✅ CABE EN VRAM (FP8 (W8A8 / E4M3))
Llama-3.1-70B-Instruct on NVIDIA A100 80GB SXM (80GB VRAM)
Unquantized FP16 (134.86 GB) exceeds 80 GB, but 4-bit Q4_K_M requires 43.09 GB (54% VRAM utilization) at ~32.9 tokens/sec.
🎛️ Ajustador Interactivo de VRAM, Contexto y Offload
📊 Telemetría de Memoria y Rendimiento en Tiempo Real
43.09 GB / 80 GB
🟦 Pesos del Modelo: 40.09 GB
🟪 Caché KV: 2.5 GB
🟨 Buffer del Motor: 0.5 GB
VRAM de GPU Requerida43.09 GB
RAM de Sistema Requerida (CPU)0.00 GB
VRAM Disponible en Hardware80 GB (1x NVIDIA A100 80GB SXM)
Margen / Déficit de VRAM36.91 GB
Velocidad de Generación (Decode)~32.9 tokens/sec
Velocidad de Procesamiento (Prefill)~1150 tokens/sec
Tiempo al Primer Token (Prompt 4K)~3562 ms
Topología TransformerDense-GQA (80L, 8/64 KV/Q heads)
🚀 Comando de Lanzamiento Listo para Copiar
ollama run llama3.1:70b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf meta-llama/Llama-3.1-70B-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-3.1-70B-Instruct: 43.09 GB in 4-bit)
Recommended Cloud Tier: L40S / RTX 6000 Ada (48GB) · Full FP16 Needs: 134.86 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-3.1-70B-Instruct × 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 | 131.41 GB | 2.5 GB | 134.41 GB | No (+54.4GB) | ~1.2 / 1150 tok/s | 2x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 67.82 GB | 2.5 GB | 70.82 GB | AJUSTADO (<12% MARGEN) | ~19.3 / 1150 tok/s | 1x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 69.81 GB | 2.5 GB | 72.81 GB | AJUSTADO (<12% MARGEN) | ~18.7 / 1150 tok/s | 1x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 53.88 GB | 2.5 GB | 56.88 GB | Sí (71%) | ~24.2 / 1150 tok/s | 1x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 46.95 GB | 2.5 GB | 49.94 GB | Sí (62%) | ~27.9 / 1150 tok/s | 1x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 40.09 GB | 2.5 GB | 43.09 GB | Sí (54%) | ~32.9 / 1150 tok/s | 1x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 35.21 GB | 2.5 GB | 38.21 GB | Sí (48%) | ~37.4 / 1150 tok/s | 1x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 32.38 GB | 2.5 GB | 35.38 GB | Sí (44%) | ~40.6 / 1150 tok/s | 1x |
Probar este Modelo en Otras GPUs
Llama-3.1-70B-Instruct on NVIDIA RTX 5090 (32GB)Llama-3.1-70B-Instruct on NVIDIA RTX 4090 (24GB)Llama-3.1-70B-Instruct on NVIDIA RTX 5080 (16GB)Llama-3.1-70B-Instruct on NVIDIA RTX 4080 Super (16GB)Llama-3.1-70B-Instruct on NVIDIA RTX 4070 Ti Super (16GB)Llama-3.1-70B-Instruct on NVIDIA RTX 3090 (24GB)Llama-3.1-70B-Instruct on NVIDIA RTX 4060 Ti 16GB (16GB)Llama-3.1-70B-Instruct on NVIDIA RTX 3060 12GB (12GB)Llama-3.1-70B-Instruct on NVIDIA RTX 6000 Ada (48GB)Llama-3.1-70B-Instruct on NVIDIA RTX A6000 (48GB)
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