README.md

metadata

license: agpl-3.0
tags:
  - tribunus
  - inference
  - benchmarking
  - apple-silicon
  - mlx
  - compute-image
  - evidence

Tribunus Benchmarks

Research data from the Tribunus Compute inference engine. Includes benchmark results, backend comparison data, TurboQuant KV cache compression analysis, ComputeImage compile manifests, and evidence receipts.

Methodology

All benchmarks run on Apple Silicon (M1 Max, 64GB) running macOS 26.5.

• Model: Qwen2.5 0.5B Instruct (NF4 quantized)
• Temperature: 0.0 (greedy)
• Tokens: variable (50-512 per run)
• Warm-up: 3 runs before measurement
• Report: throughput (tok/s), time-to-first-token (TTFT), latency distribution

Hardware

Component	Detail
SoC	Apple M1 Max
RAM	64GB unified
GPU	32-core Metal GPU
ANE	16-core Neural Engine
OS	macOS 26.5
MLX	0.31.2 (tribunus fork)

Datasets

• benchmarks/qwen2.5-0.5b.json — Throughput, TTFT, latency distribution
• benchmarks/backend-comparison.json — MLX GPU vs Accelerate CPU vs Core ML ANE
• compute-images/manifest-qwen2.5-0.5b.json — NF4 quantized, 24 layers, 556 tensors
• evidence/receipt-samples.json — Sample receipt data from inference runs

Results Summary

Phase	tok/s	Notes
Baseline	65	Custom MLX-heavy runtime with heterogeneous scaffolding
Arena/residency	100-160	Zero-copy arena, paged residency
Speculation active	180-280	Speculative decode with ANE draft
Full stack	300+	Draft model, verifier batching, KV transactions

Citation

@software{tribunus_compute_2025,
  author = {Julian Torr},
  title = {Tribunus Compute Benchmarks},
  year = {2025},
  url = {https://huggingface.co/datasets/Tribunus-dev/tribunus-benchmarks}
}

License

AGPL-3.0