❌ ÜBERSCHREITET EINZEL-GPU VRAM (NEEDS 5x NVIDIA RTX A6000)

Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX A6000 (48GB VRAM)

You need 5x NVIDIA RTX A6000 (240 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 / 48 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-VRAM48 GB (1x NVIDIA RTX A6000)
VRAM-Reserve / Defizit-179.32 GB
Geschätzte Decode-Geschwindigkeit~7 tokens/sec
Geschätzte Prefill-Geschwindigkeit~1170 tokens/sec
Time-to-First-Token (4K Prompt)~3501 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 $4,500 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) × NVIDIA RTX A6000

Quantization Format Core / Emb BPW Weights (GiB) KV Cache (8K) Total VRAM 1x NVIDIA RTX A6000 (48GB) Decode / Prefill Min GPUs
FP16 / BF16 (Unquantized) 16 / 16 745.06 GB 1.5 GB 747.05 GB Nein (+699.0GB) ~1.8 / 1170 tok/s 16x
FP8 (W8A8 / E4M3) 8.25 / 8.5 384.23 GB 1.5 GB 386.22 GB Nein (+338.2GB) ~3.7 / 1170 tok/s 9x
GGUF Q8_0 (8.50 bpw) 8.5 / 8.5 395.81 GB 1.5 GB 397.8 GB Nein (+349.8GB) ~3.6 / 1170 tok/s 9x
GGUF Q6_K (6.56 bpw) 6.56 / 6.56 305.47 GB 1.5 GB 307.46 GB Nein (+259.5GB) ~4.9 / 1170 tok/s 7x
GGUF Q5_K_M (5.69 bpw) 5.69 / 6.56 265.17 GB 1.5 GB 267.16 GB Nein (+219.2GB) ~5.7 / 1170 tok/s 6x
GGUF Q4_K_M / AWQ (4.83 bpw) 4.83 / 6.56 225.33 GB 1.5 GB 227.32 GB Nein (+179.3GB) ~7 / 1170 tok/s 5x
GGUF IQ4_XS (4.25 bpw) 4.25 / 5.5 198.21 GB 1.5 GB 200.2 GB Nein (+152.2GB) ~8.2 / 1170 tok/s 5x
GGUF Q3_K_M (3.91 bpw) 3.91 / 5 182.34 GB 1.5 GB 184.33 GB Nein (+136.3GB) ~9.1 / 1170 tok/s 4x

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 L40S 48GB (48GB)

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