Llama 4 locally in 2026: VRAM, GPUs and realistic alternatives
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Damien · LocalIALlama 4 Scout, Maverick, Behemoth: what really fits at home in 2026. VRAM per version, minimum GPUs, and 5 alternatives (70-123B) that compete.
Llama 4 redefined 2026 expectations: native MoE, 10M token context, multimodal. But the VRAM required to run it locally often makes eyes bleed. Here is what really fits at home in 2026, and what has to wait.
The three Llama 4 versions
| Llama 4 Scout | 109B total · 17B active (MoE 16×) | 10M context | ~68 GB Q4 |
| Llama 4 Maverick | 400B total · 17B active (MoE 128×) | 1M context | ~250 GB Q4 |
| Llama 4 Behemoth | ~2T (teacher, not released) | — | Cluster-only |
Llama 4 Scout: feasible but demanding
- 2× RTX A6000 NVLink (96 GB) — fits in Q4 with margin
- 2× RTX 6000 Ada (96 GB) — same, faster
- 1× H100 80 GB — fits in Q4 with tight margin
- 1× H200 (141 GB) or MI300X (192 GB) — fits in Q5/Q6 comfortably
- Does NOT fit: RTX 5090, RTX 4090, single A6000, Mac Studio (works but ~3-5 tok/s)
Real alternatives in 2026
| Llama 3.3 70B | 70B dense, ~52 GB Q5 | Open reference, huge ecosystem |
| Qwen 2.5 72B | 72B dense, ~54 GB Q5 | Excellent code + multilingual |
| DeepSeek R1 Distill 70B | 70B dense, ~52 GB Q5 | State-of-the-art reasoning |
| Mistral Large 123B | 123B dense, ~84 GB Q5 | FR sovereignty, GPT-4-class |
| Mixtral 8x22B | 141B (39B active), ~96 GB | Proven MoE, server throughput |
Default 2026 pick for 90% of SME/agency use cases: Llama 3.3 70B Q5_K_M on a Pro rig (2× RTX 5090). 5× cheaper than a Scout-capable rig, comparable real-world performance in chat/RAG.
Open the calculator / request a quote with your target model, users and constraints.
LlamaGPU2026