Llama 4 lokaal in 2026: VRAM, GPU's en realistische alternatieven
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Damien · LocalIALlama 4 Scout, Maverick, Behemoth: wat thuis echt past in 2026. VRAM per versie, minimale GPU's en 5 competitieve alternatieven 70-123B.
Vertaald artikel. Deze versie is gelokaliseerd zodat internationale pagina's geen Franse artikeltekst tonen. Technische data, prijzen en adviezen blijven gelijk.
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 de calculator / vraag een offerte aan met doelmodel, gebruikers en randvoorwaarden.
LlamaGPU2026