Llama 3.1 405B vs Qwen3 235B-A22B (MoE)
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 | Qwen3 235B-A22B (MoE) | |
|---|---|---|
| Parameters | 405B | 235B total / ~22B active (MoE) |
| Context window | 128K tokens | 128K tokens |
| License | Llama Community License | Apache-2.0 |
| ~VRAM @ 4-bit (Q4_K_M) | ~230 GB | ~130 GB |
| ~VRAM @ 8-bit (Q8_0) | ~410 GB | ~235 GB |
| Minimum device | Supermicro 8x H100 SuperServer | Apple Mac Studio (M2 Ultra) |
| Recommended device | Supermicro 8x H100 SuperServer | Supermicro 8x H100 SuperServer |
| Deployment | Cloud | Cloud |
| Capabilities | Tools, Reasoning, Multilingual, Long context | Tools, Reasoning, Code, Multilingual, Long context |
Highlighted cells mark the lighter / longer / more permissive side per row, for local deployment. Informational rows have no winner.
Bottom line
Qwen3 235B-A22B (MoE) (~235B) 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, Qwen3 235B-A22B (MoE) needs about 130GB versus ~230GB, a meaningful gap when choosing a GPU. Both target a 128K context window. Qwen3 235B-A22B (MoE)'s Apache-2.0 license is the more permissive of the two for commercial use. Minimum viable hardware differs: Llama 3.1 405B starts on a Supermicro 8x H100 SuperServer, Qwen3 235B-A22B (MoE) on a Apple Mac Studio (M2 Ultra). 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 Qwen3 235B-A22B (MoE) if you want the lighter footprint and cheaper hardware, or you want the more permissive Apache-2.0 license.
Datacenter tier — realistically a multi-GPU / multi-node or cloud target even at 4-bit.
Datacenter / multi-GPU or cloud. Mixture-of-experts: large total memory, but only ~22B params activate per token.
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.