Compare Hardware for Running LLMs
A ladder from an entry GPU to a datacenter card, showing usable memory, our Local AI Score, and the largest open model each can run well. Memory capacity is what unlocks bigger models; bandwidth drives how fast they generate.
| Device | Class | Memory | Usable | Local AI Score | Largest model it runs well |
|---|---|---|---|---|---|
| NVIDIA GeForce RTX 3060 12GB | Consumer GPUs | 12GB | ~10.6GB | 33/100 | CodeLlama 13B |
| NVIDIA GeForce RTX 3090 | Consumer GPUs | 24GB | ~21.1GB | 44/100 | Gemma 2 27B |
| NVIDIA GeForce RTX 4090 | Consumer GPUs | 24GB | ~21.1GB | 47/100 | Gemma 2 27B |
| Apple Mac Studio (M4 Max) | Apple Silicon | 128GB | ~89.6GB | 67/100 | Gemma 2 27B |
| NVIDIA RTX A6000 | Professional GPUs | 48GB | ~42.2GB | 50/100 | Mixtral 8x7B (MoE) |
| Dual RTX 3060 Local Server (reference profile) | AI Servers | 24GB | ~21.1GB | 41/100 | CodeLlama 13B |
| Quad RTX 4090 AI Workstation (reference profile) | AI Workstations | 96GB | ~84.5GB | 75/100 | Qwen2.5 72B |
| NVIDIA H100 (80GB) | Datacenter GPUs | 80GB | ~70.4GB | 91/100 | Qwen2.5 72B |
| AMD Instinct MI300X | Datacenter GPUs | 192GB | ~169GB | 100/100 | Qwen3 235B-A22B (MoE) |
| Cloud H200 141GB (profile) | Cloud GPU Profiles | 141GB | ~124.1GB | 97/100 | Qwen2.5 72B |
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