other sequences finish and can interleave context processing with generation. NVIDIA describes this approach as a way to keep execution units busier when request lengths vary.
Set a maximum number of active sequences, batch-token budget, or equivalent scheduler limit in the serving engine. Increase it gradually while watching memory and tail latency. The useful limit is not the largest number the scheduler accepts: it is the largest that keeps the latency target and leaves enough memory for the cache and runtime workspace.
When batching helps
- Requests arrive frequently enough to keep a pool of active sequences.
- Prompt and completion lengths vary, so fixed batches would otherwise wait on stragglers.
- GPU traces show idle gaps between requests or between decode steps.
When it does not
- Traffic is too sparse to fill a batch; waiting for more requests only adds queue time.
- The workload has a strict interactive latency limit that leaves little scheduling slack.
- Memory is already the limiting resource. More concurrent sequences consume more KV-cache space and can trigger preemption or out-of-memory failures.
Judge the change at the target concurrency, not on an unconstrained throughput run. A higher tokens-per-second figure is useful only if queueing and tail latency remain acceptable.
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Manage the KV cache as a first-class capacity budget
During autoregressive generation, the KV cache stores attention state so the system does not recompute the entire history for every new token. Its footprint grows with sequence length and the number of active sequences. Long prompts, long completions, and higher concurrency therefore compete directly for GPU memory.
Control the inputs that consume cache
- Set a context limit that reflects the product requirement rather than the model’s maximum window.
- Set a maximum number of active sequences and a completion-length limit; expose exceptions only to workloads that need them.
- Separate long-context traffic from short interactive traffic when one class causes the other to miss latency targets.
- Measure cache occupancy by request shape. An average context length is not enough when a small fraction of very long requests causes failures.
Use paging, reuse, and deliberate allocation
Serving systems can manage cache blocks rather than reserving one contiguous region per sequence. NVIDIA’s TensorRT-LLM material discusses paged cache management, cache reuse, and quantized cache variants. Reuse is valuable when requests share prefixes, but it requires correct invalidation and accounting for the memory retained by reusable blocks.
vLLM’s current “Optimization and Tuning” documentation covers GPU-memory utilization, tensor parallelism, chunked prefill, and preemption controls. Exact option names and defaults depend on the installed vLLM version, so verify them against that version’s documentation and observe the resulting allocation rather than copying a value from another release.
Recognize cache preemption
If the engine repeatedly evicts or recomputes cache state, raw throughput can fall even when GPU utilization looks high. Treat preemption as a memory-capacity signal: reduce active sequences or context length, improve cache allocation, or evaluate a lower-memory cache format before simply increasing concurrency.
Test precision and quantization as deployment experiments
Lower-precision weights can reduce model memory and sometimes increase throughput. Lower-precision KV-cache formats can allow more concurrent or longer requests. Neither result is automatic: support depends on the model, GPU architecture, kernels, and serving engine, and numerical changes can alter output quality.
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Evaluate each candidate format with a paired test:
- Confirm that the model architecture, kernels, and GPU support the format without falling back to a slower path.
- Measure peak and steady memory, prompt-processing time, generation rate, time to first token, and tail latency.
- Run representative prompts, including difficult and long-context cases, through the same quality checks used in production.
- Check stability under the intended concurrency. A format that fits one request may fail when several caches coexist.
- Retain the change only if the accepted-output rate and service objectives remain within your limits.
Do not describe a precision change as quality-neutral without evidence for the specific model and task. If quality falls, the apparent infrastructure saving can become a product cost through retries, human review, or customer churn.
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Use speculative decoding only when memory and acceptance make it worthwhile
Speculative decoding uses a smaller draft mechanism to propose tokens and a target model to verify them. When many proposed tokens are accepted, the target can generate a response with less serial work. Acceptance behavior, draft-model choice, implementation, and request lengths determine whether it helps.
The method also needs memory for the draft path and its state. NVIDIA’s NIM guidance warns that speculative methods require more memory than serving the target model alone. On a constrained GPU, an out-of-memory event erases any throughput gain. If memory is insufficient, the guidance points to reducing KV-cache footprint, context length, or concurrency, or choosing a lighter speculative method.
NVIDIA has reported a three-times throughput result for Llama 3.3 70B with TensorRT-LLM speculative decoding. That is a vendor-reported, setup-specific example; it is not a promise for another model, GPU, request mix, or latency target. Reproduce the comparison with your own quality and memory checks before planning capacity around it.
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Large prompts can monopolize the GPU while interactive requests wait. In vLLM, chunked prefill divides prompt processing into smaller pieces that can be scheduled alongside decode work. This can protect decode latency in mixed workloads, but it changes scheduling behavior and may alter total throughput.
Tune chunk size and related limits only after identifying prefill as a bottleneck. Compare time to first token, inter-token latency, queueing, and total tokens per second. The vLLM documentation also describes preemption and memory-related tuning; settings are version-specific, and the documentation does not guarantee lower total cost for every workload.
Consider disaggregated prefill and decode for a measured phase bottleneck
Prefill and decode stress hardware differently. Disaggregated serving places them on separate worker pools so each phase can be tuned independently—for example, allocating resources for prompt-heavy traffic without letting it disrupt token generation.
