❌ EXCEDE LA VRAM DE 1 GPU (NEEDS 4x NVIDIA RTX 4070 Ti Super)
Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 4070 Ti Super (16GB VRAM)
You need 4x NVIDIA RTX 4070 Ti Super (64 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 / 16 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 Hardware16 GB (1x NVIDIA RTX 4070 Ti Super)
Margen / Déficit de VRAM-47.70 GB
Velocidad de Generación (Decode)~7.3 tokens/sec
Velocidad de Procesamiento (Prefill)~1333 tokens/sec
Tiempo al Primer Token (Prompt 4K)~3072 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 $799 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 4070 Ti Super
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x NVIDIA RTX 4070 Ti Super (16GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 203.03 GB | 1.5 GB | 205.02 GB | No (+189.0GB) | ~1.8 / 1333 tok/s | 13x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 104.75 GB | 1.5 GB | 106.74 GB | No (+90.7GB) | ~3.8 / 1333 tok/s | 7x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 107.86 GB | 1.5 GB | 109.85 GB | No (+93.8GB) | ~3.7 / 1333 tok/s | 7x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 83.24 GB | 1.5 GB | 85.23 GB | No (+69.2GB) | ~5 / 1333 tok/s | 6x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 72.41 GB | 1.5 GB | 74.4 GB | No (+58.4GB) | ~5.9 / 1333 tok/s | 5x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 61.71 GB | 1.5 GB | 63.7 GB | No (+47.7GB) | ~7.3 / 1333 tok/s | 4x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 54.23 GB | 1.5 GB | 56.22 GB | No (+40.2GB) | ~8.6 / 1333 tok/s | 4x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 49.88 GB | 1.5 GB | 51.87 GB | No (+35.9GB) | ~9.6 / 1333 tok/s | 4x |
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 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 RTX A6000 (48GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA L40S 48GB (48GB)
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