✅ PASST IN DEN VRAM (FP8 (W8A8 / E4M3))
DeepSeek-R1-Distill-Llama-8B on NVIDIA RTX 4080 Super (16GB VRAM)
Unquantized FP16 (16.89 GB) exceeds 16 GB, but 4-bit Q4_K_M requires 6.22 GB (39% VRAM utilization) at ~104.3 tokens/sec.
🎛️ Interaktiver VRAM-, Kontext- & Offload-Konfigurator
📊 Echtzeit-Speicher- & Performance-Telemetrie
6.22 GB / 16 GB
🟦 Modellgewichte: 4.73 GB
🟪 KV-Cache: 1 GB
🟨 Engine-Puffer: 0.49 GB
Benötigter GPU-VRAM6.22 GB
Benötigter System-RAM (CPU)0.00 GB
Verfügbarer Hardware-VRAM16 GB (1x NVIDIA RTX 4080 Super)
VRAM-Reserve / Defizit9.78 GB
Geschätzte Decode-Geschwindigkeit~104.3 tokens/sec
Geschätzte Prefill-Geschwindigkeit~3380 tokens/sec
Time-to-First-Token (4K Prompt)~1212 ms
Transformer-TopologieDense-GQA (32L, 8/32 KV/Q heads)
🚀 Startbefehl (vLLM / llama.cpp / Ollama)
ollama run deepseek-r1:8b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf deepseek-ai/DeepSeek-R1-Distill-Llama-8B-GGUF -c 8192 -ngl 99 -fa
CLOUD-GPU PREISVERGLEICH & MIETEN-VS-KAUFEN RECHNER
Nicht genug lokaler VRAM? Cloud-GPU in 60 Sekunden starten (DeepSeek-R1-Distill-Llama-8B: 6.22 GB in 4-bit)
Recommended Cloud Tier: RTX 4090 (24GB) · Full FP16 Needs: 16.89 GB
Amortisationszeit (Kauf vs. Miete): ~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 | 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-R1-Distill-Llama-8B × 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 | 14.96 GB | 1 GB | 16.45 GB | Nein (+0.4GB) | ~26.1 / 3380 tok/s | 2x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 7.74 GB | 1 GB | 9.23 GB | Ja (58%) | ~61.1 / 3380 tok/s | 1x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 7.95 GB | 1 GB | 9.43 GB | Ja (59%) | ~59.3 / 3380 tok/s | 1x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 6.13 GB | 1 GB | 7.62 GB | Ja (48%) | ~76.8 / 3380 tok/s | 1x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 5.43 GB | 1 GB | 6.91 GB | Ja (43%) | ~88.6 / 3380 tok/s | 1x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 4.73 GB | 1 GB | 6.22 GB | Ja (39%) | ~104.3 / 3380 tok/s | 1x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 4.13 GB | 1 GB | 5.61 GB | Ja (35%) | ~118.6 / 3380 tok/s | 1x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 3.79 GB | 1 GB | 5.28 GB | Ja (33%) | ~128.9 / 3380 tok/s | 1x |
Dieses Modell auf anderen GPUs testen
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