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README.md

@@ -22,6 +22,7 @@ Community-submitted LLM inference benchmark data from real consumer, prosumer, a
 Each row is one normalized benchmark result: a specific model × quantization × hardware × configuration combination with measured performance metrics.
 
 **Use this to:**
+
 - Compare throughput, latency, and power efficiency across GPU models and quantizations
 - Study how concurrency and context length scale on different hardware
 - Build leaderboards, dashboards, and data-driven GPU purchasing decisions
@@ -31,10 +32,10 @@ Each row is one normalized benchmark result: a specific model × quantization ×
 
 ## Files
 
-| File | Description |
-| ---- | ----------- |
+| File                   | Description                                                        |
+| ---------------------- | ------------------------------------------------------------------ |
 | `data/results_*.jsonl` | Raw benchmark submissions, one file per run, appended continuously |
-| `llms.txt` | Machine-readable summary for LLM context injection |
+| `llms.txt`             | Machine-readable summary for LLM context injection                 |
 
 ---
 
@@ -75,166 +76,166 @@ All columns are present on every row. Fields that do not apply to a given runner
 
 ### Model identity
 
-| Column | Type | Description |
-| ------ | ---- | ----------- |
-| `run_type` | string | `quantitative`, `qualitative`, or `all` |
-| `model` | string | Full model path (e.g. `unsloth/Qwen3.5-9B-GGUF/Qwen3.5-9B-Q8_0.gguf`) |
-| `model_base` | string | Base model name without quant suffix (e.g. `Qwen3.5-9B`) |
-| `quant` | string | Quantization format (e.g. `Q4_K_M`, `Q8_0`, `BF16`) |
-| `model_org` | string\|null | HuggingFace organisation (e.g. `unsloth`); null for local paths |
-| `model_repo` | string\|null | Full HF `org/repo` string; null for local paths |
-| `runner_type` | string | Benchmark backend: `llama-bench`, `llama-server`, or `llama-server-loadtest` |
+| Column        | Type         | Description                                                                  |
+| ------------- | ------------ | ---------------------------------------------------------------------------- |
+| `run_type`    | string       | `quantitative`, `qualitative`, or `all`                                      |
+| `model`       | string       | Full model path (e.g. `unsloth/Qwen3.5-9B-GGUF/Qwen3.5-9B-Q8_0.gguf`)        |
+| `model_base`  | string       | Base model name without quant suffix (e.g. `Qwen3.5-9B`)                     |
+| `quant`       | string       | Quantization format (e.g. `Q4_K_M`, `Q8_0`, `BF16`)                          |
+| `model_org`   | string\|null | HuggingFace organisation (e.g. `unsloth`); null for local paths              |
+| `model_repo`  | string\|null | Full HF `org/repo` string; null for local paths                              |
+| `runner_type` | string       | Benchmark backend: `llama-bench`, `llama-server`, or `llama-server-loadtest` |
 
 ### LLM engine
 
-| Column | Type | Description |
-| ------ | ---- | ----------- |
-| `llm_engine_name` | string\|null | Inference engine (e.g. `llama.cpp`) |
+| Column               | Type         | Description                                              |
+| -------------------- | ------------ | -------------------------------------------------------- |
+| `llm_engine_name`    | string\|null | Inference engine (e.g. `llama.cpp`)                      |
 | `llm_engine_version` | string\|null | Engine version with build hash (e.g. `b5063 (58ab80c3)`) |
 