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This is an architectural change, not a free configuration switch. TensorRT-LLM’s disaggregated-serving documentation describes transferring the KV cache between workers and overlapping transfer with computation. The design therefore adds coordination, network or interconnect traffic, cache-transfer timing, failure handling, and operational complexity.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose experiments by the constraint they address
| Candidate | Primary constraint addressed | What to measure | Main risk |
|---|---|---|---|
| Continuous or in-flight batching | Idle GPU time and variable request lengths | Throughput, queue time, tail latency, active sequences | More concurrency increases KV-cache pressure |
| KV-cache limits, paging, and reuse | Memory capacity for long or concurrent requests | Cache occupancy, preemption, context support, latency | Eviction or incorrect reuse can add work or affect correctness |
| Weight or cache quantization | Model and cache memory footprint | Memory, kernel path, throughput, quality | Unsupported formats, slower fallback, or quality loss |
| Speculative decoding | Serial decode work | Acceptance rate, decode rate, memory, quality | Additional memory and draft/verification overhead |
| Chunked prefill | Prompt-processing interference with decode | Time to first token, inter-token latency, total throughput | Scheduling overhead or lower aggregate throughput |
| Disaggregated prefill/decode | Persistent phase-specific resource imbalance | Transfer time, phase utilization, end-to-end latency, reliability | Coordination and KV-cache transfer complexity |
A safe optimization sequence
- Freeze the baseline. Record model, precision, GPU count and type, engine versions, traffic shape, latency and quality targets, utilization, memory, and failure rates.
- Classify the bottleneck. Decide whether the evidence points to idle execution, compute saturation, cache memory, prefill, decode, or queueing.
- Apply the least invasive control. Start with batching and scheduler limits, context and concurrency caps, and cache allocation settings that address the diagnosed constraint.
- Run a controlled load test. Use the same request distribution and compare steady-state throughput, accepted output, latency percentiles, memory, and failures.
- Test precision changes separately. Keep the model and traffic fixed so any quality or performance difference is attributable to the format.
- Test speculative decoding only with headroom. Confirm memory capacity for the draft path and record token acceptance rather than assuming a gain.
- Escalate architecture only when justified. Consider disaggregated serving after phase-specific measurements show that simpler controls cannot meet the target.
- Canary and monitor. Roll out gradually, watch long-context and peak-load cohorts, and keep the previous configuration ready for rollback.
How to calculate whether a change really saves money
For a fixed-capacity deployment, divide the recurring GPU cost for the measurement interval by accepted output during that interval. If you report cost per token, state whether the denominator is generated tokens or quality-approved tokens. For a request-based service, include failed, timed-out, and retried requests in the cost numerator while counting only completed requests in the useful-output denominator.
Compare both steady-state and burst behavior. An optimization may lower cost during a full queue but worsen burst recovery, forcing extra replicas or causing dropped requests. Include engineering and operational cost when evaluating a major architecture such as disaggregated serving; a small infrastructure gain may not offset the additional systems burden.
Common failure modes and fixes
“GPU utilization is high, so throughput must be optimal.”
High utilization can represent cache recomputation, preemption, or inefficient kernels. Inspect accepted throughput, cache events, and latency rather than relying on one utilization gauge.
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“Increase concurrency until the graph is full.”
Concurrency consumes KV-cache memory and can push tail latency beyond the product target. Increase it in small steps and stop at the last point that satisfies memory and latency limits.
“Quantization always lowers cost without changing answers.”
Unsupported kernels may fall back to slower execution, and numerical changes can affect quality. Verify hardware support and run task-level evaluations on the actual model.
“Speculative decoding is a universal speed-up.”
It depends on draft-token acceptance and adds memory. Measure acceptance and end-to-end latency; reduce cache, context, or concurrency—or remove the method—if memory becomes unstable.
“A vendor’s three-times result is our expected saving.”
The reported Llama 3.3 70B TensorRT-LLM result is tied to its tested setup. Treat it as evidence that the method can work, not as a capacity or cost forecast for another deployment.
“Split prefill and decode immediately.”
Disaggregation adds KV-cache transfer and coordination. First prove that a persistent phase imbalance, rather than general underutilization or memory pressure, justifies that complexity.
Operational checklist
- Can you reproduce the baseline with a fixed workload and the same software versions?
- Do you know whether the limiting resource is compute, memory, prefill, decode, queueing, or quality?
- Are active-sequence and context limits explicit rather than accidental defaults?
- Are cache occupancy, preemption, and out-of-memory events monitored?
- Are latency percentiles measured separately for time to first token and token generation?
- Does every precision or decoding change have a representative quality test?
- Are vendor benchmark figures labeled with their model and setup instead of generalized?
- Can you roll back without rebuilding the model or changing the client contract?
The practical decision
Begin with measurement, then tune scheduling and cache capacity around the workload you actually serve. Precision changes and speculative decoding can unlock more capacity, but only when hardware support, memory headroom, acceptance behavior, and output quality are verified together. Reserve disaggregated serving for a demonstrated prefill/decode imbalance. This sequence turns limited GPUs into a controlled optimization problem rather than a search for a guaranteed percentage.
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