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Queryable GPU inference benchmarks from Poor Paul's Benchmark (PPB). Quantitative tools: recommend_quantization, query_ppb_results, get_gpu_headroom, list_tested_configs, compare_quants_quantitative, get_combined_scores, rank_by_priority, recommend_hardware, explain_result. Qualitative tools: get_qualitative_summary, query_qualitative_results, get_context_rot_breakdown, get_tool_accuracy_breakdown, compare_quants_qualitative. Data source: https://huggingface.co/datasets/paulplee/ppb-results
Strong foundation with 14 well-named tools following verb_noun convention (list_, query_, get_, recommend_, compare_, rank_, explain_). All tools have descriptions (avg ~120 chars, within 10-1024 baseline). Input schemas present for all tools with typed parameters and descriptions. However, output schemas are not documented in source code, only inferred from tool names and docstrings. Error handling is minimal; no recovery guidance or actionable error messages visible. Tool descriptions lack WHEN-to-use context and dependency hints. Some parameter descriptions are terse (e.g., 'Partial match on GPU name' lacks format/constraint details). No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite all tools being READ_ONLY.
Side-by-side qualitative comparison across quantizations for a single model+gpu.
Side-by-side quantitative comparison across quantizations for a single model+gpu.
Explain why a particular benchmark result is what it is.
Get quantitative + qualitative metrics for one (gpu, model, quant) configuration.
Get detailed context-rot scores broken down by context length, depth position, and needle type.
Calculate VRAM headroom and maximum safe concurrent users for a model+quant on a GPU.
Get all available qualitative scores (context-rot, tool accuracy, answer quality, multi-turn) for a model+quant combination.
Output schemas not documented. Tool descriptions infer return types (e.g., 'ToolAccuracyBreakdown') but no formal schema definitions visible in source. LLMs cannot plan downstream calls or extract fields reliably.
No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite all 14 tools being READ_ONLY. Agents cannot infer safety properties without explicit hints.
Parameter descriptions lack format/constraint details. E.g., 'Partial match on GPU name' does not specify case sensitivity, max length, or matching algorithm. 'Exact quantization label' lacks enum values or valid examples.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | C | 64 | <=2025-11-25 | v2 |
Get detailed tool-call accuracy metrics for a model+quant. Measures whether the model produces valid JSON tool calls with correct tool selection and parameter values.
List all tested GPU, model, and quantization combinations in the dataset.
Query quantitative benchmark results with flexible filtering.
Query qualitative benchmark results with filtering by model, quantization, GPU, and runner type.
Rank quantizations for a model+gpu by a priority metric (throughput, latency, quality, or balanced).
Recommend GPU hardware options for a target model and workload.
Recommend the best quantization for a model on a GPU given VRAM and concurrency constraints.
No error handling or recovery guidance visible. Tools return empty results on no match (e.g., get_tool_accuracy_breakdown returns empty ToolAccuracyBreakdown) without telling LLM what to try next.
Tool descriptions lack WHEN-to-use context and dependency hints. E.g., 'recommend_quantization' does not explain when to call it vs 'query_ppb_results' or 'rank_by_priority'. No guidance on multi-step workflows.