 ### Hardware
 
-| Column | Type | Description |
-| ------ | ---- | ----------- |
-| `gpu_name` | string\|null | Primary GPU model name |
-| `gpu_vram_gb` | float\|null | Primary GPU VRAM in GB |
-| `gpu_driver` | string\|null | GPU driver version |
-| `gpu_count` | int | Number of GPUs used |
-| `gpu_names` | string\|null | Comma-joined list of all GPU names (multi-GPU runs) |
-| `gpu_total_vram_gb` | float\|null | Total VRAM across all GPUs |
-| `unified_memory` | bool\|null | `true` for Apple Silicon — GPU and CPU share the same memory pool |
+| Column                   | Type         | Description                                                             |
+| ------------------------ | ------------ | ----------------------------------------------------------------------- |
+| `gpu_name`               | string\|null | Primary GPU model name                                                  |
+| `gpu_vram_gb`            | float\|null  | Primary GPU VRAM in GB                                                  |
+| `gpu_driver`             | string\|null | GPU driver version                                                      |
+| `gpu_count`              | int          | Number of GPUs used                                                     |
+| `gpu_names`              | string\|null | Comma-joined list of all GPU names (multi-GPU runs)                     |
+| `gpu_total_vram_gb`      | float\|null  | Total VRAM across all GPUs                                              |
+| `unified_memory`         | bool\|null   | `true` for Apple Silicon — GPU and CPU share the same memory pool       |
 | `gpu_compute_capability` | string\|null | CUDA compute capability (e.g. `"9.0"` for Blackwell); null for non-CUDA |
-| `gpu_pcie_gen` | int\|null | PCIe generation (e.g. `5`); null for unified-memory platforms |
-| `gpu_pcie_width` | int\|null | PCIe link width in lanes (e.g. `16`); null for unified-memory platforms |
-| `gpu_power_limit_w` | float\|null | Configured TDP limit in Watts (from NVML); null for non-NVIDIA |
-| `backends` | string\|null | Compute backend with version (e.g. `CUDA 13.0`, `Metal`, `CPU`) |
-| `cpu_model` | string\|null | CPU model name |
+| `gpu_pcie_gen`           | int\|null    | PCIe generation (e.g. `5`); null for unified-memory platforms           |
+| `gpu_pcie_width`         | int\|null    | PCIe link width in lanes (e.g. `16`); null for unified-memory platforms |
+| `gpu_power_limit_w`      | float\|null  | Configured TDP limit in Watts (from NVML); null for non-NVIDIA          |
+| `backends`               | string\|null | Compute backend with version (e.g. `CUDA 13.0`, `Metal`, `CPU`)         |
+| `cpu_model`              | string\|null | CPU model name                                                          |
 
 ### Benchmark configuration
 
-| Column | Type | Description |
-| ------ | ---- | ----------- |
-| `n_ctx` | int\|null | Context window size in tokens |
-| `n_batch` | int\|null | Batch size for prompt processing |
-| `split_mode` | string\|null | Multi-GPU split strategy (`layer`, `row`, `none`); null for single-GPU |
-| `tensor_split` | string\|null | Per-GPU VRAM weight string (e.g. `"1,1"`); null for single-GPU |
-| `concurrent_users` | int\|null | Number of simulated parallel users. For `llama-server-loadtest`, **each row is one concurrency level** from the measured curve. |
+| Column             | Type         | Description                                                                                                                     |
+| ------------------ | ------------ | ------------------------------------------------------------------------------------------------------------------------------- |
+| `n_ctx`            | int\|null    | Context window size in tokens                                                                                                   |
+| `n_batch`          | int\|null    | Batch size for prompt processing                                                                                                |
+| `split_mode`       | string\|null | Multi-GPU split strategy (`layer`, `row`, `none`); null for single-GPU                                                          |
+| `tensor_split`     | string\|null | Per-GPU VRAM weight string (e.g. `"1,1"`); null for single-GPU                                                                  |
+| `concurrent_users` | int\|null    | Number of simulated parallel users. For `llama-server-loadtest`, **each row is one concurrency level** from the measured curve. |
 
 ### Workload
 
-| Column | Type | Description |
-| ------ | ---- | ----------- |
-| `task_type` | string\|null | Workload category (e.g. `text-generation`, `context-rot-niah`) |
-| `prompt_dataset` | string\|null | Prompt source (e.g. `sharegpt-v3`); null for `llama-bench` |
-| `num_prompts` | int\|null | Prompts sent per run; null for `llama-bench` |
-| `n_predict` | int\|null | Max tokens generated per prompt; null for `llama-bench` |
+| Column           | Type         | Description                                                    |
+| ---------------- | ------------ | -------------------------------------------------------------- |
+| `task_type`      | string\|null | Workload category (e.g. `text-generation`, `context-rot-niah`) |
+| `prompt_dataset` | string\|null | Prompt source (e.g. `sharegpt-v3`); null for `llama-bench`     |
+| `num_prompts`    | int\|null    | Prompts sent per run; null for `llama-bench`                   |
+| `n_predict`      | int\|null    | Max tokens generated per prompt; null for `llama-bench`        |
 
