Llama 4 en local en 2026: VRAM, GPUs y alternativas realistas
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Damien · LocalIALlama 4 Scout, Maverick, Behemoth: que pasa realmente en casa en 2026. VRAM por version, GPUs minimos y 5 alternativas 70-123B competitivas.
Articulo traducido. Esta version esta localizada para evitar mezclar interfaces internacionales con texto frances. Los datos tecnicos, importes y recomendaciones se mantienen iguales.
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.
Abre la calculadora / escríbenos para un consejo con tu modelo objetivo, usuarios y restricciones.
Preguntas frecuentes
Can Llama 4 Scout run locally in 2026?+
Yes, in Q4_K_M it needs ~68 GB VRAM (an Enterprise build, 2x A6000 NVLink at 96 GB) or 2x RTX 6000 Ada. A single RTX 5090 (32 GB) is not enough. Llama 4 Scout = 109B total params / 17B active (16x MoE).
What alternatives to Llama 4 if I do not have the rig?+
Llama 3.3 70B (52 GB Q5, on a Pro 2x RTX 5090): covers 90% of cases. Qwen 2.5 72B: equivalent + excellent at code. DeepSeek R1 Distill 70B: top reasoning. Mistral Large 123B: sovereignty + premium multilingual.
Is Llama 4 Maverick (400B) feasible locally?+
Very hard: ~250 GB VRAM in Q4. It needs a multi-node cluster, or an H200 (141 GB) or MI300X (192 GB). Outside a typical SME budget. Prefer Mixtral 8x22B (96 GB, 39B active) for similar MoE quality at a quarter of the price.
Is Llama 4 Scout's 10M-token context usable?+
In theory yes, but the KV cache for 10M tokens in FP16 = +20 GB VRAM per request. In practice, 128k-256k tokens stays the usable maximum with 96 GB VRAM. To go further you need an H200/MI300X or a rolling window.
Wait for Llama 5 or switch to Llama 4 now?+
There is no official Llama 5 roadmap in mid-2026. For an SME waiting, Llama 3.3 70B on a Pro build (~EUR 11,990) is the best ratio. Llama 4 Scout is justified only if you need long context (1M+) or native multimodal.
LlamaGPU2026