Hacker News AI · 10/17/2026, 12:36:53
Llmspeed Tool Estimates Local LLM Speed Before Hardware Purchase
68AI Score
Executive Summary
Developer releases open-source tool Llmspeed to simulate local LLM inference speeds on specific hardware before purchase. Integrating community benchmarks and spec-based projections, it covers major platforms like NVIDIA, AMD, and Apple Silicon, offering intuitive guidance for local AI deployment.
SOURCE COVERAGEFull Original Report
Contents2 sections
Browse by hardware
- NVIDIA RTX 3090 24GB
- AMD Ryzen AI Max+ 395 128GB (Strix Halo)
- NVIDIA RTX 5090 32GB
- NVIDIA RTX 4090 24GB
- Mac Studio M5 Ultra 512GB
- MacBook Pro M5 Max 128GB
- NVIDIA RTX 5060 Ti 16GB
- NVIDIA DGX Spark 128GB
- NVIDIA RTX PRO 6000 96GB
- Mac Studio M3 Ultra 512GB
- AMD RX 7900 XTX 24GB
- NVIDIA RTX 3060 12GB
- Mac Studio M4 Max 128GB
- NVIDIA RTX 5070 Ti 16GB
- AMD RX 9070 XT 16GB
- Intel Arc Pro B70 32GB
- Mac Mini M4 Pro 64GB
- AMD Radeon AI PRO R9700 32GB
- NVIDIA RTX 5080 16GB
- MacBook Pro M5 Pro 48GB
- AMD RX 7900 XT 20GB
- NVIDIA RTX 4070 Ti Super 16GB
- MacBook M4 Pro 24GB
- NVIDIA RTX 4070 12GB
- MacBook M3 Max 48GB
- CPU-only (Ryzen 9 9950X, 96GB DDR5)
- MacBook M2 Pro 16GB
- H100 80GB (cloud)
- A100 80GB (cloud)
- H200 141GB (cloud)
Browse by model
- Qwen 3.6/3.8 27B
- GLM-5.3 Flash
- GPT-OSS 20B
- Gemma 4 26B A4B
- Qwen 3.5/3.6 35B A3B
- Ornith 1.5 35B A3B
- DeepSeek V4 Flash
- GPT-OSS 120B
- Gemma 4 31B
- Llama 3.1 8B
- Qwen 3.5 9B
- DeepSeek R1 Distill 14B
- Ornith 1.5 9B
- Muse Glimmer 30B
- Qwen3 30B A3B
Estimates for a short prompt on a single device. Measured numbers come from community benchmarks (r/LocalLLaMA, localscore.ai and more); others are derived from similar setups or projected from specs. Open source on GitHub.