❌ ÜBERSCHREITET EINZEL-GPU VRAM (NEEDS 4x Apple M4 Pro Mac Mini (64GB))
Llama-3.1-405B-Instruct on Apple M4 Pro Mac Mini (64GB) (64GB VRAM)
You need 4x Apple M4 Pro Mac Mini (64GB) (256 GB total VRAM) for full GPU execution, or rent an A100/H100 80GB cloud instance.
🎛️ Interaktiver VRAM-, Kontext- & Offload-Konfigurator
📊 Echtzeit-Speicher- & Performance-Telemetrie
233.02 GB / 64 GB
🟦 Modellgewichte: 228.57 GB
🟪 KV-Cache: 3.94 GB
🟨 Engine-Puffer: 0.52 GB
Benötigter GPU-VRAM233.02 GB
Benötigter System-RAM (CPU)0.00 GB
Verfügbarer Hardware-VRAM64 GB (1x Apple M4 Pro Mac Mini (64GB))
VRAM-Reserve / Defizit-169.02 GB
Geschätzte Decode-Geschwindigkeit~1.2 tokens/sec
Geschätzte Prefill-Geschwindigkeit~45 tokens/sec
Time-to-First-Token (4K Prompt)~91022 ms
Transformer-TopologieDense-GQA (126L, 8/128 KV/Q heads)
🚀 Startbefehl (vLLM / llama.cpp / Ollama)
ollama run llama3.1:405b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf meta-llama/Llama-3.1-405B-Instruct-GGUF -c 8192 -ngl 99 -fa
CLOUD-GPU PREISVERGLEICH & MIETEN-VS-KAUFEN RECHNER
Nicht genug lokaler VRAM? Cloud-GPU in 60 Sekunden starten (Llama-3.1-405B-Instruct: 233.02 GB in 4-bit)
Recommended Cloud Tier: Multi-GPU H100 / H200 Node · Full FP16 Needs: 759.28 GB
Amortisationszeit (Kauf vs. Miete): ~32 months (vs $2,199 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: Llama-3.1-405B-Instruct × Apple M4 Pro Mac Mini (64GB)
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x Apple M4 Pro Mac Mini (64GB) (64GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 754.37 GB | 3.94 GB | 758.82 GB | Nein (+694.8GB) | ~1.2 / 45 tok/s | 12x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 389.1 GB | 3.94 GB | 393.55 GB | Nein (+329.6GB) | ~1.2 / 45 tok/s | 7x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 400.76 GB | 3.94 GB | 405.21 GB | Nein (+341.2GB) | ~1.2 / 45 tok/s | 7x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 309.29 GB | 3.94 GB | 313.74 GB | Nein (+249.7GB) | ~1.2 / 45 tok/s | 5x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 268.7 GB | 3.94 GB | 273.15 GB | Nein (+209.1GB) | ~1.2 / 45 tok/s | 5x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 228.57 GB | 3.94 GB | 233.02 GB | Nein (+169.0GB) | ~1.2 / 45 tok/s | 4x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 200.99 GB | 3.94 GB | 205.44 GB | Nein (+141.4GB) | ~1.2 / 45 tok/s | 4x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 184.88 GB | 3.94 GB | 189.34 GB | Nein (+125.3GB) | ~1.2 / 45 tok/s | 3x |
Dieses Modell auf anderen GPUs testen
Llama-3.1-405B-Instruct on NVIDIA RTX 5090 (32GB)Llama-3.1-405B-Instruct on NVIDIA RTX 4090 (24GB)Llama-3.1-405B-Instruct on NVIDIA RTX 5080 (16GB)Llama-3.1-405B-Instruct on NVIDIA RTX 4080 Super (16GB)Llama-3.1-405B-Instruct on NVIDIA RTX 4070 Ti Super (16GB)Llama-3.1-405B-Instruct on NVIDIA RTX 3090 (24GB)Llama-3.1-405B-Instruct on NVIDIA RTX 4060 Ti 16GB (16GB)Llama-3.1-405B-Instruct on NVIDIA RTX 3060 12GB (12GB)Llama-3.1-405B-Instruct on NVIDIA RTX 6000 Ada (48GB)Llama-3.1-405B-Instruct on NVIDIA RTX A6000 (48GB)
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