TL;DR
Get business pricing on tech for your team
- Business-only prices and quantity discounts
- Tax-exempt purchasing
- Multiple users, one account, clear invoices
Llmspeed is a website for comparing how quickly selected large language models may run on a range of local hardware. It labels its figures as estimates for a short prompt, combining community benchmarks with projections; results are not guaranteed performance for every workload.
Llmspeed has launched a browser-based tool for comparing estimated speeds of selected local large language models on a wide range of hardware, giving prospective buyers a way to explore performance before choosing a device. The site says its estimates concern a short prompt on a single device and combine community benchmark results with figures derived from similar systems or projected from specifications.
The interface lets users browse by hardware or by model. Listed devices range from consumer graphics cards and Apple computers to AMD systems, workstation products, and cloud GPUs. The model list includes several sizes and families, such as Qwen, Gemma, DeepSeek, GLM, and GPT-OSS. The source page lists products including the RTX 3090, RTX 5090, Mac Studio M5 Ultra, and cloud-based H100 and H200 systems.
Llmspeed says its measured figures come from community benchmarks, including material from r/LocalLLaMA and localscore.ai. Other entries are not direct measurements: the site says some are derived from similar setups or projected from hardware specifications. It describes the displayed results as estimates, rather than promising that a particular user will achieve the same speed.
The project identifies itself as open source on GitHub. The supplied source material does not include a release date, detailed test methodology, numerical results for individual combinations, or a description of how frequently the estimates are updated.
A Performance Guide Before Purchase
Local model performance can vary with both the model and the machine running it. A comparison tool that puts hardware and model options side by side may help people narrow their choices before spending money on a graphics card, computer, or other system. It can also make the trade-off between running a model locally and using cloud hardware easier to explore.
However, Llmspeed’s figures should be treated as planning estimates, not purchase guarantees. A result based on a short prompt or projected from specifications may not predict performance on longer inputs, different generation settings, or a user’s particular software configuration. The site’s distinction between measured and estimated results is important when using the comparison.
As an affiliate, we earn on qualifying purchases.
How Llmspeed Builds Its Comparisons
Interest in running language models locally has brought together users comparing hardware capacity, model size, and speed. Llmspeed addresses one part of that decision: how a model might perform on a selected device. Its catalog includes consumer and professional hardware as well as cloud accelerators, so users can compare options across different settings.
The source page frames the figures narrowly: they are for a short prompt and one device. It also names two types of evidence—community measurements and estimates based on comparable setups or specifications—rather than presenting every listed pairing as a direct benchmark. No benchmark table or test protocol was included in the supplied material, so further methodological detail cannot be confirmed here.
“Estimates for a short prompt on a single device.”
— Llmspeed website
As an affiliate, we earn on qualifying purchases.
Limits of the Speed Estimates
The supplied information does not show the actual speed figures for specific model-and-device combinations or explain the benchmark procedure in detail. It is also unclear how the site handles differences in software, memory use, prompt length, generation settings, or updates to hardware and model versions. Llmspeed says some numbers are projections, but the source material does not specify which individual entries fall into that category.
There is no independent validation or user testing described in the supplied material. Because the estimates are explicitly tied to a short prompt and a single device, readers should not assume they represent every real-world task or setup.
As an affiliate, we earn on qualifying purchases.
What Users Can Check Next
Users can explore the live site by hardware or model and consult the linked GitHub project for any available code or further documentation. Before relying on a particular comparison, they can check whether its result comes from a direct community benchmark or an estimate, and whether the tested setup resembles their intended use.
The source material does not announce a schedule for new benchmarks, planned features, or future releases. Any changes to the catalog or methodology remain unconfirmed.
local large language model hardware
As an affiliate, we earn on qualifying purchases.
As an affiliate, we earn on qualifying purchases.
Key Questions
What is Llmspeed?
Llmspeed is a website for comparing estimated speeds of selected local language models across a list of hardware options.
Are all the figures measured directly?
No. The site says some figures come from community benchmarks, while others are derived from similar setups or projected from hardware specifications.
What kind of workload do the estimates cover?
Llmspeed describes its estimates as applying to a short prompt on a single device. The supplied material does not specify a broader set of workloads.
Can Llmspeed tell me exactly how fast a model will run on my system?
No exact result is guaranteed by the information provided. Actual speed may depend on the particular hardware and software setup and the task being run; the site presents its numbers as estimates.
Is the project open source?
The Llmspeed site says the project is open source on GitHub. The supplied source material does not give further information about its license or contribution process.
Source: hn
Halloween Picks
halloween
As an affiliate, we earn on qualifying purchases.
