❌ ÜBERSCHREITET EINZEL-GPU VRAM (NEEDS 2x Apple M4 Max (128GB Unified))
Llama-4-Maverick-17B-128E (400B MoE) on Apple M4 Max (128GB Unified) (128GB VRAM)
You need 2x Apple M4 Max (128GB Unified) (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
227.32 GB / 128 GB
🟦 Modellgewichte: 225.33 GB
🟪 KV-Cache: 1.5 GB
🟨 Engine-Puffer: 0.49 GB
Benötigter GPU-VRAM227.32 GB
Benötigter System-RAM (CPU)0.00 GB
Verfügbarer Hardware-VRAM128 GB (1x Apple M4 Max (128GB Unified))
VRAM-Reserve / Defizit-99.32 GB
Geschätzte Decode-Geschwindigkeit~11 tokens/sec
Geschätzte Prefill-Geschwindigkeit~556 tokens/sec
Time-to-First-Token (4K Prompt)~7363 ms
Transformer-TopologieMoE-GQA (48L, 8/40 KV/Q heads)
🚀 Startbefehl (vLLM / llama.cpp / Ollama)
ollama run llama4:maverick # Or llama.cpp server (8K context, Flash Attention): llama-server -hf meta-llama/Llama-4-Maverick-17B-128E-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-4-Maverick-17B-128E (400B MoE): 227.32 GB in 4-bit)
Recommended Cloud Tier: Multi-GPU H100 / H200 Node · Full FP16 Needs: 747.5 GB
Amortisationszeit (Kauf vs. Miete): ~32 months (vs $4,699 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: Llama-4-Maverick-17B-128E (400B MoE) × Apple M4 Max (128GB Unified)
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x Apple M4 Max (128GB Unified) (128GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 745.06 GB | 1.5 GB | 747.05 GB | Nein (+619.0GB) | ~2 / 556 tok/s | 6x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 384.23 GB | 1.5 GB | 386.22 GB | Nein (+258.2GB) | ~4.6 / 556 tok/s | 4x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 395.81 GB | 1.5 GB | 397.8 GB | Nein (+269.8GB) | ~4.5 / 556 tok/s | 4x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 305.47 GB | 1.5 GB | 307.46 GB | Nein (+179.5GB) | ~6.5 / 556 tok/s | 3x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 265.17 GB | 1.5 GB | 267.16 GB | Nein (+139.2GB) | ~8.2 / 556 tok/s | 3x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 225.33 GB | 1.5 GB | 227.32 GB | Nein (+99.3GB) | ~11 / 556 tok/s | 2x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 198.21 GB | 1.5 GB | 200.2 GB | Nein (+72.2GB) | ~14.3 / 556 tok/s | 2x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 182.34 GB | 1.5 GB | 184.33 GB | Nein (+56.3GB) | ~17.3 / 556 tok/s | 2x |
Dieses Modell auf anderen GPUs testen
Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 5090 (32GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 4090 (24GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 5080 (16GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 4080 Super (16GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 4070 Ti Super (16GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 3090 (24GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 4060 Ti 16GB (16GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 3060 12GB (12GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 6000 Ada (48GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX A6000 (48GB)
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