Compute:Arena
Community submitted local AI benchmarks
Details
- External ID
- 49737278
- Source
- HN
- Company
- —
- Product
- Compute:Arena
- Website domain
- computearena.ai
- Launched
- Sept. 17, 2026
- Cohort
- —
- Upvotes
- 5
- Upvotes percentile
- 0.12998405103668262
- Tags
- —
- Fetched at
- Sept. 21, 2026, 5:02 p.m.
- Updated at
- Sept. 21, 2026, 5:02 p.m.
Description
Benchmarking AI models on real hardware is way harder than it looks. Between AMD, NVIDIA, Apple Silicon, Intel, and Qualcomm, plus hundreds of open source models and quants, getting the test bench right is a challenge.So we're making it dead simple. We're open sourcing our internal testing harness. Anyone can run open source models on their own hardware and submit results to the public leaderboard.It's live now, with a few hundred submissions already.If you want to know how a specific model performs on a given hardware, chances are the data is already there.This is the same tool we use internally to track model and chip performance. Try it out and tell us what to improve
Enrichment
- Theme
- lightweight and on-device AI runtimes
- Vertical
- Horizontal
- Function
- Observability & eval
- Audience
- Developer
- AI stance
- AI-native
- Project type
- Hobby / open-source project
- Normalized one-liner
- crowdsourced benchmarks for local ai hardware
- Manually corrected
- False
Could you build this?
Partial A web leaderboard and database for benchmark results is easy to vibe-code, but the local hardware profiling agent requires cross-platform benchmarking harnesses across Metal, ROCm, and CUDA.
What it would actually take: Requires a Python/C++ CLI harness that wraps llama.cpp and native runtimes, captures signed hardware telemetry (Metal, CUDA, ROCm), and standardizes prefill/decode timing benchmarks. Requires deep understanding of low-level LLM inference engines, tokenization metrics, and cryptographic result verification.
Discussion
2 comments analyzed.
Concerns raised: unfamiliar models on front page
Competitors
Other products that read as similar to this one — 126 launches clear the similarity bar, closest 8 shown.
Attention rank: #109 of 127 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 317 days after the earliest competitor.
- Compute:Arena · ph · 2026-09-17 · 78 upvotes · similarity 0.67
- local-ai-recipe-kit · github · 2026-09-29 · 12 upvotes · similarity 0.54
- PantheonGPU · hn · 2026-08-18 · 13 upvotes · similarity 0.54
- Benchmark Registry · ph · 2026-09-30 · 1 upvotes · similarity 0.50
- local-enough · github · 2026-09-29 · 8 upvotes · similarity 0.50
- benchboard · github · 2026-09-18 · 18 upvotes · similarity 0.49
- The AI Leaderboard · ph · 2026-09-11 · 1 upvotes · similarity 0.49
- Open Benchmarks Grants– a $3M commitment to close the AI eval gap · hn · 2026-02-11 · 6 upvotes · similarity 0.47
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a observability & eval tool for Media & entertainment yet.