Ollama Benchmark
Free open-source CLI to benchmark local LLM throughput via Ollama
A practical, no-cost tool that gives you real tokens-per-second numbers for local LLM inference via Ollama. If you're choosing between GPUs or CPU configs for local models, run this first. The community database adds a useful comparison layer, though it's limited to Ollama-served models and requires some CLI comfort. For a fuller performance picture, pair it with tools like LM Studio or llama.cpp benchmarks, but for a quick, standardized throughput check, this is a solid starting point.
Verified 4d ago · liveness 62/100 · cite: rightaichoice.com/tools/ollama-benchmark
- LLM developers optimizing local inference speed on their own hardware
- AI engineers selecting GPUs or CPU configurations for local deployment
- Researchers benchmarking model variants across different systems
- Hobbyists running local LLMs who want to know if their setup is fast enough
- Users needing cloud-based model performance comparisons
- Those seeking latency or memory benchmarks only
- Non-technical users uncomfortable with command-line interfaces
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Skip Ollama Benchmark if you don't use Ollama, need latency or memory metrics, or prefer a graphical interface—it's a throughput-only CLI tool tied to Ollama.
Ollama Benchmark is completely free, open-source, and MIT licensed. Unlike commercial benchmarking services or paid inference platforms, there are no subscription fees, no usage limits, and no hidden charges. The only cost is your time setting up Ollama and the CLI.
In short
Ollama Benchmark — Free open-source CLI to benchmark local LLM throughput via Ollama. Best for LLM developers optimizing local inference speed on their own hardware, AI engineers selecting GPUs or CPU configurations for local deployment, Researchers benchmarking model variants across different systems. Free to use.
What people actually say about Ollama Benchmark — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
50 mentions across 4 sources (Hacker News, YouTube, GitHub, Lemmy) · researched Aug 15, 2026.
- +Provides a standardized, single-command benchmark for local LLM throughput
- +Crowdsourced database lets you compare against other real hardware
- +Free and open-source with MIT license, installable via pip or uv
- +Works across macOS, Linux, and Windows (when it works)
- +Frequently referenced by notable community members like Jeff Geerling
- −Python 3.13 users hit a warning and possible crash; requires 3.12
- −Network errors (WinError 10049) when pulling models on some systems
- −Crashes on Windows Server 2022 even with high-spec hardware
- −Linux users report a TypeError that halts the benchmark
- −Results are not reliably comparable across different hardware configs
- • None, but time lost to troubleshooting is significant
Viability Score
How well maintained and how widely used is Ollama Benchmark? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Single-command benchmark execution via CLI
- Measures tokens per second throughput of local LLMs via Ollama
- Automatic result submission to community database
- Compare results across hardware configurations
- Supports macOS, Linux, and Windows
- Installable via pip or uv
- Open-source under MIT license
- Community-contributed benchmarks from real hardware
- Results sorted by platform (macOS, Linux, Windows)
- Public web interface to view all benchmarks
About Ollama Benchmark
Ollama Benchmark, also known as LLM Benchmark, is a free, open-source command-line tool that measures the tokens-per-second throughput of local large language models running through Ollama. It provides a single command to run a benchmark on your hardware and automatically submits results to a community database. You can then compare your results across Apple Silicon, NVIDIA GPUs, and various CPU configurations, with results sorted by platform (macOS, Linux, Windows). The tool is installable via pip or uv and is MIT licensed. It's designed for developers, AI engineers, and researchers who need real-world, data-driven decisions about hardware and model selection for local AI inference. Unlike synthetic tests, the community database contains benchmarks from actual hardware setups.
