❌ EXCEDE LA VRAM DE 1 GPU (NEEDS 2x NVIDIA RTX A6000)
Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX A6000 (48GB VRAM)
You need 2x NVIDIA RTX A6000 (96 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
63.7 GB / 48 GB
🟦 Pesos del Modelo: 61.71 GB
🟪 Caché KV: 1.5 GB
🟨 Buffer del Motor: 0.49 GB
VRAM de GPU Requerida63.7 GB
RAM de Sistema Requerida (CPU)0.00 GB
VRAM Disponible en Hardware48 GB (1x NVIDIA RTX A6000)
Margen / Déficit de VRAM-15.70 GB
Velocidad de Generación (Decode)~17.2 tokens/sec
Velocidad de Procesamiento (Prefill)~1170 tokens/sec
Tiempo al Primer Token (Prompt 4K)~3501 ms
Topología TransformerMoE-GQA (48L, 8/40 KV/Q heads)
🚀 Comando de Lanzamiento Listo para Copiar
ollama run llama4:scout # Or llama.cpp server (8K context, Flash Attention): llama-server -hf meta-llama/Llama-4-Scout-17B-16E-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-Scout-17B-16E (109B MoE): 63.7 GB in 4-bit)
Recommended Cloud Tier: NVIDIA A100 / H100 (80GB) · Full FP16 Needs: 205.47 GB
Meses para amortizar compra vs alquiler: ~32 months (vs $4,500 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-Scout-17B-16E (109B MoE) × NVIDIA RTX A6000
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x NVIDIA RTX A6000 (48GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 203.03 GB | 1.5 GB | 205.02 GB | No (+157.0GB) | ~2.2 / 1170 tok/s | 5x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 104.75 GB | 1.5 GB | 106.74 GB | No (+58.7GB) | ~5.5 / 1170 tok/s | 3x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 107.86 GB | 1.5 GB | 109.85 GB | No (+61.8GB) | ~5.3 / 1170 tok/s | 3x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 83.24 GB | 1.5 GB | 85.23 GB | No (+37.2GB) | ~8.4 / 1170 tok/s | 2x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 72.41 GB | 1.5 GB | 74.4 GB | No (+26.4GB) | ~11.3 / 1170 tok/s | 2x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 61.71 GB | 1.5 GB | 63.7 GB | No (+15.7GB) | ~17.2 / 1170 tok/s | 2x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 54.23 GB | 1.5 GB | 56.22 GB | No (+8.2GB) | ~26.7 / 1170 tok/s | 2x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 49.88 GB | 1.5 GB | 51.87 GB | No (+3.9GB) | ~39.4 / 1170 tok/s | 2x |
Probar este Modelo en Otras GPUs
Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 5090 (32GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 4090 (24GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 5080 (16GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 4080 Super (16GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 4070 Ti Super (16GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 3090 (24GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 4060 Ti 16GB (16GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 3060 12GB (12GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 6000 Ada (48GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA L40S 48GB (48GB)
Otros Modelos en esta GPU
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