 ### Performance — throughput
 
-| Column | Type | Description |
-| ------ | ---- | ----------- |
-| `throughput_tok_s` | float\|null | Tokens per second (primary throughput metric) |
-| `vram_cliff_tokens` | int\|null | Largest `n_ctx` that loaded without OOM during pre-flight discovery |
+| Column              | Type        | Description                                                         |
+| ------------------- | ----------- | ------------------------------------------------------------------- |
+| `throughput_tok_s`  | float\|null | Tokens per second (primary throughput metric)                       |
+| `vram_cliff_tokens` | int\|null   | Largest `n_ctx` that loaded without OOM during pre-flight discovery |
 
 ### Performance — power
 
-| Column | Type | Description |
-| ------ | ---- | ----------- |
+| Column        | Type        | Description                     |
+| ------------- | ----------- | ------------------------------- |
 | `avg_power_w` | float\|null | Average GPU power draw in Watts |
-| `max_power_w` | float\|null | Peak GPU power draw in Watts |
+| `max_power_w` | float\|null | Peak GPU power draw in Watts    |
 
 ### Performance — thermal
 
-| Column | Type | Description |
-| ------ | ---- | ----------- |
-| `avg_gpu_temp_c` | float\|null | Average GPU temperature (°C) |
-| `max_gpu_temp_c` | float\|null | Peak GPU temperature (°C) |
-| `avg_cpu_temp_c` | float\|null | Average CPU temperature (°C) |
-| `max_cpu_temp_c` | float\|null | Peak CPU temperature (°C) |
-| `avg_fan_speed_rpm` | float\|null | Average fan speed (RPM) |
-| `max_fan_speed_rpm` | float\|null | Peak fan speed (RPM) |
+| Column              | Type        | Description                  |
+| ------------------- | ----------- | ---------------------------- |
+| `avg_gpu_temp_c`    | float\|null | Average GPU temperature (°C) |
+| `max_gpu_temp_c`    | float\|null | Peak GPU temperature (°C)    |
+| `avg_cpu_temp_c`    | float\|null | Average CPU temperature (°C) |
+| `max_cpu_temp_c`    | float\|null | Peak CPU temperature (°C)    |
+| `avg_fan_speed_rpm` | float\|null | Average fan speed (RPM)      |
+| `max_fan_speed_rpm` | float\|null | Peak fan speed (RPM)         |
 
 ### Performance — user experience (server runners only)
 
-| Column | Type | Description |
-| ------ | ---- | ----------- |
+| Column        | Type        | Description                      |
+| ------------- | ----------- | -------------------------------- |
 | `avg_ttft_ms` | float\|null | Average Time-To-First-Token (ms) |
-| `p50_ttft_ms` | float\|null | Median TTFT (ms) |
-| `p99_ttft_ms` | float\|null | 99th-percentile TTFT (ms) |
-| `avg_itl_ms` | float\|null | Average Inter-Token Latency (ms) |
-| `p50_itl_ms` | float\|null | Median ITL (ms) |
-| `p99_itl_ms` | float\|null | 99th-percentile ITL (ms) |
+| `p50_ttft_ms` | float\|null | Median TTFT (ms)                 |
+| `p99_ttft_ms` | float\|null | 99th-percentile TTFT (ms)        |
+| `avg_itl_ms`  | float\|null | Average Inter-Token Latency (ms) |
+| `p50_itl_ms`  | float\|null | Median ITL (ms)                  |
+| `p99_itl_ms`  | float\|null | 99th-percentile ITL (ms)         |
 
 ### Qualitative evaluation
 
 Populated when `run_type` is `qualitative` or `all`. All null for pure `quantitative` runs.
 
