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The reliable way to reduce AI inference cost without making users wait longer is to measure the workload in two phases, then remove its actual bottleneck. Prompt prefill is usually compute-bound; token decode is commonly limited by memory bandwidth. Track time to first token (TTFT), time between tokens (TBT), throughput, tail latency, output quality, and cost together. Then tune serving efficiency before changing the model.
There is no universally safe switch. A configuration that lowers cost per token for long prompts can worsen interactive latency, while a technique that improves TTFT can increase total accelerator hours. The practical target is the lowest cost per successful request that still meets your latency and quality SLOs.
Start with a workload baseline, not a configuration change
Measure the production shape you actually serve: prompt-token distribution, output-token distribution, concurrency, arrival bursts, model, hardware, runtime, context limit, and quality requirements. A benchmark using one short prompt at low concurrency cannot predict an interactive service handling long contexts and bursty traffic.
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Prefill processes the input prompt and builds the initial key-value (KV) cache. It is commonly compute-bound, so long prompts can drive TTFT and accelerator utilization. Decode generates output tokens sequentially. Each step reads model weights and KV-cache data, and is often memory-bandwidth-bound. Decode behavior shows up in TBT and tokens per second after the first token.
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Record at least these metrics for every test cell:
- TTFT: request arrival to the first generated token.
- TBT: the interval between generated tokens, reported as an average and as a distribution.
- p50, p95, and p99 latency: averages hide queueing and overloaded-worker behavior.
- Throughput: input tokens per second, output tokens per second, requests per second, and tokens per accelerator.
- Cost: accelerator time and platform charges divided by successful requests or output tokens.
- Quality: task accuracy, refusal behavior, structured-output validity, and human or automated preference scores.
- Utilization and memory: compute utilization, memory bandwidth, model memory, KV-cache occupancy, queue depth, and cache hit rate.
Keep the request mix fixed while changing one major variable. Report the same concurrency and latency target for every candidate. A lower raw cost is not an improvement if retries, timeouts, invalid outputs, or quality failures increase.
Define the objective as a constrained optimization
Write the SLO before tuning. For example: “p95 TTFT below 800 ms, p95 TBT below 60 ms, structured-output validity above 99.5%, and minimum quality score X.” Optimize cost only among configurations that satisfy those constraints. A useful comparison is:
cost per successful request = total serving cost / requests that meet latency and quality requirements
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Also calculate cost per output token when output volume is the business driver. Keep one-time compilation or warm-up costs separate from steady-state cost, and state whether a price is a recurring rate, a one-time allowance, or a promotional term.
Improve serving efficiency before changing the model
Serving changes can remove duplicated work or keep accelerators busy without altering model weights. Their value depends on concurrency, request shape, prefix reuse, and queueing.
Use continuous or in-flight batching
Static batching waits for a group of requests before starting work. Continuous batching admits new requests as sequences finish, keeping the accelerator occupied while avoiding a full-batch synchronization point. This is especially useful when requests have different prompt and output lengths.
Larger batches generally improve throughput, but they can increase queueing and request latency. Smaller batches tend to favor interactive latency. Tune the maximum number of active sequences and scheduler limits against the arrival pattern, not against a synthetic benchmark with a constant stream.
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- Establish a baseline at the target concurrency and burst rate.
- Increase the active-sequence limit gradually while recording p95/p99 TTFT and TBT.
- Stop increasing it when throughput gains flatten or the latency SLO is breached.
- Repeat with realistic mixtures of short and long prompts; a setting that works for uniform requests may fail for mixed traffic.
Monitor queue wait separately from model execution time. If queue wait grows while accelerator utilization is high, more batching is not the answer; add capacity, improve routing, or reduce work per request.
Cache repeated prefixes
Prefix caching stores the KV state for an identical prompt prefix. Later requests can reuse that state instead of recomputing the shared system prompt, policy text, retrieval scaffold, or conversation prefix. It can reduce prefill compute and improve TTFT when the same prefix appears often.
Cache keys must include every input element that changes the model’s computation, such as model version, tokenizer-relevant settings, system text, tools, and applicable adapter state. Set an eviction policy and memory budget so caching does not crowd out active sequences.
Make routing cache-aware
Prefix caching only helps when a request reaches a worker that still holds the relevant prefix. Random load balancing can send identical prefixes to different workers and destroy locality. Route by a stable prefix hash or use a gateway that knows cache ownership, while retaining a fallback for failed or overloaded workers.
Google Cloud reported a GKE Inference Gateway case study on March 27, 2026, attributing a 35% TTFT reduction and doubled cache efficiency to routing changes without changing hardware, model, or cluster size. Those figures belong to that case study; they are not a guaranteed result for another deployment.
Use chunked prefill for mixed workloads
A very long prompt can monopolize the accelerator while shorter requests wait for their first token. Chunked prefill divides long prompt processing into smaller pieces and interleaves it with decode work. This can improve interactive tail latency in mixed prompt-length traffic, at the possible cost of additional scheduling overhead.
Test chunk size with your model and runtime. Very small chunks can add overhead; very large chunks provide little isolation from long-prefill stalls. Measure p95 TTFT for short requests separately from long-request throughput.
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Right-size context and sequence limits
Provisioning every worker for the maximum advertised context length can waste memory when most requests are short. Set admission and sequence limits from observed percentiles, while preserving a deliberate path for requests that genuinely require longer context.
- Use a standard limit for the common workload.
- Reject, summarize, retrieve, or route exceptional oversized requests explicitly rather than allowing them to destabilize all traffic.
- Re-test memory headroom under the highest allowed concurrent sequence count; KV-cache use grows with active tokens, not just request count.
Reduce memory pressure with precision changes
Quantization lowers numerical precision for model values. Lower-precision weights generally occupy less memory and require less memory traffic, which can improve throughput and allow a larger or more concurrent workload on the same accelerator. The tradeoff is possible quality loss and dependence on supported kernels, model formats, and hardware.
Quantize weights carefully
Benchmark the exact checkpoint and serving stack. “A lower bit width” is not a complete specification: calibration method, per-channel or per-tensor scaling, outlier handling, kernel implementation, and hardware support all affect results.
Use a representative evaluation set that includes:
- short and long prompts;
- the languages and domains used in production;
- tool calls, JSON or schema-constrained output, and refusal cases;
- long generations where small token-level errors can accumulate.
Compare quality and latency against the unquantized baseline. If quality degrades, consider a less aggressive format, selective higher precision for sensitive layers, or routing only suitable tasks to the quantized model.
Consider KV-cache quantization for long context
The KV cache can become the dominant memory consumer for long sequences and large batches. Quantizing KV entries reduces memory per active sequence, potentially enabling more concurrency or longer contexts on the same hardware.
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Google’s GKE guidance provides an FP8 E5M2 KV-cache configuration example and explicitly warns that its setting reduces inference accuracy. Treat it as a configuration-specific example, not a universal recommendation. Validate factuality, retrieval accuracy, tool-call correctness, and long-context behavior on your own prompts before deployment.
Accelerate decode with speculative decoding
Speculative decoding uses a smaller, cheaper draft process to propose several tokens. The target model verifies those tokens in batches and accepts the matching prefix. When acceptance is high, the target performs fewer sequential decode steps, improving tokens per second and sometimes reducing TBT.
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When it helps
- The draft model is substantially cheaper than the target.
- Draft proposals closely match the target, producing a high acceptance rate.
- The runtime can verify multiple proposed tokens efficiently.
- Memory capacity can accommodate the draft process and its caches.
When it does not
A weak or expensive draft model can consume more resources than it saves. Low acceptance causes repeated verification work without enough reduction in target-model steps. Communication, scheduling, or implementation overhead can erase the gain at low batch sizes.
Measure draft-model cost, acceptance rate by position, target-model steps per output token, TTFT, TBT, total accelerator time, and quality. Compare end-to-end cost per successful request rather than quoting only the speedup of the target model’s decode loop.
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Compiled execution paths, fused kernels, and specialized inference runtimes can improve hardware utilization and reduce framework overhead. Current vLLM documentation lists continuous batching, chunked prefill, prefix caching, quantization, speculative decoding, and prefill/decode disaggregation among its capabilities. Exact support varies by release, model, hardware, and configuration, so verify the current support matrix before committing to a feature.
Compilation can introduce warm-up time, compilation artifacts, memory requirements, and hardware-specific constraints. Benchmark both cold-start and warm steady state if autoscaling or frequent worker replacement matters. Keep a rollback image with the previous runtime and model format.
Separate prefill and decode only when scale justifies it
Prefill is commonly compute-heavy, while decode is often memory-bandwidth-heavy. Prefill/decode disaggregation places the phases on resources suited to each behavior and can let each pool scale independently.
The split adds network transfers, KV-state movement, scheduling, observability, failure handling, and capacity-planning complexity. It is most compelling when measurements show sustained imbalance: for example, prefill workers are compute-saturated while decode workers have unused compute but insufficient memory bandwidth. Include network cost and transfer latency in the cost-per-request calculation; a theoretical accelerator saving is not enough.
Compare optimizations with a controlled matrix
Run an experiment matrix instead of enabling every feature at once. Keep model checkpoint, tokenizer, prompt corpus, hardware type, software image, and traffic replay fixed while changing one factor or one documented combination.
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| Dimension | What to record | Why it matters |
|---|---|---|
| Latency | TTFT and TBT, each at p50, p95, and p99 | Prefill and decode regressions can cancel out in one end-to-end number. |
| Throughput | Requests/s, input tokens/s, output tokens/s, tokens per accelerator | Shows whether utilization gains translate into useful work. |
| Cost | Cost per successful request and per output token | Penalizes retries, timeouts, and failed quality checks. |
| Quality | Task accuracy, structured-output validity, safety and refusal behavior | Prevents a cheaper but unusable configuration from winning. |
| Resource use | Compute utilization, memory bandwidth, model memory, KV occupancy, cache hit rate | Identifies whether the bottleneck moved. |
| Operations | Warm-up time, failure modes, runtime and hardware constraints | Captures the cost of operating the optimization. |
Test at the concurrency used by your SLO, then test just above it to expose queueing behavior. Include bursty arrivals, cancellation, retries, streaming clients, and the longest supported prompts. A provider’s evaluation workflow can be a useful model: AWS documents SageMaker options for quantization, speculative decoding, compilation, and performance evaluation, including latency, throughput, and price comparisons. That workflow does not establish that a particular setting will win for your service.
A practical rollout sequence
- Instrument. Add phase-level timestamps, token counts, queue time, cache events, accelerator utilization, and per-request cost attribution.
- Characterize. Bucket traffic by prompt length, output length, concurrency, prefix-reuse rate, and task type.
- Set gates. Define latency percentiles, quality thresholds, availability, and a cost target.
- Tune scheduling. Test continuous batching, active-sequence limits, chunked prefill, and context limits.
- Exploit reuse. Add prefix caching and cache-aware routing where repeated prefixes justify memory and routing complexity.
- Reduce precision. Benchmark weight quantization, then KV-cache quantization for long-context or high-concurrency workloads.
- Optimize decode. Test speculative decoding with an acceptance-rate dashboard and a clear cost accounting for the draft process.
- Optimize execution. Evaluate compilation and specialized runtimes, including warm-up and rollback behavior.
- Consider architecture changes. Test prefill/decode disaggregation only after phase-level measurements show a persistent imbalance.
- Canary and monitor. Release to a small traffic slice, compare against the baseline, and automatically roll back on SLO or quality violations.
Common failure modes and fixes
Throughput rises but users wait longer
The batch or concurrency limit is probably increasing queueing. Inspect queue wait and p95/p99 TTFT, reduce the active-sequence limit, or add capacity.
TTFT improves but TBT worsens
Prefill work may be prioritized at the expense of decode, or cache reuse may be increasing concurrency pressure. Separate phase metrics, adjust scheduler fairness, and check KV-cache occupancy.
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Requests may differ in a supposedly shared prefix, or routing may scatter them across workers. Normalize only inputs that are semantically identical, verify cache-key fields, and use locality-aware routing.
Quantization lowers cost but damages task quality
Move to a less aggressive format, recalibrate, keep sensitive layers at higher precision, or route quality-critical tasks to the baseline model. Do not judge quality from perplexity alone when production tasks require tools, schemas, or factual retrieval.
Speculative decoding adds cost
Inspect acceptance by token position and draft utilization. A smaller or better-matched draft, fewer proposed tokens, or disabling speculation for workloads with low acceptance may be better.
Optimization works in a lab but fails in production
Production may have different prompt lengths, bursts, cancellations, cache locality, model adapters, or autoscaling churn. Replay real traces, include tail latency, and measure warm and cold paths.
What “good” looks like
The efficient configuration is workload-specific. One service may win with prefix caching and cache-aware routing because its system prompt repeats thousands of times. Another may gain more from KV-cache quantization because long contexts dominate memory. A third may need smaller batches to protect TTFT even though larger batches produce more tokens per second.
Karl Weinmeister, Director of Developer Relations at Google Cloud, summarized the trade-off this way: “Every dollar that you spend on model inference buys you a position on a graph of latency and throughput.” Treat that graph as a constrained frontier: move toward lower cost only while the required latency and quality remain inside their limits.
Recheck prices, service availability, runtime support, and hardware economics for the exact deployment. Vendor and case-study results are not independent apples-to-apples benchmarks, and no single setting should be generalized across providers, models, or traffic patterns.
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