✅ CABE EN VRAM (GGUF Q4_K_M / AWQ (4.83 bpw))
Mistral-Small-3.1-24B-Instruct on NVIDIA RTX 4080 Super (16GB VRAM)
Unquantized FP16 (46.15 GB) exceeds 16 GB, but 4-bit Q4_K_M requires 15.28 GB (96% VRAM utilization) at ~35.5 tokens/sec.
🎛️ Ajustador Interactivo de VRAM, Contexto y Offload
📊 Telemetría de Memoria y Rendimiento en Tiempo Real
15.28 GB / 16 GB
🟦 Pesos del Modelo: 13.54 GB
🟪 Caché KV: 1.25 GB
🟨 Buffer del Motor: 0.49 GB
VRAM de GPU Requerida15.28 GB
RAM de Sistema Requerida (CPU)0.00 GB
VRAM Disponible en Hardware16 GB (1x NVIDIA RTX 4080 Super)
Margen / Déficit de VRAM0.72 GB
Velocidad de Generación (Decode)~35.5 tokens/sec
Velocidad de Procesamiento (Prefill)~1150 tokens/sec
Tiempo al Primer Token (Prompt 4K)~3561 ms
Topología TransformerDense-GQA (40L, 8/32 KV/Q heads)
🚀 Comando de Lanzamiento Listo para Copiar
ollama run mistral-small:24b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf mistralai/Mistral-Small-3.1-24B-Instruct-2503-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 (Mistral-Small-3.1-24B-Instruct: 15.28 GB in 4-bit)
Recommended Cloud Tier: RTX 4090 (24GB) · Full FP16 Needs: 46.15 GB
Meses para amortizar compra vs alquiler: ~32 months (vs $999 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: Mistral-Small-3.1-24B-Instruct × NVIDIA RTX 4080 Super
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x NVIDIA RTX 4080 Super (16GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 43.96 GB | 1.25 GB | 45.7 GB | No (+29.7GB) | ~1.8 / 1150 tok/s | 3x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 22.71 GB | 1.25 GB | 24.45 GB | No (+8.4GB) | ~5.7 / 1150 tok/s | 2x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 23.35 GB | 1.25 GB | 25.09 GB | No (+9.1GB) | ~5.4 / 1150 tok/s | 2x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 18.02 GB | 1.25 GB | 19.76 GB | No (+3.8GB) | ~10.7 / 1150 tok/s | 2x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 15.77 GB | 1.25 GB | 17.51 GB | No (+1.5GB) | ~18.2 / 1150 tok/s | 2x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 13.54 GB | 1.25 GB | 15.28 GB | AJUSTADO (<12% MARGEN) | ~35.5 / 1150 tok/s | 1x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 11.87 GB | 1.25 GB | 13.61 GB | Sí (85%) | ~40.3 / 1150 tok/s | 1x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 10.91 GB | 1.25 GB | 12.65 GB | Sí (79%) | ~43.8 / 1150 tok/s | 1x |
Probar este Modelo en Otras GPUs
Mistral-Small-3.1-24B-Instruct on NVIDIA RTX 5090 (32GB)Mistral-Small-3.1-24B-Instruct on NVIDIA RTX 4090 (24GB)Mistral-Small-3.1-24B-Instruct on NVIDIA RTX 5080 (16GB)Mistral-Small-3.1-24B-Instruct on NVIDIA RTX 4070 Ti Super (16GB)Mistral-Small-3.1-24B-Instruct on NVIDIA RTX 3090 (24GB)Mistral-Small-3.1-24B-Instruct on NVIDIA RTX 4060 Ti 16GB (16GB)Mistral-Small-3.1-24B-Instruct on NVIDIA RTX 3060 12GB (12GB)Mistral-Small-3.1-24B-Instruct on NVIDIA RTX 6000 Ada (48GB)Mistral-Small-3.1-24B-Instruct on NVIDIA RTX A6000 (48GB)Mistral-Small-3.1-24B-Instruct on NVIDIA L40S 48GB (48GB)
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