paulplee/ppb-results

Poor Paul's Benchmark Results

Community-submitted LLM inference benchmark data from real consumer, prosumer, and small-business hardware.

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
• Query via AI: any MCP client connected to mcp.poorpaul.dev can answer questions from this data directly

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Files

File	Description
data/results_*.jsonl	Raw benchmark submissions, one file per run, appended continuously
llms.txt	Machine-readable summary for LLM context injection

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Quick start

import pandas as pd
from datasets import load_dataset

# Stream all rows (recommended for large queries)
ds = load_dataset("paulplee/ppb-results", streaming=True, split="train")
df = pd.DataFrame(iter(ds))

# Filter to throughput rows for a specific GPU
tput = df[
    (df["gpu_name"] == "NVIDIA GeForce RTX 5090") &
    (df["runner_type"] == "llama-bench")
][["model_base", "quant", "n_ctx", "throughput_tok_s"]]

Or via DuckDB directly from HuggingFace:

SELECT gpu_name, model_base, quant, concurrent_users,
       AVG(throughput_tok_s) AS mean_tok_s,
       COUNT(*)              AS n
FROM   read_parquet('hf://datasets/paulplee/ppb-results/data/*.jsonl')
WHERE  runner_type = 'llama-bench'
GROUP  BY ALL
ORDER  BY mean_tok_s DESC;

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Schema (v0.9.0)

All columns are present on every row. Fields that do not apply to a given runner are null.

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

LLM engine

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
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

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.

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

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

Performance — power

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

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)

Performance — user experience (server runners only)

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)

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

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

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)
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

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

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

Extensibility

Column	Type	Description
tags	string|null	Free-form JSON string for arbitrary metadata from the suite TOML

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Null value guide

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

Qualitative columns are populated only when run_type is qualitative or all.

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Deduplication

Use fingerprints to control for duplicates in analysis:

# Exact duplicate rows (same result, same machine, same run)
df.drop_duplicates(subset=["result_fingerprint"], inplace=True)

# Latest run per (gpu, model, quant, n_ctx, concurrent_users) config
latest = (
    df.sort_values("timestamp")
      .drop_duplicates(subset=["run_fingerprint"], keep="last")
)

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Ecosystem

Benchmark tool	poor-pauls-benchmark — run benchmarks and contribute results
MCP server	ppb-mcp — lets any MCP-compatible LLM client query this dataset directly
Analytics	poorpaul.dev/insights — leaderboard and visual analysis

Connect any MCP client to https://mcp.poorpaul.dev/mcp to query this data conversationally.

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Contributing results

1. Clone poor-pauls-benchmark
2. Configure suites/my_gpu.toml with your hardware and models
3. Run: uv run ppb.py all suites/my_gpu.toml
4. Results are pushed here automatically — no PR required

No hardware contribution is too small. Every GPU tier that's missing from this dataset is a blind spot for the community.

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License

Dataset content: CC BY 4.0 — contributions are attributed to their submitters.

Tooling: MIT — see the benchmark repository.

Third-party evaluation data included in rows:

• BFCL v4 evaluation cases © UC Berkeley, CC BY 4.0
• MT-Bench questions © LMSYS, MIT licence
• ShareGPT prompts under their original licence