Ollama Benchmark
Free open-source CLI benchmark that measures local LLM tokens-per-second throughput through Ollama and posts each run to a public results
One command, zero dollars, and a public table to check your number against strangers' hardware. For a pre-purchase GPU or Mac decision, that's a fast trade. Just know it measures tokens-per-second only, on Ollama-served models only, and it won't tell you latency or VRAM headroom.
Verified 1d ago · liveness 68/100 · cite: rightaichoice.com/tools/ollama-benchmark
- Developers running local LLMs through Ollama who want a measured tokens-per-second number
- AI engineers choosing between GPU and CPU configurations before buying hardware
- Researchers comparing model variants or quantizations across machines
- Hobbyists checking whether existing hardware is fast enough for a local model
- Teams needing cloud-hosted or API model performance comparisons
- Buyers who need time-to-first-token, latency, or memory-headroom profiling
- Non-technical users who don't want to work in a terminal or manage a virtualenv
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Skip Ollama Benchmark if you need latency, time-to-first-token, or VRAM profiling, or if you benchmark runtimes other than Ollama — it reports tokens per second for Ollama-served models and nothing else.
Ollama Benchmark is free and MIT-licensed, so there is no tier ladder to climb: the CLI costs nothing and the community results are open to everyone, which puts it below paid benchmarking suites and hosted inference-performance dashboards. The real cost is your time — a virtualenv, an Ollama install, and terminal comfort.
In short
Ollama Benchmark — Free open-source CLI benchmark that measures local LLM tokens-per-second throughput through Ollama and posts each run to a public results. Best for Developers running local LLMs through Ollama who want a measured tokens-per-second number, AI engineers choosing between GPU and CPU configurations before buying hardware, Researchers comparing model variants or quantizations across machines. 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.
Average across the 4 sources that answered — each source counts once, not each post.
- +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: October 2026
How we score →Key Features
- Single-command benchmark via `llm_benchmark run`
- Measures tokens-per-second throughput for local LLMs served through Ollama
- Automatically submits each run to a public community benchmark database
- Public results views: All Platforms, macOS, Linux, Windows
- Compares results across Apple Silicon, NVIDIA GPUs, and CPU configurations
- Installable via pip with `pip install llm-benchmark`
- Installable via uv with `uv pip install llm-benchmark`
- Documented venv setup using `python3 -m venv .venv`
- Command-line interface with no GUI
- Open source under the MIT license
- Source code hosted on GitHub
- Package distributed on PyPI
- Community-contributed results from real hardware, not synthetic tests
About Ollama Benchmark
If your question is "how fast will this model actually run on my box," LLM Benchmark answers it in the terminal. It's a free, MIT-licensed CLI published on PyPI as llm-benchmark and demos as Ollama Benchmark. You make a directory, create a virtualenv, `pip install llm-benchmark` (or `uv pip install llm-benchmark` inside a uv venv), then run `llm_benchmark run`. The tool drives the models you already serve through Ollama and reports throughput in tokens per second, submitting every result to a public community database on the way out. The shared results set is the point. The vendor publishes tables filtered by All Platforms, macOS, Linux, and Windows, so your number lands next to Apple Silicon, NVIDIA GPU, and CPU runs from people who actually own the hardware. Vendor framing is blunt about this: community-contributed benchmarks from real setups, not synthetic tests. That makes the site a reference point for hardware buying and model selection, not a one-off speed test you forget by lunch. Installation is documented for both pip and uv, source lives on GitHub, and it runs on macOS, Linux, and Windows. The boundaries matter before you commit: it benchmarks Ollama-served models only, it reports throughput rather than time-to-first-token or memory headroom, and it assumes a Python virtualenv and a comfort level with the command line. There is no GUI and no hosted dashboard. Against alternatives, LLM Benchmark trades breadth for speed of answer. LM Studio reports its own throughput readouts inside a GUI, llama.cpp ships its own tooling, and vLLM users have their own benchmarking path. This tool wins on one thing: a single command, a free MIT package, and a public number you can compare. On pricing: the project is open source and the site carries no paid tier list.
