❌ ÜBERSCHREITET EINZEL-GPU VRAM (NEEDS 4x NVIDIA RTX 4060 Ti 16GB)
Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 4060 Ti 16GB (16GB VRAM)
You need 4x NVIDIA RTX 4060 Ti 16GB (64 GB total VRAM) for full GPU execution, or rent an A100/H100 80GB cloud instance.
🎛️ Interaktiver VRAM-, Kontext- & Offload-Konfigurator
📊 Echtzeit-Speicher- & Performance-Telemetrie
63.7 GB / 16 GB
🟦 Modellgewichte: 61.71 GB
🟪 KV-Cache: 1.5 GB
🟨 Engine-Puffer: 0.49 GB
Benötigter GPU-VRAM63.7 GB
Benötigter System-RAM (CPU)0.00 GB
Verfügbarer Hardware-VRAM16 GB (1x NVIDIA RTX 4060 Ti 16GB)
VRAM-Reserve / Defizit-47.70 GB
Geschätzte Decode-Geschwindigkeit~6.9 tokens/sec
Geschätzte Prefill-Geschwindigkeit~667 tokens/sec
Time-to-First-Token (4K Prompt)~6144 ms
Transformer-TopologieMoE-GQA (48L, 8/40 KV/Q heads)
🚀 Startbefehl (vLLM / llama.cpp / Ollama)
ollama run llama4:scout # Or llama.cpp server (8K context, Flash Attention): llama-server -hf meta-llama/Llama-4-Scout-17B-16E-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-Scout-17B-16E (109B MoE): 63.7 GB in 4-bit)
Recommended Cloud Tier: NVIDIA A100 / H100 (80GB) · Full FP16 Needs: 205.47 GB
Amortisationszeit (Kauf vs. Miete): ~32 months (vs $449 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-Scout-17B-16E (109B MoE) × NVIDIA RTX 4060 Ti 16GB
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x NVIDIA RTX 4060 Ti 16GB (16GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 203.03 GB | 1.5 GB | 205.02 GB | Nein (+189.0GB) | ~1.8 / 667 tok/s | 13x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 104.75 GB | 1.5 GB | 106.74 GB | Nein (+90.7GB) | ~3.7 / 667 tok/s | 7x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 107.86 GB | 1.5 GB | 109.85 GB | Nein (+93.8GB) | ~3.6 / 667 tok/s | 7x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 83.24 GB | 1.5 GB | 85.23 GB | Nein (+69.2GB) | ~4.8 / 667 tok/s | 6x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 72.41 GB | 1.5 GB | 74.4 GB | Nein (+58.4GB) | ~5.7 / 667 tok/s | 5x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 61.71 GB | 1.5 GB | 63.7 GB | Nein (+47.7GB) | ~6.9 / 667 tok/s | 4x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 54.23 GB | 1.5 GB | 56.22 GB | Nein (+40.2GB) | ~8.1 / 667 tok/s | 4x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 49.88 GB | 1.5 GB | 51.87 GB | Nein (+35.9GB) | ~9 / 667 tok/s | 4x |
Dieses Modell auf anderen GPUs testen
Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 5090 (32GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 4090 (24GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 5080 (16GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 4080 Super (16GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 4070 Ti Super (16GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 3090 (24GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 3060 12GB (12GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 6000 Ada (48GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX A6000 (48GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA L40S 48GB (48GB)
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