Show HN: Llmspeed – Feel How Fast A Local LLM Runs Before You Buy The Hardware

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Llmspeed is a website that lets visitors compare estimated local language-model speed across a list of computer hardware and models. Its figures combine community benchmarks with estimates derived from similar setups or hardware specifications, so they are a guide rather than guaranteed results for a particular user’s setup.

Llmspeed has launched a website for comparing estimated speeds of local large language models across a range of hardware, giving prospective buyers a way to preview performance before purchasing a computer or accelerator. The site says its figures are for a short prompt on a single device and mix community benchmark results with estimates and projections, so they should not be read as guaranteed speeds.

The page lets users browse by hardware or by model. Its hardware list spans consumer graphics cards, workstation and data-center accelerators, Apple computers, AMD systems, a CPU-only configuration, and cloud GPUs. Listed examples include the NVIDIA RTX 3090 and RTX 5090, AMD Radeon AI PRO R9700, Mac Studio configurations, and cloud-based H100 and H200 systems.

The model menu includes a mix of families and sizes, among them Qwen, Gemma, DeepSeek, GLM, GPT-OSS and Llama. The source page describes the figures as estimates for a short prompt on one device. It says some results come from community benchmarks, including material from r/LocalLLaMA and localscore.ai, while other numbers are derived from similar systems or projected from specifications.

That distinction matters: the displayed result may not be a direct test of every exact hardware-and-model combination. Llmspeed links to an open-source GitHub project, but the supplied source material does not specify a complete testing protocol, the dates of individual benchmark results, or how estimates are calculated.

A Preview Before Hardware Purchases

Running a model locally can depend on more than the model name. Hardware capability and memory affect which models a system can run and how quickly it can generate responses. A comparison tool that puts models and devices side by side may help shoppers narrow options before spending on a GPU, workstation, or computer.

But a single speed estimate cannot settle a purchase decision. Actual performance can vary with software, model settings, prompt length, memory use, and the rest of a system. Because Llmspeed combines measurements with projections, readers should treat it as a starting point for comparison, not a promise of what their own machine will deliver. The distinction is particularly relevant when comparing consumer hardware with cloud accelerators or systems using different memory configurations.

Benchmarks Behind the Comparisons

The announcement is framed as a practical preview: users can inspect performance across hardware and models before they own the equipment. The source lists systems ranging from a 12GB RTX 3060 to high-memory Apple and accelerator configurations, as well as CPU-only and cloud options. This breadth makes the page useful for exploring possible trade-offs, though the listed configurations are not necessarily tested under identical conditions.

Llmspeed identifies community benchmark sources, including r/LocalLLaMA and localscore.ai, and says some figures are inferred from comparable setups or specifications. Community measurements can expand coverage beyond formal lab tests, but their value depends on clear methodology and comparable workloads. The source page’s short-prompt, single-device description provides a limited frame for interpreting results; it does not establish performance for longer conversations, batch workloads, or every supported software stack.

“Estimates for a short prompt on a single device.”

— Llmspeed website

How Reliable Are Individual Estimates?

The available source does not give a complete account of benchmark methods, the date or number of tests behind each measured result, or the assumptions used to project untested configurations. It is also unclear whether each entry represents one result, an average, or a range, and how closely the short-prompt workload reflects common local use.

The listed model and hardware names alone do not establish that every pairing has been tested. Users should check the project’s details and treat projected numbers separately from direct measurements. No independent validation or buying recommendation is provided in the source material.

Check the Data Before Buying

Readers considering a purchase can use Llmspeed to identify candidate hardware and models, then consult the underlying benchmarks and project documentation for test conditions. The site links to GitHub, where additional information about its data and calculation methods may be available. The source provided does not announce a schedule for updates or a next release.

For now, the practical next step is to compare like with like: check whether a figure is measured or projected, confirm the workload and device configuration, and seek results from a setup close to the one being considered. Llmspeed provides estimates, not a guarantee of real-world performance.

Key Questions

What does Llmspeed do?

It lets visitors compare estimated local LLM speed across listed models and hardware, including consumer devices, workstations, and cloud accelerators.

Are all the figures direct benchmark results?

No. Llmspeed says some numbers come from community benchmarks; others are derived from similar systems or projected from hardware specifications.

What workload do the estimates describe?

The website describes them as estimates for a short prompt on a single device. The supplied material does not establish how results translate to longer or more complex workloads.

Can shoppers rely on the site to choose hardware?

It can help identify options, but its figures are not guarantees. Check whether each result is measured or projected and review its underlying test conditions before buying.

Source: hn