Llama 3.1 405B vs Llama 3.1 70B
Size, context window, license, approximate VRAM and the minimum local hardware each model needs — computed from our catalog and compatibility engine, not benchmarks.
| Llama 3.1 405B | Llama 3.1 70B | |
|---|---|---|
| Parameters | 405B | 70B |
| Context window | 128K tokens | 128K tokens |
| License | Llama Community License | Llama Community License |
| ~VRAM @ 4-bit (Q4_K_M) | ~230 GB | ~42 GB |
| ~VRAM @ 8-bit (Q8_0) | ~410 GB | ~75 GB |
| Minimum device | Supermicro 8x H100 SuperServer | NVIDIA RTX A6000 |
| Recommended device | Supermicro 8x H100 SuperServer | Supermicro 8x H100 SuperServer |
| Deployment | Cloud | Hybrid |
| Capabilities | Tools, Reasoning, Multilingual, Long context | Tools, Reasoning, Multilingual, Long context |
Highlighted cells mark the lighter / longer / more permissive side per row, for local deployment. Informational rows have no winner.
Bottom line
Llama 3.1 70B (~70B) is lighter than Llama 3.1 405B (~405B), so it runs on more modest hardware, while Llama 3.1 405B trades a larger footprint for more capacity. At 4-bit, Llama 3.1 70B needs about 42GB versus ~230GB, a meaningful gap when choosing a GPU. Both target a 128K context window. Minimum viable hardware differs: Llama 3.1 405B starts on a Supermicro 8x H100 SuperServer, Llama 3.1 70B on a NVIDIA RTX A6000. Figures are approximate working-set estimates, not benchmarks — verify the exact release before committing hardware.
Pick Llama 3.1 405B if you have the memory to spare and want the larger model.
Pick Llama 3.1 70B if you want the lighter footprint and cheaper hardware.
Datacenter tier — realistically a multi-GPU / multi-node or cloud target even at 4-bit.
Flagship tier — ~42GB at 4-bit needs a 48GB card, a 64GB+ unified-memory Mac, or multi-GPU.
Run the winner on hardware you control
Pick the model that fits your footprint, then turn the right machine into a private AI Business OS — no per-seat data leaving your premises.