❌ ÜBERSCHREITET EINZEL-GPU VRAM (NEEDS 24x NVIDIA RTX 4060 Ti 16GB)
DeepSeek-V3-0324 671B (MoE) on NVIDIA RTX 4060 Ti 16GB (16GB VRAM)
You need 24x NVIDIA RTX 4060 Ti 16GB (384 GB total VRAM) for full GPU execution, or rent an A100/H100 80GB cloud instance.
🎛️ Interaktiver VRAM-, Kontext- & Offload-Konfigurator
📊 Echtzeit-Speicher- & Performance-Telemetrie
378.7 GB / 16 GB
🟦 Modellgewichte: 377.67 GB
🟪 KV-Cache: 0.54 GB
🟨 Engine-Puffer: 0.5 GB
Benötigter GPU-VRAM378.7 GB
Benötigter System-RAM (CPU)0.00 GB
Verfügbarer Hardware-VRAM16 GB (1x NVIDIA RTX 4060 Ti 16GB)
VRAM-Reserve / Defizit-362.70 GB
Geschätzte Decode-Geschwindigkeit~2.7 tokens/sec
Geschätzte Prefill-Geschwindigkeit~310 tokens/sec
Time-to-First-Token (4K Prompt)~13218 ms
Transformer-TopologieMLA-MoE (61L, 8/128 KV/Q heads)
🚀 Startbefehl (vLLM / llama.cpp / Ollama)
ollama run deepseek-v3:671b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf deepseek-ai/DeepSeek-V3-0324-GGUF -c 8192 -ngl 99 -fa
CLOUD-GPU PREISVERGLEICH & MIETEN-VS-KAUFEN RECHNER
Nicht genug lokaler VRAM? Cloud-GPU in 60 Sekunden starten (DeepSeek-V3-0324 671B (MoE): 378.7 GB in 4-bit)
Recommended Cloud Tier: Multi-GPU H100 / H200 Node · Full FP16 Needs: 1251.32 GB
Amortisationszeit (Kauf vs. Miete): ~32 months (vs $449 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 | Auf RunPod starten → |
| Vast.ai | $0.29 / hr | $0.95 / hr | $1.79 / hr | $34.80 / mo | Save 50–70% on RTX 4090 / 5090 / A100 rentals | Spot-GPU mieten → |
| Vultr Cloud GPU | — | $1.79 / hr | $2.69 / hr | $214.80 / mo (A100) | $300 Free Credit | $300 Guthaben sichern → |
| DigitalOcean GPU | — | — | $2.99 / hr | $358.80 / mo (H100) | $200 Free Credit (60d) | $200 Startguthaben → |
Alle Quantisierungsstufen im Detail: DeepSeek-V3-0324 671B (MoE) × NVIDIA RTX 4060 Ti 16GB
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x NVIDIA RTX 4060 Ti 16GB (16GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 1249.83 GB | 0.54 GB | 1250.87 GB | Nein (+1234.9GB) | ~1.2 / 310 tok/s | 79x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 644.5 GB | 0.54 GB | 645.53 GB | Nein (+629.5GB) | ~1.6 / 310 tok/s | 41x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 663.97 GB | 0.54 GB | 665.01 GB | Nein (+649.0GB) | ~1.6 / 310 tok/s | 42x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 512.43 GB | 0.54 GB | 513.46 GB | Nein (+497.5GB) | ~2 / 310 tok/s | 33x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 444.66 GB | 0.54 GB | 445.69 GB | Nein (+429.7GB) | ~2.3 / 310 tok/s | 28x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 377.67 GB | 0.54 GB | 378.7 GB | Nein (+362.7GB) | ~2.7 / 310 tok/s | 24x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 332.26 GB | 0.54 GB | 333.29 GB | Nein (+317.3GB) | ~3.1 / 310 tok/s | 21x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 305.66 GB | 0.54 GB | 306.7 GB | Nein (+290.7GB) | ~3.4 / 310 tok/s | 20x |
Dieses Modell auf anderen GPUs testen
DeepSeek-V3-0324 671B (MoE) on NVIDIA RTX 5090 (32GB)DeepSeek-V3-0324 671B (MoE) on NVIDIA RTX 4090 (24GB)DeepSeek-V3-0324 671B (MoE) on NVIDIA RTX 5080 (16GB)DeepSeek-V3-0324 671B (MoE) on NVIDIA RTX 4080 Super (16GB)DeepSeek-V3-0324 671B (MoE) on NVIDIA RTX 4070 Ti Super (16GB)DeepSeek-V3-0324 671B (MoE) on NVIDIA RTX 3090 (24GB)DeepSeek-V3-0324 671B (MoE) on NVIDIA RTX 3060 12GB (12GB)DeepSeek-V3-0324 671B (MoE) on NVIDIA RTX 6000 Ada (48GB)DeepSeek-V3-0324 671B (MoE) on NVIDIA RTX A6000 (48GB)DeepSeek-V3-0324 671B (MoE) on NVIDIA L40S 48GB (48GB)
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