Show HN: Llmspeed – feel how fast a local LLM runs before you buy the hardware
Llmspeed, posted to Hacker News as a Show HN, is a simulator intended to let users feel how fast a local LLM runs before buying hardware. The site organizes browsing in two directions: by hardware and by model. The hardware list spans consumer and workstation GPUs, Apple silicon machines, AMD accelerators, and cloud options, including the NVIDIA RTX 3090 24GB, RTX 5090 32GB, RTX 4090 24GB, RTX 5060 Ti 16GB, RTX PRO 6000 96GB, Tesla V100 32GB, AMD Radeon AI PRO R9700 32GB, AMD RX 7900 XTX 24GB, AMD Instinct MI50 32GB, Intel Arc Pro B70 32GB, Mac Studio M5 Ultra in 256GB and 512GB configurations, MacBook Pro M5 Max 128GB, Mac mini M5 Pro 64GB, NVIDIA DGX Spark 128GB, a CPU-only Ryzen 9 9950X 96GB DDR5 setup, and H100 80GB and H200 141GB cloud instances. The model list includes Qwen 3.6/3.8 27B, Qwen 3.8 Flash-Next, GLM-5.3 Flash, Gemma 4 26B A4B, GPT-OSS 20B, Qwen 3.5/3.6 35B A3B, Gemma 4 31B, Qwen 3.5 9B, DeepSeek V4 Flash, DeepSeek V4.1 Flash, GPT-OSS 120B, Llama 3.1 8B, Gemma 4 12B, GLM-5.3, and Nemotron 3.5 Lightning 30B A3B. The site describes its figures as estimates for a short prompt on a single device. It says measured numbers come from community benchmarks, naming r/LocalLLaMA and localscore.ai among others, while other figures are derived from similar setups or projected from specs. The simulator requires JavaScript, and the site points to its code and data at github.com/nicorobinreal/llmspeed, describing the project as open source.
Developers weighing a local-model hardware purchase can compare listed GPUs, Apple silicon, and cloud options against specific models before spending.