❌ EXCEDE LA VRAM DE 1 GPU (NEEDS 4x Apple M4 Pro Mac Mini (64GB))
Llama-3.1-405B-Instruct on Apple M4 Pro Mac Mini (64GB) (64GB VRAM)
You need 4x Apple M4 Pro Mac Mini (64GB) (256 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
233.02 GB / 64 GB
🟦 Pesos del Modelo: 228.57 GB
🟪 Caché KV: 3.94 GB
🟨 Buffer del Motor: 0.52 GB
VRAM de GPU Requerida233.02 GB
RAM de Sistema Requerida (CPU)0.00 GB
VRAM Disponible en Hardware64 GB (1x Apple M4 Pro Mac Mini (64GB))
Margen / Déficit de VRAM-169.02 GB
Velocidad de Generación (Decode)~1.2 tokens/sec
Velocidad de Procesamiento (Prefill)~45 tokens/sec
Tiempo al Primer Token (Prompt 4K)~91022 ms
Topología TransformerDense-GQA (126L, 8/128 KV/Q heads)
🚀 Comando de Lanzamiento Listo para Copiar
ollama run llama3.1:405b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf meta-llama/Llama-3.1-405B-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-3.1-405B-Instruct: 233.02 GB in 4-bit)
Recommended Cloud Tier: Multi-GPU H100 / H200 Node · Full FP16 Needs: 759.28 GB
Meses para amortizar compra vs alquiler: ~32 months (vs $2,199 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-3.1-405B-Instruct × Apple M4 Pro Mac Mini (64GB)
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x Apple M4 Pro Mac Mini (64GB) (64GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 754.37 GB | 3.94 GB | 758.82 GB | No (+694.8GB) | ~1.2 / 45 tok/s | 12x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 389.1 GB | 3.94 GB | 393.55 GB | No (+329.6GB) | ~1.2 / 45 tok/s | 7x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 400.76 GB | 3.94 GB | 405.21 GB | No (+341.2GB) | ~1.2 / 45 tok/s | 7x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 309.29 GB | 3.94 GB | 313.74 GB | No (+249.7GB) | ~1.2 / 45 tok/s | 5x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 268.7 GB | 3.94 GB | 273.15 GB | No (+209.1GB) | ~1.2 / 45 tok/s | 5x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 228.57 GB | 3.94 GB | 233.02 GB | No (+169.0GB) | ~1.2 / 45 tok/s | 4x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 200.99 GB | 3.94 GB | 205.44 GB | No (+141.4GB) | ~1.2 / 45 tok/s | 4x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 184.88 GB | 3.94 GB | 189.34 GB | No (+125.3GB) | ~1.2 / 45 tok/s | 3x |
Probar este Modelo en Otras GPUs
Llama-3.1-405B-Instruct on NVIDIA RTX 5090 (32GB)Llama-3.1-405B-Instruct on NVIDIA RTX 4090 (24GB)Llama-3.1-405B-Instruct on NVIDIA RTX 5080 (16GB)Llama-3.1-405B-Instruct on NVIDIA RTX 4080 Super (16GB)Llama-3.1-405B-Instruct on NVIDIA RTX 4070 Ti Super (16GB)Llama-3.1-405B-Instruct on NVIDIA RTX 3090 (24GB)Llama-3.1-405B-Instruct on NVIDIA RTX 4060 Ti 16GB (16GB)Llama-3.1-405B-Instruct on NVIDIA RTX 3060 12GB (12GB)Llama-3.1-405B-Instruct on NVIDIA RTX 6000 Ada (48GB)Llama-3.1-405B-Instruct on NVIDIA RTX A6000 (48GB)
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