❌ EXCEDE LA VRAM DE 1 GPU (NEEDS 3x NVIDIA A100 80GB SXM)
Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA A100 80GB SXM (80GB VRAM)
You need 3x NVIDIA A100 80GB SXM (240 GB total VRAM) for full GPU execution, or rent an A100/H100 80GB cloud instance.
🎛️ Ajustador Interactivo de VRAM, Contexto y Offload
📊 Telemetría de Memoria y Rendimiento en Tiempo Real
227.32 GB / 80 GB
🟦 Pesos del Modelo: 225.33 GB
🟪 Caché KV: 1.5 GB
🟨 Buffer del Motor: 0.49 GB
VRAM de GPU Requerida227.32 GB
RAM de Sistema Requerida (CPU)0.00 GB
VRAM Disponible en Hardware80 GB (1x NVIDIA A100 80GB SXM)
Margen / Déficit de VRAM-147.32 GB
Velocidad de Generación (Decode)~8.6 tokens/sec
Velocidad de Procesamiento (Prefill)~4716 tokens/sec
Tiempo al Primer Token (Prompt 4K)~868 ms
Topología TransformerMoE-GQA (48L, 8/40 KV/Q heads)
🚀 Comando de Lanzamiento Listo para Copiar
ollama run llama4:maverick # Or llama.cpp server (8K context, Flash Attention): llama-server -hf meta-llama/Llama-4-Maverick-17B-128E-Instruct-GGUF -c 8192 -ngl 99 -fa
COMPARADOR CLOUD GPU Y CALCULADORA ALQUILAR VS COMPRAR
¿No tienes suficiente VRAM? Alquila una GPU en la nube en 60 segundos (Llama-4-Maverick-17B-128E (400B MoE): 227.32 GB in 4-bit)
Recommended Cloud Tier: Multi-GPU H100 / H200 Node · Full FP16 Needs: 747.5 GB
Meses para amortizar compra vs alquiler: ~32 months (vs $14,500 hardware)
| Cloud Provider | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Est. Monthly Cost (4h/day) | Free Credit / Trial | Deploy |
|---|---|---|---|---|---|---|
| RunPod | $0.44 / hr | $1.39 / hr | $2.39 / hr | $52.80 / mo | Instant PyTorch / vLLM Templates + Serverless GPU | Desplegar en RunPod → |
| Vast.ai | $0.29 / hr | $0.95 / hr | $1.79 / hr | $34.80 / mo | Save 50–70% on RTX 4090 / 5090 / A100 rentals | Alquilar GPU Spot → |
| Vultr Cloud GPU | — | $1.79 / hr | $2.69 / hr | $214.80 / mo (A100) | $300 Free Credit | Reclamar $300 Gratis → |
| DigitalOcean GPU | — | — | $2.99 / hr | $358.80 / mo (H100) | $200 Free Credit (60d) | Obtener $200 Gratis → |
Desglose Completo por Formato de Cuantización: Llama-4-Maverick-17B-128E (400B MoE) × NVIDIA A100 80GB SXM
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x NVIDIA A100 80GB SXM (80GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 745.06 GB | 1.5 GB | 747.05 GB | No (+667.0GB) | ~1.9 / 4716 tok/s | 10x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 384.23 GB | 1.5 GB | 386.22 GB | No (+306.2GB) | ~4.2 / 4716 tok/s | 5x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 395.81 GB | 1.5 GB | 397.8 GB | No (+317.8GB) | ~4 / 4716 tok/s | 5x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 305.47 GB | 1.5 GB | 307.46 GB | No (+227.5GB) | ~5.6 / 4716 tok/s | 4x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 265.17 GB | 1.5 GB | 267.16 GB | No (+187.2GB) | ~6.8 / 4716 tok/s | 4x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 225.33 GB | 1.5 GB | 227.32 GB | No (+147.3GB) | ~8.6 / 4716 tok/s | 3x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 198.21 GB | 1.5 GB | 200.2 GB | No (+120.2GB) | ~10.5 / 4716 tok/s | 3x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 182.34 GB | 1.5 GB | 184.33 GB | No (+104.3GB) | ~12 / 4716 tok/s | 3x |
Probar este Modelo en Otras GPUs
Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 5090 (32GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 4090 (24GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 5080 (16GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 4080 Super (16GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 4070 Ti Super (16GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 3090 (24GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 4060 Ti 16GB (16GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 3060 12GB (12GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 6000 Ada (48GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX A6000 (48GB)
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