✅ PASST IN DEN VRAM (FP8 (W8A8 / E4M3))
Llama-4-Scout-17B-16E (109B MoE) on AMD Instinct MI300X 192GB (192GB VRAM)
Unquantized FP16 (205.47 GB) exceeds 192 GB, but 4-bit Q4_K_M requires 63.7 GB (33% VRAM utilization) at ~350 tokens/sec.
🎛️ Interaktiver VRAM-, Kontext- & Offload-Konfigurator
📊 Echtzeit-Speicher- & Performance-Telemetrie
63.7 GB / 192 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-VRAM192 GB (1x AMD Instinct MI300X 192GB)
VRAM-Reserve / Defizit128.30 GB
Geschätzte Decode-Geschwindigkeit~350 tokens/sec
Geschätzte Prefill-Geschwindigkeit~18000 tokens/sec
Time-to-First-Token (4K Prompt)~228 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 $19,500 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-Scout-17B-16E (109B MoE) × AMD Instinct MI300X 192GB
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x AMD Instinct MI300X 192GB (192GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 203.03 GB | 1.5 GB | 205.02 GB | Nein (+13.0GB) | ~21.8 / 18000 tok/s | 2x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 104.75 GB | 1.5 GB | 106.74 GB | Ja (56%) | ~205.3 / 18000 tok/s | 1x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 107.86 GB | 1.5 GB | 109.85 GB | Ja (57%) | ~199.3 / 18000 tok/s | 1x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 83.24 GB | 1.5 GB | 85.23 GB | Ja (44%) | ~258.2 / 18000 tok/s | 1x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 72.41 GB | 1.5 GB | 74.4 GB | Ja (39%) | ~297.7 / 18000 tok/s | 1x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 61.71 GB | 1.5 GB | 63.7 GB | Ja (33%) | ~350 / 18000 tok/s | 1x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 54.23 GB | 1.5 GB | 56.22 GB | Ja (29%) | ~350 / 18000 tok/s | 1x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 49.88 GB | 1.5 GB | 51.87 GB | Ja (27%) | ~350 / 18000 tok/s | 1x |
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 4060 Ti 16GB (16GB)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)
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