| # Methodology |
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| Smoke24 is a 24-task slice of Terminal-Bench 2.0 designed for fast, repeated comparison of coding-agent behavior. |
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| ## Rationale |
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| The subset was created to answer a practical local iteration problem: can a new |
| model, quantization recipe, context setting, or serving setup be assessed on an |
| RTX 3090-class machine within a couple of hours? |
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| The target is a benchmark that is small enough to run frequently on local |
| hardware, while still exercising real Terminal-Bench agentic tasks. It is meant |
| to provide an early signal on model quality, runtime behavior, token use, parser |
| stability, and failure modes before spending time on broader or more expensive |
| benchmark passes. |
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| The local rows in this project are interpreted under one serving boundary: |
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| - vLLM or LiteLLM residency serving |
| - RTX 3090-class local deployment context |
| - Terminus-2 agent harness |
| - 30 minute task timeout |
| - 32 CPU / 48 GiB sandbox shape |
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| The dashboard emphasizes three classes of signal: |
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| - Quality: verifier success rate on the same 24 tasks. |
| - Local serving cost: LLM API minutes, wall-clock minutes, generated tokens per solved task. |
| - Stability: historical Smoke24 pass variance, parser-warning counts, task-level pass/fail matrix. |
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| ## External References |
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| External public Terminal-Bench rows are reconstructed as equivalent Smoke24 success references. They are not hardware-comparable for local cost or wall-clock metrics, so they should appear as reference lines or success-only tables rather than local-cost scatter points. |
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| ## Variance |
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| Smoke24 is intentionally small. One-task deltas can be meaningful but should not be over-read without historical pass variance. The report keeps score, token, LLM-time, observed decode-speed, exception, and parser-warning variance in separate aggregate files. |
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