Behind the Verdict
Ollama Benchmark fills a narrow but important niche: giving you a standardized tokens-per-second number for local LLM inference through Ollama, without the fuss of building your own benchmark script. We'd reach for it when we're deciding between two GPUs or whether to upgrade to more RAM for a local model. The single command setup via pip or uv is genuinely painless, and the automatic submission to the community database means you get context on how your hardware stacks up against other real setups, not just synthetic tests. Where it bites: it only benchmarks models served through Ollama, so if you use a different inference engine, this isn't your tool. Also, it's CLI-only, which will turn off non-technical users. Compared to LM Studio or llama.cpp benchmarks, which offer more granular control and metrics like latency, this tool keeps things simple. If you want a quick, repeatable throughput check across your fleet, this is your jam. But if you need deep profiling or memory analysis, look elsewhere. In practice, the community results are only as good as the hardware configurations people submit, so treat comparisons as directional, not gospel. For a zero-cost starting point, it's hard to beat.
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Real-world workflow fit
Concrete scenarios for the personas Ollama Benchmark actually fits — and what changes day-one when you adopt it.
You're choosing between running a 7B model on your M1 Mac vs. a rented RTX 3080 for local inference.
Outcome: You install llm-benchmark via pip, run `llm_benchmark run` on both machines, and compare tokens/sec results to make a data-driven hardware choice.
You're planning an edge deployment on a Jetson and need to pick a model that fits the hardware's throughput.
Outcome: You use Ollama Benchmark to test several model sizes on the Jetson, see which one meets your latency target, and submit results to the community database for reference.
You need to benchmark a new model variant across multiple systems for a paper.
Outcome: You run the benchmark on a few different GPUs and CPUs, collect the tokens/sec numbers, and include a comparison table with community data as a supplement.
Use Cases
- Compare throughput of different local LLMs on your hardware to select the best model
- Identify the most cost-effective GPU or CPU for running local inference in production
- Benchmark your setup before deploying an LLM at the edge
- Contribute results to help the community understand real-world hardware performance
- Validate hardware upgrades by measuring throughput improvements
Limitations
- This tool benchmarks local LLMs via Ollama, requiring Ollama to be installed.
- It is a CLI tool, so users need some familiarity with the command line.
- Benchmarks are community-contributed, so data quality may vary.
as of 2026-08-24
Verification history
We have re-verified Ollama Benchmark 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Ollama Benchmark's pricing actually pencils out — and where peers do it cheaper.
Ollama Benchmark is completely free, open-source, and MIT licensed. Unlike commercial benchmarking services or paid inference platforms, there are no subscription fees, no usage limits, and no hidden charges. The only cost is your time setting up Ollama and the CLI.
Setup time & first value
How long it actually takes to get something useful out of Ollama Benchmark — broken out by persona, not the marketing-page minute.
For a developer familiar with the terminal: under 2 minutes to install via `pip install llm-benchmark` and run the first benchmark. If you need to install Ollama first, add 5–10 minutes. Total time to first value: about 5 minutes for those with Ollama already installed.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Ollama Benchmark
Common stack mates teams adopt alongside Ollama Benchmark, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Ollama Benchmark vs Spider Cloud
Choose Spider Cloud if you need to feed real-time web data into AI agents or RAG pipelines with robust anti-detection and structured output. Choose Ollama Benchmark if you're tuning local LLM deployment and want free, crowdsourced performance data. They solve fundamentally different problems, so pick based on whether your bottleneck is data acquisition or inference speed.
Ollama Benchmark vs Voyage Ai
Choose Voyage AI if you need high-accuracy, domain-specific embeddings (finance, legal) with long context (32K) for enterprise RAG—expect custom pricing. Choose Ollama Benchmark if you're optimizing local LLM inference speed across hardware, want a free open-source tool with community comparisons. They solve different problems: one is a model provider, the other a benchmarking utility.
Ollama Benchmark vs Temporal Ai
Temporal AI and Ollama Benchmark solve completely different problems. Temporal is a heavyweight orchestration platform for mission-critical, long-running workflows requiring reliability and state persistence, ideal for production AI agents and microservices. Ollama Benchmark is a lightweight, free CLI tool for measuring local LLM throughput—perfect for hardware selection and optimization. Choose Temporal if you need durable execution; choose Ollama Benchmark if you need to benchmark local models.
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Frequently Asked Questions
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