Behind the Verdict
We'd reach for this at the moment of decision: you're eyeing a Mac Studio or a used 3090, or you're torn between two quantizations of the same model, and you want a number rather than a forum argument. Install, run, get tokens per second, then open the results tables and see where you land versus Apple Silicon, NVIDIA, and CPU entries. The community database is what separates it from running a quick timer yourself. A single-machine test tells you your rig is fast. A public table tells you whether your rig is fast for the money, which is the question people actually have when they're about to spend four figures on hardware. The honest caveats. Throughput is the whole story here, so time-to-first-token, sustained-load behavior, and memory headroom are all outside the frame. If you're tuning a production serving stack, that's a real gap, and you'll want llama.cpp's own tooling or your runtime's profiler alongside it. Runtime lock-in is the other limit. The tool drives Ollama, so if your stack is vLLM, LM Studio, or a cloud API, this isn't your measurement. The comparison you'd want against hosted model pricing simply isn't here. And it's a terminal tool with a virtualenv. Non-technical buyers, or anyone hoping for a GUI dashboard, will bounce off the setup steps. Support is a GitHub repo and a PyPI listing, not a sales team. Where it bites in practice: benchmark runs are sensitive to what else is running, thermals, and the exact quantization. Community numbers are self-reported from uncontrolled environments, useful as a range and unreliable as a spec sheet. Treat the tables as directional. Pick it when you run Ollama, want a free MIT-licensed measurement, and care about hardware comparison. Pass when you need latency profiling, non-Ollama runtimes, or a hosted
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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.
Install `llm-benchmark` in a venv, run `llm_benchmark run` against the same Ollama model on both machines, then filter the public results by All Platforms to see where each configuration lands against Apple Silicon, NVIDIA GPU, and CPU entries.
Outcome: A measured tokens-per-second number per card plus community context, giving a concrete basis for the purchase instead of a spec-sheet guess.
Benchmark the target model on the actual edge box through Ollama, submit the run, then re-run after a driver or Ollama upgrade to check for regressions.
Outcome: A documented before/after throughput baseline for that specific hardware and model pairing.
Run `llm_benchmark run` against two quantizations of the same model served through Ollama and compare the reported tokens-per-second figures, then check the macOS and Linux result tables for similar setups.
Outcome: A quick, free read on which variant is faster on the machine at hand, with community data as a sanity check.
Use Cases
- Measure tokens per second for a specific model on your machine before committing to a hardware purchase
- Compare throughput between two GPUs, two CPUs, or an Apple Silicon laptop and a desktop GPU
- Benchmark a setup before deploying a local LLM at the edge
- Re-run the benchmark after an OS, driver, or Ollama upgrade to confirm throughput did not regress
- Submit your hardware's results so others can see real-world performance for that configuration
- Check the public results tables to estimate what a given platform class actually delivers
Limitations
- Ollama Benchmark only measures models you serve through Ollama, so results do not cover vLLM, LM Studio, or other runtimes.
- It is a command-line tool that expects basic terminal comfort and a Python virtual environment (venv or uv).
- It reports tokens-per-second throughput alone and does not capture time-to-first-token, latency percentiles, or memory headroom.
- The community database is contributed by users on varied hardware and settings, so entries are rough signals rather than controlled benchmarks — compare trends across many rows, not single entries.
as of 2026-10-10
Verification history
We have re-verified Ollama Benchmark 9 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 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Ollama Benchmark tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0
Ideal for
Developers, AI engineers, and researchers who already run local inference through Ollama and want a free throughput baseline for their own hardware.
What this tier adds
Starting tier — the only published plan: $0, MIT-licensed, installed via pip or uv, with each run automatically submitted to the public community results database.
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 free and MIT-licensed, so there is no tier ladder to climb: the CLI costs nothing and the community results are open to everyone, which puts it below paid benchmarking suites and hosted inference-performance dashboards. The real cost is your time — a virtualenv, an Ollama install, and terminal comfort.
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.
Most people reach a first result in roughly ten to twenty minutes: create a directory and a Python virtualenv (`python3 -m venv .venv`), activate it, `pip install llm-benchmark`, then `llm_benchmark run`. Using uv trims that further — `uv venv .venv`, `uv pip install llm-benchmark`, `uv run llm_benchmark run`. The long pole is having Ollama running with a model already pulled.
Switching to or from Ollama Benchmark
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual stopwatch testing: replace ad-hoc timing with `llm_benchmark run` so each result is recorded in tokens per second and added to the public database.
- →From llama.cpp's built-in tooling: keep llama.cpp for non-Ollama runtimes, and use `llm_benchmark run` when you want throughput numbers for models served through Ollama in a comparable, shareable format.
- ↗To llama.cpp's own tooling: move over when you need benchmarking outside Ollama-served models or want to profile the runtime directly.
- ↗To LM Studio's built-in speed readouts: move over if you want GUI-side throughput figures instead of a CLI and virtualenv workflow.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Ollama Benchmark”, and we withheld 6: 6 did not mention Ollama Benchmark. We are showing none, because we could not prove any of them are about Ollama Benchmark.
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.
Lance
Lance is an open lakehouse format for multimodal AI, pairing a file format, table format, and catalog spec with hybrid vector and full-text search on object
Unsloth
Open-source local AI workspace for fine-tuning and running LLMs, image, and video models on your own GPU
Deci
Automated deep learning model optimization for NVIDIA GPUs.
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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