-| Column | Type | Description |
-| ------ | ---- | ----------- |
-| `context_rot_score` | float\|null | Mean accuracy across all (length × depth) long-context recall cases |
-| `context_rot_accuracy_by_length` | string\|null | JSON `{haystack_length: accuracy}` map |
-| `context_rot_accuracy_by_depth` | string\|null | JSON `{depth_pct: accuracy}` map |
-| `tool_selection_accuracy` | float\|null | Fraction of cases with correct tool name selected |
-| `parameter_accuracy` | float\|null | Fraction of cases with all required arguments matching ground truth |
-| `parameter_hallucination_rate` | float\|null | Fraction of cases with invented arguments not in schema |
-| `parse_success_rate` | float\|null | Fraction of cases with parseable tool-call JSON |
-| `overall_tool_accuracy` | float\|null | Geometric mean of tool selection × parameter accuracy |
-| `knowledge_accuracy_mean` | float\|null | Mean fraction of factual claims judged consistent with common knowledge |
-| `knowledge_accuracy_std` | float\|null | Standard deviation of per-prompt knowledge-accuracy scores |
-| `answer_relevancy_mean` | float\|null | Mean judge-rated response relevancy (0–1) |
-| `coherence_mean` | float\|null | Mean judge-rated coherence (0–1) |
-| `quality_composite_score` | float\|null | Mean of knowledge accuracy, relevancy, and coherence |
-| `memory_accuracy` | float\|null | LongMemEval recall accuracy (0–1); null when MT-Bench mode was used |
-| `mt_bench_score` | float\|null | MT-Bench score (1–10 scale); null when LongMemEval mode was used |
-| `cases_evaluated` | int\|null | Number of evaluation cases that completed |
-| `cases_skipped_context` | int\|null | Cases skipped because context exceeded `vram_cliff_tokens` |
+| Column                           | Type         | Description                                                             |
+| -------------------------------- | ------------ | ----------------------------------------------------------------------- |
+| `context_rot_score`              | float\|null  | Mean accuracy across all (length × depth) long-context recall cases     |
+| `context_rot_accuracy_by_length` | string\|null | JSON `{haystack_length: accuracy}` map                                  |
+| `context_rot_accuracy_by_depth`  | string\|null | JSON `{depth_pct: accuracy}` map                                        |
+| `tool_selection_accuracy`        | float\|null  | Fraction of cases with correct tool name selected                       |
+| `parameter_accuracy`             | float\|null  | Fraction of cases with all required arguments matching ground truth     |
+| `parameter_hallucination_rate`   | float\|null  | Fraction of cases with invented arguments not in schema                 |
+| `parse_success_rate`             | float\|null  | Fraction of cases with parseable tool-call JSON                         |
+| `overall_tool_accuracy`          | float\|null  | Geometric mean of tool selection × parameter accuracy                   |
+| `knowledge_accuracy_mean`        | float\|null  | Mean fraction of factual claims judged consistent with common knowledge |
+| `knowledge_accuracy_std`         | float\|null  | Standard deviation of per-prompt knowledge-accuracy scores              |
+| `answer_relevancy_mean`          | float\|null  | Mean judge-rated response relevancy (0–1)                               |
+| `coherence_mean`                 | float\|null  | Mean judge-rated coherence (0–1)                                        |
+| `quality_composite_score`        | float\|null  | Mean of knowledge accuracy, relevancy, and coherence                    |
+| `memory_accuracy`                | float\|null  | LongMemEval recall accuracy (0–1); null when MT-Bench mode was used     |
+| `mt_bench_score`                 | float\|null  | MT-Bench score (1–10 scale); null when LongMemEval mode was used        |
+| `cases_evaluated`                | int\|null    | Number of evaluation cases that completed                               |
+| `cases_skipped_context`          | int\|null    | Cases skipped because context exceeded `vram_cliff_tokens`              |
 
