✅ PASST IN DEN VRAM (FP8 (W8A8 / E4M3))
Microsoft Phi-4 14B on NVIDIA RTX 4090 (24GB VRAM)
Unquantized FP16 (29.81 GB) exceeds 24 GB, but 4-bit Q4_K_M requires 10.5 GB (44% VRAM utilization) at ~78.3 tokens/sec.
🎛️ Interaktiver VRAM-, Kontext- & Offload-Konfigurator
📊 Echtzeit-Speicher- & Performance-Telemetrie
10.5 GB / 24 GB
🟦 Modellgewichte: 8.45 GB
🟪 KV-Cache: 1.56 GB
🟨 Engine-Puffer: 0.49 GB
Benötigter GPU-VRAM10.5 GB
Benötigter System-RAM (CPU)0.00 GB
Verfügbarer Hardware-VRAM24 GB (1x NVIDIA RTX 4090)
VRAM-Reserve / Defizit13.50 GB
Geschätzte Decode-Geschwindigkeit~78.3 tokens/sec
Geschätzte Prefill-Geschwindigkeit~2930 tokens/sec
Time-to-First-Token (4K Prompt)~1398 ms
Transformer-TopologieDense-GQA (40L, 10/40 KV/Q heads)
🚀 Startbefehl (vLLM / llama.cpp / Ollama)
ollama run phi4:14b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf microsoft/phi-4-GGUF -c 8192 -ngl 99 -fa
CLOUD-GPU PREISVERGLEICH & MIETEN-VS-KAUFEN RECHNER
Nicht genug lokaler VRAM? Cloud-GPU in 60 Sekunden starten (Microsoft Phi-4 14B: 10.5 GB in 4-bit)
Recommended Cloud Tier: RTX 4090 (24GB) · Full FP16 Needs: 29.81 GB
Amortisationszeit (Kauf vs. Miete): ~32 months (vs $1,699 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: Microsoft Phi-4 14B × NVIDIA RTX 4090
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x NVIDIA RTX 4090 (24GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 27.31 GB | 1.56 GB | 29.36 GB | Nein (+5.4GB) | ~8 / 2930 tok/s | 2x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 14.11 GB | 1.56 GB | 16.16 GB | Ja (67%) | ~45.8 / 2930 tok/s | 1x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 14.51 GB | 1.56 GB | 16.56 GB | Ja (69%) | ~44.5 / 2930 tok/s | 1x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 11.2 GB | 1.56 GB | 13.25 GB | Ja (55%) | ~57.6 / 2930 tok/s | 1x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 9.81 GB | 1.56 GB | 11.87 GB | Ja (49%) | ~66.4 / 2930 tok/s | 1x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 8.45 GB | 1.56 GB | 10.5 GB | Ja (44%) | ~78.3 / 2930 tok/s | 1x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 7.4 GB | 1.56 GB | 9.46 GB | Ja (39%) | ~88.9 / 2930 tok/s | 1x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 6.8 GB | 1.56 GB | 8.86 GB | Ja (37%) | ~96.7 / 2930 tok/s | 1x |
Dieses Modell auf anderen GPUs testen
Microsoft Phi-4 14B on NVIDIA RTX 5090 (32GB)Microsoft Phi-4 14B on NVIDIA RTX 5080 (16GB)Microsoft Phi-4 14B on NVIDIA RTX 4080 Super (16GB)Microsoft Phi-4 14B on NVIDIA RTX 4070 Ti Super (16GB)Microsoft Phi-4 14B on NVIDIA RTX 3090 (24GB)Microsoft Phi-4 14B on NVIDIA RTX 4060 Ti 16GB (16GB)Microsoft Phi-4 14B on NVIDIA RTX 3060 12GB (12GB)Microsoft Phi-4 14B on NVIDIA RTX 6000 Ada (48GB)Microsoft Phi-4 14B on NVIDIA RTX A6000 (48GB)Microsoft Phi-4 14B on NVIDIA L40S 48GB (48GB)
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