⏩ Speculative Decoding Speedup Calculator
Estimate LLM latency and speedup from draft tokens, acceptance rate, draft throughput, target verification time, and output length.
Calculate Speculative Decoding Speedup
What this speculative decoding speedup calculator calculates
Speculative decoding uses a faster proposer to draft multiple tokens and lets the target model verify them together. Accepted tokens reduce the number of serial target-model decoding steps while preserving the target distribution under a correct implementation.
Actual gains depend on acceptance length, proposal overhead, target verification cost, hardware, traffic level, batch size, and method. Some deployments see latency benefits at low or medium QPS but smaller throughput gains under saturated batching.
Speculative Decoding Speedup Calculator formula
Assumptions and limitations
The result is an engineering estimate based on the values entered. Model architectures, tokenizers, frameworks, accelerators, parallel strategies, billing rules, and production traffic differ. Validate important decisions with measured data from the exact model, runtime, hardware, and workflow you intend to use.
Worked example
For 500 output tokens, a four-token draft, 70% average acceptance, a fast draft model, and 35 ms target verification blocks, the estimated number of serial target iterations falls sharply. The speedup disappears if draft generation or verification overhead is too large.
How to interpret and use the result
Use this calculator to screen configurations, then benchmark the exact model and workload. Compare time to first token, inter-token latency, total latency, GPU utilization, and throughput at several QPS levels. Acceptance can change with temperature, domain, draft model quality, and output style.
- Start with measured or representative production assumptions.
- Run conservative, expected, and optimistic scenarios.
- Record model version, framework, precision, hardware, and review date.
- Replace estimates with observed p50 and p95 values after testing.
Common mistakes to avoid
- Assuming acceptance percentage alone determines speedup.
- Ignoring the draft model’s memory and compute cost.
- Applying low-QPS latency results to a high-QPS continuously batched server.
Methodology references
These sources describe the underlying technology. The calculator remains an approximation because implementations and workloads differ.
Frequently asked questions
Is speculative decoding lossless?
Correct rejection-sampling implementations are designed to preserve the target model distribution, though numerical and runtime behavior should still be tested.
Why can a high acceptance rate still be slow?
A slow draft model or expensive verification step can consume more time than the serial target steps it replaces.
Should I maximize draft length?
Not automatically. Longer drafts can waste work when later tokens are rejected, so tune proposal length with acceptance and verification cost.
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Methodology and privacy
This educational tool uses the formula and assumptions displayed on the page. Calculations run locally in your browser, and the page does not transmit the values you enter. Results are estimates rather than provider quotes, benchmark guarantees, financial advice, or capacity guarantees. Last methodology review: August 2, 2026.
Formula Explorer connections
Interpretation: This formula describes how token load, memory and throughput constrain an online model deployment. Assumption: Tokenizer, model architecture, batching, queueing, runtime, hardware and traffic distribution must match the intended production system.