 ### Blob columns
 
-| Column | Type | Description |
-| ------ | ---- | ----------- |
-| `qualitative` | string\|null | Full qualitative result payload as JSON string |
-| `quantitative` | string\|null | Full quantitative result payload as JSON string |
-| `meta` | string\|null | Reproducibility hints (e.g. `quality_prompts_cache_hash`) as JSON string |
+| Column         | Type         | Description                                                              |
+| -------------- | ------------ | ------------------------------------------------------------------------ |
+| `qualitative`  | string\|null | Full qualitative result payload as JSON string                           |
+| `quantitative` | string\|null | Full quantitative result payload as JSON string                          |
+| `meta`         | string\|null | Reproducibility hints (e.g. `quality_prompts_cache_hash`) as JSON string |
 
 ### OS / system context
 
-| Column | Type | Description |
-| ------ | ---- | ----------- |
-| `os_system` | string\|null | OS family: `Linux`, `Darwin`, `Windows` |
-| `os_release` | string\|null | Kernel / OS release string |
-| `os_machine` | string\|null | CPU architecture (e.g. `x86_64`, `arm64`) |
-| `os_distro` | string\|null | Distribution name (e.g. `Ubuntu`, `macOS`) |
+| Column              | Type         | Description                                 |
+| ------------------- | ------------ | ------------------------------------------- |
+| `os_system`         | string\|null | OS family: `Linux`, `Darwin`, `Windows`     |
+| `os_release`        | string\|null | Kernel / OS release string                  |
+| `os_machine`        | string\|null | CPU architecture (e.g. `x86_64`, `arm64`)   |
+| `os_distro`         | string\|null | Distribution name (e.g. `Ubuntu`, `macOS`)  |
 | `os_distro_version` | string\|null | Distribution version (e.g. `24.04`, `15.5`) |
-| `cpu_cores` | int\|null | Number of logical CPU cores |
-| `ram_total_gb` | float\|null | Total system RAM in GB |
+| `cpu_cores`         | int\|null    | Number of logical CPU cores                 |
+| `ram_total_gb`      | float\|null  | Total system RAM in GB                      |
 
 ### Submission metadata
 
-| Column | Type | Description |
-| ------ | ---- | ----------- |
-| `submitter` | string\|null | Optional public display name of the contributor |
-| `timestamp` | string\|null | ISO 8601 UTC time the benchmark run produced the row |
-| `submitted_at` | string\|null | ISO 8601 UTC time the row was uploaded |
+| Column         | Type         | Description                                          |
+| -------------- | ------------ | ---------------------------------------------------- |
+| `submitter`    | string\|null | Optional public display name of the contributor      |
+| `timestamp`    | string\|null | ISO 8601 UTC time the benchmark run produced the row |
+| `submitted_at` | string\|null | ISO 8601 UTC time the row was uploaded               |
 
 ### Provenance and deduplication
 
-| Column | Type | Description |
-| ------ | ---- | ----------- |
-| `schema_version` | string | Schema version at time of flattening (`0.9.0`) |
-| `benchmark_version` | string | PPB software version that produced the row |
-| `suite_run_id` | string\|null | UUID shared by all rows from the same `ppb` invocation |
-| `submission_id` | string\|null | UUID assigned during upload |
-| `row_id` | string | UUID uniquely identifying this row |
-| `machine_fingerprint` | string | SHA-256 of hardware profile fields (anonymous machine identity) |
-| `run_fingerprint` | string | SHA-256 of benchmark configuration + machine fingerprint |
-| `result_fingerprint` | string | SHA-256 of run identity + measured metrics — uniquely identifies one result |
-| `source_file_sha256` | string\|null | SHA-256 of the source JSONL file |
+| Column                | Type         | Description                                                                 |
+| --------------------- | ------------ | --------------------------------------------------------------------------- |
+| `schema_version`      | string       | Schema version at time of flattening (`0.9.0`)                              |
+| `benchmark_version`   | string       | PPB software version that produced the row                                  |
+| `suite_run_id`        | string\|null | UUID shared by all rows from the same `ppb` invocation                      |
+| `submission_id`       | string\|null | UUID assigned during upload                                                 |
+| `row_id`              | string       | UUID uniquely identifying this row                                          |
+| `machine_fingerprint` | string       | SHA-256 of hardware profile fields (anonymous machine identity)             |
+| `run_fingerprint`     | string       | SHA-256 of benchmark configuration + machine fingerprint                    |
+| `result_fingerprint`  | string       | SHA-256 of run identity + measured metrics — uniquely identifies one result |
+| `source_file_sha256`  | string\|null | SHA-256 of the source JSONL file                                            |
 
