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Llmspeed is a website for exploring how quickly selected language models may run on a range of local hardware before buying a device. Its figures mix community benchmark measurements with estimates and projections, so they are not all direct tests of the listed hardware and models.
Llmspeed is a public comparison site for people considering hardware to run large language models locally, presenting speed estimates for combinations of devices and models. The site says its figures describe a short prompt on a single device and combine community benchmark results with estimates derived from similar setups or projected from hardware specifications.
The site lets visitors browse by hardware or by model. Its listed hardware ranges from consumer graphics cards and laptops to workstation-class systems, Apple computers, CPU-only configurations and cloud accelerators. The model list includes several Qwen, Gemma, GLM and DeepSeek variants, as well as GPT-OSS and Llama models.
Llmspeed identifies Reddit’s r/LocalLLaMA, localscore.ai and other sources as inputs for measured results. It also says some figures are inferred from comparable systems or projected from specifications, rather than directly measured on the exact hardware-and-model pairing. The project describes itself as open source on GitHub.
The stated use case is comparison before purchase: visitors can get an indication of how fast a local model might run on a device they own or are considering. The supplied material does not include the individual speed figures, the benchmark methodology, or a breakdown showing which entries are measured and which are estimates.
Comparing Local AI Hardware Before Buying
Local model speed can affect whether a device is practical for interactive use, and hardware options differ in memory capacity, architecture and price. A tool that gathers estimates in one place may help prospective buyers narrow down which models and devices to investigate before spending on a new system.
But the mixed evidence behind the results matters. A projected figure is not equivalent to a test on the exact configuration, and performance can vary with software, model settings and workload. Llmspeed is best treated as a starting point for comparison, not a guarantee of the speed a buyer will see.
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A Catalog Across Models and Devices
Running a language model locally means inference takes place on a user’s own computer or other selected hardware rather than solely on a remote service. Llmspeed focuses on the performance question within that choice: it organizes device and model options so visitors can compare possible pairings.
The catalog includes devices with markedly different memory capacities, from 12GB and 16GB graphics cards to systems listed with 128GB or more, alongside cloud GPUs. That breadth makes the site relevant to buyers weighing a desktop upgrade against a laptop, workstation or hosted accelerator. However, the source material does not say that every listed model can run on every listed device, or that every pairing has a benchmark result.
“Estimates for a short prompt on a single device.”
— Llmspeed website
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How Much of Each Figure Is Measured
The source material does not provide individual performance values, a full testing protocol, or labels identifying the evidence behind each hardware-model result. It is therefore unclear how many combinations have been tested directly, what metric is used for speed, and whether measurements use consistent software and settings.
The site describes estimates for a short prompt on a single device, but does not specify the prompt length, model configuration, or whether results reflect other workloads such as long conversations. It also does not provide a stated update schedule or date for the benchmark data. Readers should verify important purchase decisions against tests on the exact configuration where possible.
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Check Results Against Real Tests
People comparing devices can browse Llmspeed’s hardware and model lists, then check whether a figure is a direct measurement or an estimate before relying on it. The project’s open-source status may allow readers to inspect its implementation, but the supplied information does not detail the repository or how contributions are handled.
No further release, testing milestone or update schedule is stated in the source material. More detail about methodology, dates and the evidence for each entry would help users judge how closely the estimates match their intended workload.
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Key Questions
What is Llmspeed?
Llmspeed is a website for comparing estimated local language-model speed across a list of hardware and models.
Are all the numbers measured on the exact devices shown?
No. Llmspeed says some numbers come from community benchmarks, while others are derived from similar setups or projected from specifications.
What kind of performance does the site describe?
The site characterizes its estimates as applying to a short prompt on a single device. The supplied information does not specify the exact prompt or metric.
Can Llmspeed tell me exactly what speed I will get?
Not based on the available description. Results may be estimates rather than tests of the exact hardware, model and software configuration, so they should be treated as indicative rather than guaranteed.
Where does Llmspeed say its data comes from?
The site names r/LocalLLaMA, localscore.ai and other community benchmark sources, and says the project is open source on GitHub.
Source: hn
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