Llama 3.1 70B 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 70B | Qwen3 235B-A22B (MoE) | |
|---|---|---|
| Parameters | 70B | 235B total / ~22B active (MoE) |
| Context window | 128K tokens | 128K tokens |
| License | Llama Community License | Apache-2.0 |
| ~VRAM @ 4-bit (Q4_K_M) | ~42 GB | ~130 GB |
| ~VRAM @ 8-bit (Q8_0) | ~75 GB | ~235 GB |
| Minimum device | NVIDIA RTX A6000 | Apple Mac Studio (M2 Ultra) |
| Recommended device | Supermicro 8x H100 SuperServer | Supermicro 8x H100 SuperServer |
| Deployment | Hybrid | 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
Llama 3.1 70B (~70B) is lighter than Qwen3 235B-A22B (MoE) (~235B), so it runs on more modest hardware, while Qwen3 235B-A22B (MoE) trades a larger footprint for more capacity. At 4-bit, Llama 3.1 70B needs about 42GB versus ~130GB, 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 70B starts on a NVIDIA RTX A6000, 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 70B if you want the lighter footprint and cheaper hardware.
Pick Qwen3 235B-A22B (MoE) if you have the memory to spare and want the larger model, or you want the more permissive Apache-2.0 license.
Flagship tier — ~42GB at 4-bit needs a 48GB card, a 64GB+ unified-memory Mac, or multi-GPU.
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.