Ollama Benchmark
Benchmark local LLM throughput via Ollama across hardware
A no-nonsense tool for anyone serious about running local LLMs. It fills a critical gap by providing standardized, community-validated throughput data that helps avoid expensive hardware mistakes.
- LLM developers optimizing local inference speed
- AI engineers selecting hardware for local deployment
- Researchers comparing model performance across systems
- Hobbyists running local LLMs on personal hardware
- Users needing cloud-based model comparisons
- Those seeking latency or memory benchmarks only
- Non-technical users uncomfortable with command-line tools
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In short
Ollama Benchmark — Benchmark local LLM throughput via Ollama across hardware. Best for LLM developers optimizing local inference speed, AI engineers selecting hardware for local deployment, Researchers comparing model performance across systems. Free to use.
Viability Score
How likely is Ollama Benchmark to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Single-command benchmark execution via CLI
- Measures throughput (tokens per second) of local LLMs via Ollama
- Automatic result submission to community database
- Comparison of results across hardware configurations
- Supports macOS, Linux, and Windows platforms
- Installable via pip or uv
- Open-source under MIT license
- Community-contributed benchmarks from real hardware
- Site and database updated regularly (last 2026-07-03)
About Ollama Benchmark
LLM Benchmark is an open-source CLI tool that measures the throughput (tokens per second) of local large language models running through Ollama. It provides a standardized way to assess how different hardware—Apple Silicon, NVIDIA GPUs, various CPUs—handle LLM inference, enabling data-driven hardware and model selection. The tool executes benchmarks with a single command and automatically submits results to a community database, allowing users to compare their setup against others. Unlike synthetic tests, it uses real workloads and crowdsourced data from actual hardware, making it practical for developers, researchers, and AI engineers optimizing local AI deployments. Available for macOS, Linux, and Windows, LLM Benchmark simplifies performance testing with a minimal CLI interface. Users install via pip or uv and run benchmarks quickly, with results displayed in a public repository. The project is actively maintained (last site update July 2026) and focuses on providing current throughput data for popular local LLMs. What sets LLM Benchmark apart is its community-driven approach: every contributed benchmark adds to a growing dataset that helps users make informed decisions about hardware-model combinations. It is free and open-source under MIT license, with no paid tiers or rate limits, making it accessible to anyone running local AI. The tool is particularly valuable for hobbyists, AI engineers, and researchers seeking reproducible, real-world performance comparisons. By filling a gap where reliable local LLM performance data is scarce, it positions itself as a practical complement to synthetic benchmarks like lm-evaluation-harness.
Behind the Verdict
LLM Benchmark is refreshingly simple: one command, one metric (tokens per second), and a public leaderboard. It's not trying to be a full model evaluation suite—it's a focused hardware benchmarking tool for Ollama users. We'd reach for this when deciding between an M2 Max and an RTX 4090 for running Llama 3 or Mistral locally. The community database is its killer feature. Instead of relying on vendor specs or synthetic tests, you get real numbers from real setups. In practice, that means you can see that an M1 Ultra with 64GB handles 7B models at about 50 tok/s while an RTX 3090 does 80 tok/s—helpful data that's hard to find elsewhere. Where it bites: it only works with Ollama, so if you use llama.cpp directly or other runners, you're out of luck. Also, throughput is only one metric—latency, memory usage, and power draw matter too, but aren't measured. The CLI-only interface will deter non-technical users. Compared to the closest alternative, Ollama's own benchmark flag (ollama run --verbose), LLM Benchmark offers structured output and automatic community submission. It's a better fit for systematic comparison across multiple models or hardware, especially if you want to share results. For developers optimizing edge deployment or hobbyists building a home server, this is a straightforward recommendation. For enterprises needing a full MLPerf-style benchmark or cloud inference comparisons, look elsewhere.
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Use Cases
- Compare throughput of different local LLMs on your hardware to select the best model for your use case.
- Identify the most cost-effective GPU or CPU for running local inference in production.
- Benchmark your setup before deploying an LLM at the edge to ensure performance meets requirements.
- Contribute your results to help the community understand real-world hardware performance.
- Validate hardware upgrades by measuring throughput improvements before and after changes.
Limitations
- Only runs locally via Ollama; does not benchmark cloud models.
- Requires some CLI familiarity.
- Community data quality depends on user contributions.
Integrations
Resources & Guides
Official links
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