Compute:Arena

Base Compute's open-source leaderboard and CLI for benchmarking local inference across models, quantizations, chips, and runtimes.

Base Compute · Evaluation, observability, and MLOps tooling · tool

Published in Feed

Overview

Base Compute released Compute:Arena on September 22, 2026, a public leaderboard and CLI for measuring local inference performance across the combination of model, quantization format, hardware chip, and runtime. It measures prefill throughput (128-16,384 tokens) and decode throughput (128 tokens), records model file hashes, quantization formats, and chip specs, and generates signed local reports capturing telemetry like temperature, power state, and memory pressure. It supports two runtimes, BaseRT for .base bundles and llama.cpp for .gguf files, verifies benchmarks offline before submission, and signs reports with Ed25519 to ensure integrity.

This is a sourced directory entry. Features and capabilities are reported in the linked sources; this entry does not document independent Aymorphic testing.

Key features

  • Benchmarks the combined model/quantization/chip/runtime 'four-dimensional problem' rather than one axis at a time
  • Measures prefill (128-16,384 tokens) and decode (128 tokens) throughput
  • Captures telemetry: temperature, power state, memory pressure
  • Ed25519-signed local reports for verifiable, tamper-evident results
  • Supports BaseRT (.base) and llama.cpp (.gguf) runtimes

Use cases

  • Comparing local inference performance across hardware before deployment
  • Verifying quantization/runtime tradeoffs with signed, reproducible reports
  • Public leaderboard benchmarking for local/edge inference setups

Pricing

Free and open source under the Apache 2.0 license; CLI available at computearena.ai.

Pricing reflects the source record and may change. Confirm current terms with the provider.

Dates and sources

Released
22 Sept 2026
Record updated
24 Sept 2026
Record last checked
24 Sept 2026

Explore the Feed

Other entries filed under evaluation, observability, and mlops tooling; inclusion does not imply equivalent features.

Browse the Feed