llmrun

llmrun is a web-based AI hardware checker that tests GPU compatibility, VRAM requirements, and model performance for running LLMs locally.

Llmrun is a hardware checker designed to help users determine which AI models can run locally on their machines. It evaluates GPU compatibility, VRAM requirements, quantization options, and expected performance for various large language models. Users select their GPU or Apple Silicon device from a list to see which models fit in their memory, complete with fit ratings ranging from S to F. The tool provides browser-based detection without uploading user data, and supports deployment via platforms like Ollama, LM Studio, or direct GGUF downloads. It caters to individuals seeking to run open-source models on consumer hardware, ranging from entry-level entry-tier setups to enthusiast and professional workstations.

llmrun at a glance

Best for: Software engineer
Runs on: Web
Pricing: Free
Released: October 2026
Category: AI coding tool
Website: llmrun.dev

Key features

Browser-based hardware detection
GPU compatibility checking
VRAM requirement analysis
Quantization option evaluation
Expected performance calculation
Hardware fit ratings from S to F
Model rankings by composite score

Use it for

Check if local hardware can run specific AI models
Find models matching specific VRAM tiers
Compare performance across different GPUs and Macs
Identify compatible models for coding or reasoning

Alternatives to llmrun

RunThisLLM — Provides exact hardware and VRAM requirements for over 295 local AI models.
AirLLM — Runs massive 70B+ parameter models on small 4GB consumer GPUs.
whichllm — Ranks models using real benchmarks and hardware fit instead of parameter count.
llmfit — Auto-detects hardware to recommend and score compatible open-source LLMs.
llm-d — Deploys production-grade LLM serving on existing hardware clusters.
Strata — Runs large AI models locally on consumer gaming PCs without server reliance.
Runpod Vllm Deployer — It provides safe deployment plans and explicit confirmation gates for billable GPUs.
GPUStack — Unifies AI model serving and GPU compute management across heterogeneous hardware.
LMDeploy — Accelerates inference with high-performance engines and quantization tools.
Soup — Fine-tune and align models like Llama-3.1-8B on a 4 GB laptop GPU.
WebLLM — Runs high-performance language models directly inside web browsers.
MTPLX — It runs local models twice as fast via native MTP speculative decoding.

All llmrun alternatives