Community benchmark directory
Browse submitted local-AI throughput results across 526 submissions, 36 models, 16 chips, and 8 contributors, according to the site’s displayed totals.
ComputeArena
Freemium
ComputeArena is a community benchmark directory for measuring local AI model throughput across chips, quantisations, and runtimes. It helps developers run offline benchmarks, inspect signed reports, and compare decode and prefill performance on compatible workloads.
什么是 ComputeArena？
ComputeArena is a community benchmark directory for local AI inference. It collects signed benchmark reports produced on community hardware and organizes them by model, quantisation, chip, runtime, and backend.
The service reports decode and prefill throughput in tokens per second. Its leaderboard focuses on compatible PP512 prefill and TG128 decode workloads, with filters for model, runtime, quantisation, chip, prefill size, and ranking metric. Nonstandard and older reports remain available in model, chip, and profile views.
Users can contribute from macOS or Linux through the ComputeArena CLI. The workflow runs benchmarks offline with BaseRT or llama.cpp, keeps reports on the local machine until review, and requires sign-in only when submitting selected results. A comparison view provides side-by-side analysis of chips, runtimes, or runtime versions using matched benchmark pairs where available.
ComputeArena 能做什么？
Decode and prefill measurements
Inspect token-per-second results for both generation decode and prompt prefill, with measured workloads such as TG128 and PP512 shown alongside the values.
Multi-dimensional leaderboard
Filter and rank compatible configurations by model, runtime, quantisation, chip, prefill size, decode, or prefill performance.
Offline CLI workflow
Install the ComputeArena CLI on macOS or Linux, select BaseRT or llama.cpp, run benchmarks offline, and review saved reports before sharing them.
Matched comparisons
Compare two chips, runtimes, or runtime versions using like-for-like pairs, while the interface calls out differences in benchmark conditions and protocol fields.
Runtime and hardware detail
Results expose runtime versions, backends such as Metal, CUDA, BLAS, Vulkan, or ROCm where reported, quantisation, cooldown, conditioning, and harness schema.
使用场景
“Evaluate local inference hardware”
Use leaderboard filters to examine how a model and quantisation perform on available chips before choosing a local inference setup.
“Compare chips or runtimes”
Select two hardware platforms, runtimes, or runtime versions in Compare to review matched runs and see decode and prefill ratios alongside the underlying conditions.
“Investigate model and quantisation trade-offs”
Browse a model across quantisations and chips to distinguish changes in model weights from differences caused by hardware or execution runtime.
“Contribute reproducible measurements”
Run the CLI benchmark offline on a macOS or Linux machine, inspect the saved report, and submit only the results the contributor wants to publish.
常见问题
What does ComputeArena measure?
It publishes local AI inference throughput, including decode and prefill token rates. Results are associated with a model, quantisation, chip, runtime, and reported benchmark conditions.
Which runtimes are supported?
The documented workflow supports BaseRT and llama.cpp. The comparison data includes runtime versions and backend information where those fields are reported.
Can benchmarks run without an account or network connection?
Yes. The site says benchmarks can run offline and that no account is required for running them. Sign-in is needed when a user wants to submit selected saved reports.
What are PP512 and TG128?
They are the headline workload labels used by the leaderboard: PP512 represents prefill at 512 tokens, and TG128 represents a 128-token decode workload. The measured workload is shown with each result.
Are all comparisons strictly equivalent?
No. ComputeArena identifies like-for-like pairs, but comparisons can still differ in models, quantisations, runtime versions, backends, cooldown, conditioning, workload details, or harness schema. The Compare view displays these differences and warns that ratios may reflect protocol rather than chip performance.
快速信息
- Category
- AI benchmarking and developer tool
- Platforms
- macOS and Linux
- Runtimes
- BaseRT and llama.cpp
- Workflow
- Install CLI, run offline, review reports, then submit selectively
- Displayed coverage
- 526 submissions, 36 models, 16 chips, 8 contributors
- Website
- computearena.ai
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