Llmspeed provides performance estimates for running local LLMs on various GPUs and CPUs. The tool helps users predict token speeds before purchasing hardware.
NouGen Intelligence
Strategic Report
Strategic Implications
The story focuses on performance benchmarking, which is valuable, but lacks specific technical details about the LLM architecture or the exact hardware used for the benchmark.
Market Verdict
— The Bottom LineA helpful estimation tool for hardware buyers that blends community data with spec-based projections.
What happened
A new project called Llmspeed has been released to help users estimate the performance of local Large Language Models (LLMs) before they invest in hardware. The tool allows users to browse a list of hardware configurations and LLM models to see how fast a specific combination will feel during execution. The project is open source and available on GitHub.
The tool lists a wide array of hardware, including NVIDIA RTX series GPUs (from the 3060 to the upcoming 5090), AMD Radeon and Instinct cards, Apple Silicon (M3, M4, and M5 series), and CPU-only configurations like the Ryzen 9 9950X. It also includes cloud-based options such as the H100 and H200 for comparison.
How it works
Llmspeed functions as a lookup and estimation tool for inference speed. Users can select a specific piece of hardware and a specific model to see the projected performance. The supported models include various versions of Qwen (such as Qwen 3.5 and 3.8), Gemma 4, DeepSeek V4, Llama 3.1, and GLM-5.3.
The tool focuses on the user experience of speed, specifically targeting how a short prompt performs on a single device. By providing these estimates, the tool attempts to remove the guesswork involved in matching VRAM requirements and compute power to specific model sizes, such as the 8B Llama 3.1 or the larger 120B GPT-OSS.
The catch
There are significant caveats regarding the data used by Llmspeed. The tool does not rely on a single, controlled laboratory environment for all its data. Instead, it uses a mix of sources. Some of the numbers are measured values taken from community benchmarks, specifically citing sources like r/LocalLLaMA and localscore.ai.
Other numbers are not measured at all. For hardware or models where direct benchmark data is unavailable, the tool uses derived numbers. These are calculated based on similar existing setups or projected from the technical specifications of the hardware. This means that for newer or unreleased hardware, the speed is an estimate rather than a verified fact. Because it targets short prompts on single devices, the results may not reflect performance during long-context window operations or multi-user environments.
Verdict
Llmspeed is a useful reference for hardware planning, provided users understand that some figures are projections rather than measured benchmarks.
Sources: https://llmspeed.dev/
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NouGen Intelligence Analysis
Key Points
- Llmspeed estimates local LLM token speeds for various hardware configurations.
- Data is sourced from community benchmarks and technical specification projections.
- Supports a wide range of hardware from RTX 3060s to M5 Ultra Macs.
- Focuses on short prompt performance on single devices.
Strategic Implications
The story focuses on performance benchmarking, which is valuable, but lacks specific technical details about the LLM architecture or the exact hardware used for the benchmark.