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Llmspeed is a website for comparing how quickly selected language models may run on a range of local hardware. It says some figures come from community benchmarks, while others are estimates or projections, so the results are a research aid rather than a guarantee of performance on a particular setup.
Llmspeed has published an online tool for comparing estimated performance of local language models across a range of GPUs, computers and other hardware, giving prospective buyers a way to research speed before choosing a device. The site says its figures combine community benchmark measurements with estimates derived from similar systems or hardware specifications, so not every result is a direct test.
Visitors can browse by hardware or by model. The listed devices range from consumer GPUs such as the Nvidia RTX 3060 and RTX 5090 to AMD graphics cards, Apple computers, systems with large shared-memory configurations, CPU-only hardware, and cloud-based H100 and H200 accelerators. The model list includes several Qwen, Gemma, DeepSeek and GLM options, along with Llama 3.1 and GPT-OSS.
The page describes its figures as estimates for a short prompt on a single device. It says measured results are drawn from community benchmarks, including r/LocalLLaMA and localscore.ai, while other figures are derived from similar setups or projected from specifications. Llmspeed links to an open-source GitHub project, though the supplied material does not specify the benchmark procedure or how often the data is updated.
The tool is aimed at a practical purchasing question: whether a system someone is considering can run a particular model at a usable speed. Its comparisons may help narrow hardware choices, but the site’s own description makes clear that a displayed estimate should not be treated as a guaranteed result for every configuration.
A Speed Check Before Hardware Shopping
Running a model locally can depend on more than a device’s headline specifications. Model size, memory capacity, software and configuration can affect whether a model fits and how quickly it generates text. A single place to compare candidate models and devices may help buyers identify options worth researching or testing before spending money.
The distinction between measured and projected results matters for that decision. A benchmark from one system may not match another system with the same product name if components, settings or software differ. Llmspeed can offer a starting point for comparison, but its estimates are not a substitute for testing the exact model on the intended hardware, particularly where speed or memory limits are important.
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Community Data Behind the Comparisons
Interest in local language models has grown alongside a wide range of consumer and workstation hardware that can run them without relying on a hosted service. Buyers comparing devices may need to weigh performance against memory, price and model choice, and benchmark results are often spread across community posts and specialist sites.
Llmspeed’s supplied description points to community sources rather than identifying a single standardized testing suite. It also says some figures are calculated from similar setups or projected from specifications. The source lists many hardware and model categories, but does not provide a publication history or explain whether each listed combination has been directly benchmarked.
“Estimates for a short prompt on a single device.”
— Llmspeed website
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How Much to Trust Each Estimate
The supplied material does not identify which individual hardware-model results are direct measurements and which are projections, nor does it describe the testing conditions, software versions, prompt details or scoring method beyond the short-prompt, single-device qualification. It also does not state when the figures were collected or how frequently they are refreshed.
It is therefore unclear how closely a displayed estimate will match a specific buyer’s system and workload. The source provides no independent validation of the comparisons, and no pricing or purchase recommendation. Readers should treat the figures as indicative until Llmspeed provides more detail or a result can be reproduced on the hardware in question.
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More Detail Needed for Buyers
Llmspeed is available now, and its GitHub project gives users a place to inspect the open-source tool. The source material does not announce a roadmap, a next release date or plans to expand testing. The next useful developments for readers would be clearer labels for measured versus projected entries, fuller benchmark methodology and dated results tied to specific software and hardware configurations.
Until those details are available, people comparing devices can use Llmspeed to shortlist combinations, then check the underlying community benchmarks and test their intended model and workload directly where possible.
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Key Questions
What does Llmspeed do?
It lets users browse estimated local language-model performance by hardware or model, across consumer GPUs, computers, CPU-only systems and cloud accelerators.
Are all the figures measured on the listed hardware?
No. Llmspeed says some values come from community benchmarks; others are derived from similar setups or projected from specifications.
What kind of workload do the estimates represent?
The site describes them as estimates for a short prompt on a single device. The supplied information does not spell out other workload or testing details.
Can I use the results to choose what to buy?
The comparisons may help you shortlist options, but they are not a guarantee of performance on your own system. Check the benchmark basis and, if possible, test the model and workload you plan to use.
Is Llmspeed open source?
Yes. The website says the project is open source on GitHub.
Source: hn
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