 ### Extensibility
 
-| Column | Type | Description |
-| ------ | ---- | ----------- |
+| Column | Type         | Description                                                      |
+| ------ | ------------ | ---------------------------------------------------------------- |
 | `tags` | string\|null | Free-form JSON string for arbitrary metadata from the suite TOML |
 
 ---
@@ -243,14 +244,14 @@ Populated when `run_type` is `qualitative` or `all`. All null for pure `quantita
 
 Many columns are runner-specific. Expected nulls by runner type:
 
-| Column group | `llama-bench` | `llama-server` | `llama-server-loadtest` |
-| --- | --- | --- | --- |
-| TTFT / ITL metrics | null | populated | populated |
-| `prompt_dataset`, `num_prompts`, `n_predict` | null | populated | populated |
-| `concurrent_users` | null | populated | populated (one row per level) |
-| `gpu_pcie_gen`, `gpu_pcie_width` | null on Apple Silicon | null on Apple Silicon | null on Apple Silicon |
-| `unified_memory` | null on NVIDIA | null on NVIDIA | null on NVIDIA |
-| Qualitative columns | null | null | null |
+| Column group                                 | `llama-bench`         | `llama-server`        | `llama-server-loadtest`       |
+| -------------------------------------------- | --------------------- | --------------------- | ----------------------------- |
+| TTFT / ITL metrics                           | null                  | populated             | populated                     |
+| `prompt_dataset`, `num_prompts`, `n_predict` | null                  | populated             | populated                     |
+| `concurrent_users`                           | null                  | populated             | populated (one row per level) |
+| `gpu_pcie_gen`, `gpu_pcie_width`             | null on Apple Silicon | null on Apple Silicon | null on Apple Silicon         |
+| `unified_memory`                             | null on NVIDIA        | null on NVIDIA        | null on NVIDIA                |
+| Qualitative columns                          | null                  | null                  | null                          |
 
 Qualitative columns are populated only when `run_type` is `qualitative` or `all`.
 
@@ -275,11 +276,11 @@ latest = (
 
 ## Ecosystem
 
-| | |
-| --- | --- |
+|                    |                                                                                                                  |
+| ------------------ | ---------------------------------------------------------------------------------------------------------------- |
 | **Benchmark tool** | [poor-pauls-benchmark](https://github.com/paulplee/poor-pauls-benchmark) — run benchmarks and contribute results |
-| **MCP server** | [ppb-mcp](https://github.com/paulplee/ppb-mcp) — lets any MCP-compatible LLM client query this dataset directly |
-| **Analytics** | [poorpaul.dev/insights](https://poorpaul.dev/insights) — leaderboard and visual analysis |
+| **MCP server**     | [ppb-mcp](https://github.com/paulplee/ppb-mcp) — lets any MCP-compatible LLM client query this dataset directly  |
+| **Analytics**      | [poorpaul.dev/insights](https://poorpaul.dev/insights) — leaderboard and visual analysis                         |
 
 Connect any MCP client to `https://mcp.poorpaul.dev/mcp` to query this data conversationally.
 
@@ -303,6 +304,7 @@ Dataset content: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) — c
 Tooling: MIT — see the [benchmark repository](https://github.com/paulplee/poor-pauls-benchmark).
 
 Third-party evaluation data included in rows:
+
 - BFCL v4 evaluation cases © UC Berkeley, [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
 - MT-Bench questions © LMSYS, [MIT licence](https://github.com/lm-sys/FastChat/blob/main/LICENSE)
 - ShareGPT prompts under their [original licence](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered)