LLM Latency Budget Planner: Hit Your Response Time SLA
This free LLM latency budget planner breaks total response time into its two structurally different phases, prefill and decode, plus network overhead, and checks the result against an SLA target you set. It is built for solution architects who need to know whether a proposed model and hardware combination can actually hit a committed response time before that commitment gets made. Enter prompt length, prefill speed, decode speed, expected output length, and overhead, and the tool returns time to first token, total response time, and your exact margin against target.
Your numbers
Full input length including system prompt and retrieved context, processed during the prefill phase.
Prompt tokens are processed in parallel and run far faster than decode, typically 20-60x decode speed.
Per-user generation speed once the first token has been produced.
Typical generated response length for this use case.
Round-trip network latency plus any time spent waiting in a request queue before processing starts.
The response time you have committed to, or want to commit to, for this use case.
Your results
Planning estimates only. Real latency varies with concurrent load, prompt content, and serving engine scheduling. Load-test under realistic concurrency before finalizing an SLA commitment.
Get your full latency budget report
We will email you a personalized latency breakdown across model and hardware options against your SLA target, and a Netray inference specialist will follow up with a benchmarking plan.
No spam. Your results stay private. Unsubscribe anytime.
Why prefill and decode need separate budgets
Prefill processes the entire prompt in parallel across GPU cores and is compute-bound, running dramatically faster per token than decode, which is memory-bandwidth-bound and generates one token at a time. This structural difference means a long prompt with a short answer and a short prompt with a long answer can have wildly different latency profiles despite similar total token counts. Time to first token is dominated by prefill and network overhead; total response time adds decode time on top, which scales with output length and per-stream decode speed under current load.
- Prefill runs 20-60x faster per token than decode because it processes the whole prompt in parallel.
- Time to first token is what users judge as responsiveness, even when total response time is longer.
- Decode speed per stream drops as concurrent load rises, so latency budgets must reflect peak concurrency, not idle-GPU benchmarks.
- Streaming responses make total response time less critical than time to first token, since users read as tokens arrive.
Setting realistic SLA targets
Under 500 milliseconds to first token feels instant. One to two seconds feels responsive. Beyond four seconds, users start assuming something is broken, regardless of how fast generation is once it begins. For streaming interfaces, total response time matters less than sustained decode speed staying above roughly 15-20 tokens per second, which is approximately human reading pace; faster than that reads as smooth even if total generation takes several seconds. For non-streaming batch or API-style integrations where the caller waits for the complete response, total response time is what matters and should be the SLA target.
What to change when margin is negative
A negative margin means the current model and hardware combination cannot hit your target at the assumed decode and prefill speeds. The fix depends on which phase dominates: if time to first token is the problem, reduce prompt length through better retrieval and reranking, or add prefill compute. If generation time is the problem, quantize the model, use a smaller model, add speculative decoding, or accept a lower per-stream decode speed target by reducing concurrent load per GPU. Rarely is the answer simply more of the same hardware; it is usually a targeted change to whichever phase is over budget.
- Time-to-first-token shortfall: shrink prompt length via better retrieval, or add prefill throughput.
- Generation-time shortfall: quantize the model, use speculative decoding, or reduce concurrent load per GPU.
- Both phases over budget together usually means the model is oversized for the latency target and a smaller model is the answer.
How Netray designs latency-critical AI systems
Netray builds AI assistants for shop-floor and field-service use cases where response time directly affects whether workers adopt the tool at all. We benchmark prefill and decode separately under realistic concurrent load, not idle-GPU best cases, and design retrieval pipelines that keep prompts lean specifically to protect time to first token. Engagements typically include a latency budget review before any model or hardware commitment is finalized.
Frequently Asked Questions
Why is time to first token more important than total response time?
Because it is what a user experiences as the system deciding to respond at all. A streaming interface that starts showing tokens within a second feels fast even if the full answer takes eight seconds to complete, because the user is reading as it arrives. A system that shows nothing for four seconds before the first token appears feels broken even if total generation only takes two seconds after that. Optimize time to first token first; it drives perceived quality more than any other single metric.
How does concurrent load change these latency numbers?
Decode speed per stream drops as more concurrent sequences share the same GPU, because continuous batching divides available compute and memory bandwidth across more work. A stream that decodes at 60 tokens per second in isolation might drop to 25-35 tokens per second at realistic peak concurrency. Always benchmark and budget latency at your expected peak load, not at idle or lightly loaded conditions, or your SLA will fail exactly when it matters most.
Does a bigger context window hurt time to first token?
Yes, directly and proportionally to how much of that window you actually use. Prefill time scales with prompt token count, so a 10,000-token prompt takes roughly five times longer to prefill than a 2,000-token prompt at the same prefill speed. This is a strong argument for aggressive retrieval reranking that keeps only the highest-value context in the prompt, rather than stuffing the window simply because the model technically supports it.
What is a reasonable network and queue overhead assumption?
For same-datacenter or on-prem deployments with a lightly loaded request queue, 20-80 milliseconds is typical. Add substantially more if requests can queue behind other work: a request arriving when the GPU is already saturated with the maximum concurrent batch will wait for a scheduling slot before processing even begins, and that queue wait can dwarf actual processing time during traffic spikes if admission control is not configured carefully.
Get a latency budget validated under realistic concurrent load before you commit to an SLA.
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