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Llmspeed is a public site for comparing how quickly selected language models may run on a range of local hardware. The site says figures are estimates for a short prompt: some come from community benchmarks, while others are derived from similar systems or projected from specifications.
Llmspeed is offering a browser-based way to compare estimated local language-model performance across a broad list of computers and accelerators, giving prospective buyers an early sense of speed before they purchase hardware. The site says its figures cover a short prompt on a single device and mix community benchmark results with estimates derived from comparable setups or hardware specifications.
The site lets visitors browse by hardware or by model. Its hardware list includes consumer NVIDIA and AMD graphics cards, workstation and data-center accelerators, Apple computers, systems with large shared-memory configurations, a CPU-only setup, and two cloud GPUs. The listed devices range from an RTX 3060 with 12GB of memory to configurations such as an RTX PRO 6000 with 96GB and Apple systems listed with up to 512GB.
The model browser includes several model families and sizes, among them Gemma 4, Qwen, DeepSeek, GLM and GPT-OSS entries. These are examples shown in the source material, not a guarantee that every model can run on every listed device. Llmspeed says its estimates are for a specific, limited scenario: a short prompt processed on one device.
Llmspeed identifies Reddit’s r/LocalLLaMA, localscore.ai and other sources as origins for measured results. It also says some numbers are not direct measurements: they are derived from similar setups or projected from specifications. The project describes itself as open source on GitHub, though the supplied material does not include a methodology document, benchmark values, or details on how estimates are labeled in each comparison.
A First Pass Before Buying Hardware
Running a model locally can depend on more than the model’s name: the computer’s memory, accelerator and configuration can affect whether a model fits and how quickly it generates output. A comparison tool can help buyers narrow a long list of possible systems and spot options worth investigating before spending on a GPU, workstation or Mac.
But the figures should be treated as planning estimates, not a purchase guarantee. Llmspeed combines direct community measurements with modeled or projected figures, and those categories can have different levels of certainty. Performance in a real workload may also differ from the site’s stated short-prompt, single-device scenario. For readers, the practical value is in making comparisons easier—not replacing a benchmark on the exact hardware and software they plan to use.
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How Llmspeed Builds Its Comparisons
The project is framed as a way to “feel how fast a local LLM runs before you buy the hardware,” but the source material describes a comparison site rather than an interactive simulation or a promise of measured performance on a visitor’s own machine. Its listed results draw on multiple inputs: community benchmarks, data from other named sources, comparisons with similar systems and projections based on specifications.
That distinction matters because benchmark results are meaningful only alongside their test conditions. A short prompt on one device does not describe every use case, such as longer inputs, sustained generation, multiple concurrent users, or a setup using more than one accelerator. The source material does not specify the testing software, model quantization, settings, or precise procedures behind individual figures, so readers cannot infer that all entries were tested under identical conditions.
“Estimates for a short prompt on a single device.”
— Llmspeed website
Benchmark Methods Need More Detail
The supplied information does not show the numerical results, identify which individual entries are measured versus estimated, or explain the methodology behind each figure. It is also unclear how recently community results were collected, what software and model settings were used, and whether comparisons account for differences in quantization or other configuration choices.
Llmspeed’s stated scope is a short prompt on a single device, so its estimates should not be assumed to predict performance for longer prompts or every local-LLM workload. The source material also does not say how the site handles updates when hardware, model versions or benchmark methods change. No independent validation of its estimates is described in the supplied report.
Check Results Against Your Workload
Readers considering a purchase can use Llmspeed to shortlist hardware and models, then check the underlying benchmark details before treating a number as a reliable forecast. The project says it is open source, which may let interested users inspect its implementation through GitHub; the source material does not state a release schedule or announce a forthcoming update.
For a firmer comparison, buyers will need results tied to the exact model, hardware configuration and workload they expect to use. Whether Llmspeed will publish more detailed per-result sourcing, test settings and update dates is not stated in the available material.
Key Questions
What is Llmspeed?
Llmspeed is a website for comparing estimated local language-model performance across listed hardware and models. Its stated goal is to help people get a sense of speed before buying hardware.
Are all Llmspeed figures measured on the listed hardware?
No. Llmspeed says some numbers come from community benchmarks, while others are derived from similar setups or projected from specifications. The supplied information does not identify the source category for every individual figure.
What kind of workload do the estimates describe?
The site describes its estimates as covering a short prompt on a single device. The source material does not establish that the figures predict results for longer prompts or other workloads.
Which hardware and models can users compare?
The listed hardware spans NVIDIA and AMD accelerators, Apple computers, CPU-only and cloud configurations. The model browser includes entries from families such as Qwen, Gemma, DeepSeek, GLM and GPT-OSS; the supplied list does not establish compatibility between every model and device.
Can Llmspeed results alone determine what hardware to buy?
They are better treated as a starting point. Because the site mixes measurements and estimates and describes a limited test scenario, buyers should check test conditions and seek results that match their intended model and workload.
Source: hn
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