Calculator Inputs
Example Data Table
| Scenario | Input Tokens | Output Tokens | Batch Size | Queue Delay (ms) | TTFT (ms) | Total Latency (ms) |
|---|---|---|---|---|---|---|
| Interactive Chat | 1200 | 220 | 2 | 20 | 462 | 1988 |
| Agent Response | 1800 | 420 | 4 | 45 | 671 | 3145 |
| Batch Summaries | 3000 | 700 | 8 | 80 | 913 | 4224 |
| Long Context QA | 6000 | 350 | 3 | 35 | 1640 | 4048 |
Formula Used
Effective Prefill Throughput = Base Prefill Throughput × Utilization Factor × Batch Factor × Cache Gain ÷ Concurrency Penalty
Effective Decode Throughput = Base Decode Throughput × Utilization Factor × Decode Batch Factor × Cache Gain ÷ Concurrency Penalty
Prefill Time = Input Tokens ÷ Effective Prefill Throughput × 1000
Decode Time = Output Tokens ÷ Effective Decode Throughput × 1000
TTFT = Queue Delay + Network Overhead + Serialization Overhead + Framework Overhead + Warmup Overhead + Prefill Time
Total Latency = TTFT + Decode Time
P95 Latency = Total Latency × (1 + Tail Jitter)
P99 Latency = Total Latency × (1 + Tail Jitter × 1.6)
Requests Per Minute = 60000 ÷ Total Latency
How To Use This Calculator
- Enter a model profile name for your scenario.
- Add expected input and output token counts.
- Enter your measured base prefill and decode throughput.
- Set batch size and concurrent request load.
- Add queue, network, serialization, framework, and warmup overheads.
- Estimate cache hit rate, cache speedup, and hardware utilization.
- Set tail jitter and your latency SLO target.
- Click calculate to view TTFT, total latency, tail estimates, throughput, and SLO status.
- Use the CSV button to export the result summary.
- Use the PDF button to save a printable report from your browser.
Model Inference Latency Guide
Why latency matters in AI serving
Model inference latency directly affects product quality. Slow responses reduce engagement. They also increase infrastructure pressure. This calculator helps estimate end to end latency before deployment. It combines queue delay, token throughput, warmup cost, and response generation time. Teams can test realistic traffic assumptions in one place. That improves planning for interactive chat, search assistants, summarization, and batch generation workloads. Better estimates lead to cleaner capacity decisions and safer service level targets.
What shapes time to first token
Time to first token is often the most visible latency metric. Users notice initial delay before they notice full completion time. TTFT depends on input length, prefill throughput, queue delay, framework overhead, and network cost. Large prompts can raise prefill time sharply. Cold starts also add noticeable delay. This is why prompt size control, caching, and warm worker pools matter. The calculator shows how these factors combine into one operational number.
Why decode speed and batching matter
After the first token appears, decode speed controls how fast the model finishes. Output length has a major effect here. Batch size can improve hardware efficiency, but it can also create queue pressure under heavy concurrency. That tradeoff matters for production AI inference. By testing batch size, utilization, and concurrent requests together, you can compare fast interactive settings with high throughput settings. This supports more balanced ML serving decisions.
How to use the estimates
Use this model inference latency calculator during benchmarking, vendor review, and release planning. Start with measured throughput from your serving stack. Then add operational overhead from gateways, serializers, orchestration layers, and traffic spikes. Compare total latency against your SLO target. Review P95 and P99 values to understand tail risk. Export the results for reports or sprint reviews. Small improvements in prompt design, caching, and concurrency control can produce meaningful latency gains.
Where teams improve latency
Most latency wins come from a few repeatable changes. Reduce unnecessary prompt tokens. Reuse cached context whenever possible. Keep worker processes warm. Tune batch size for your product goal. Interactive tools usually need lower queueing. Offline jobs can accept larger batches. Measure latency with realistic traffic, not isolated lab runs. When you track TTFT, total latency, and tail latency together, you make stronger optimization decisions for model serving.
FAQs
1. What does this calculator estimate?
It estimates time to first token, decode time, total inference latency, tail latency, end to end throughput, and basic SLO alignment for an AI serving scenario.
2. What is TTFT?
TTFT means time to first token. It measures how long users wait before the first generated token appears. It combines overhead and prefill processing time.
3. Why are input and output tokens separated?
Input tokens mostly affect prefill latency. Output tokens mostly affect decode latency. Splitting them gives a more realistic estimate for transformer based inference workloads.
4. How does batch size affect latency?
Batching can improve hardware efficiency and throughput. However, large batches can also increase queue delay and hurt interactive response times when traffic rises.
5. Why include queue and network delay?
Real production latency is not only model compute time. Queueing, gateways, serialization, and network hops often create a meaningful share of end to end delay.
6. What does cache hit rate mean here?
It represents how often cached prompt state or reusable context reduces work. A higher hit rate can lower prefill cost and improve practical serving speed.
7. Are P95 and P99 values exact?
No. They are directional estimates based on your jitter input. Use them for planning, then validate them with load tests and production telemetry.
8. Can this calculator help with SLO planning?
Yes. It compares estimated total latency with your target. That makes it useful for capacity planning, optimization reviews, and rollout readiness checks.