Build a Local Open-Model Speculative Decoding Configuration Planner

Benchmark draft and target open-weight model pairings under local vLLM serving. Synthesize speedup expectations and memory trade-offs using hosted Claude, outputting validated deployment configuration schemas.

Inference Optimization Specialist

Ingest model specifications for proposed target and draft open-weight models using pandas to structure parameter data.

Simulate theoretical latency reductions per decoding step using the formula: Speedup = 1 + (draft_tokens * acceptance_rate) - verification_overhead.

Invoke hosted Claude via Anthropic API to analyze memory overhead versus throughput acceleration, validating schemas with Pydantic.

Generate a deployment configuration payload specifying speculative draft steps and worker memory allocation.

Interactive configuration builder yielding verified YAML parameters for open serving frameworks.

Benchmark records of token acceptance rates and latency profiles across draft-target open-weight pairings. Starter examples below are synthetic. They are not downloaded records.

vLLM open-model serving Leverages vLLM speculative decoding mechanisms (n-gram, draft model serving) to evaluate serving performance.

mastra-ai, anthropic, pandas, pydantic. Mastra AI orchestrates the evaluation workflow, chaining draft model sizing with Claude reasoning tasks. Hosted Claude accessed through official SDK; draft/target models are open-weights only.

Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.

Calculates net speedup factor strictly as: 1 + (draft_tokens * assumed_acceptance_rate) - verification_overhead.

Accounts for additional memory footprint by summing target size, draft size, and a static 20GB KV-cache overhead.

Emits valid serving configuration blocks with Claude-generated optimization notes validated by Pydantic.

```json [ { "input": { "target_gb": 140, "draft_gb": 2, "assumed_acceptance_rate": 0.8, "draft_tokens": 5, "verification_overhead": 0.5 }, "expected_output": { "speedup_factor": 4.5, "required_vram_gb": 162, "viable": true, "configuration_recommendation": "Use 5 draft tokens per speculative step. Synthetic simplified record." } }, { "input": { "target_gb": 140, "draft_gb": 16, "assumed_acceptance_rate": 0.6, "draft_tokens": 4, "verification_overhead": 0.4 }, "expected_output": { "speedup_factor": 3, "required_vram_gb": 176, "viable": false, "configuration_recommendation": "Memory requirement exceeds limits. Synthetic simplified record." } } ] ```

Simulates speculative decoding configurations without deploying unmonitored local serving daemons. Use mock external writes. Require human review before any real deployment.