✅ LÄUFT NATIV IN FP16 & ALLEN QUANTISIERUNGEN

Llama-3.1-70B-Instruct on NVIDIA Blackwell B200 192GB (192GB VRAM)

In 4-bit GGUF Q4_K_M, Llama-3.1-70B-Instruct consumes 43.09 GB (40.09 GB weights + 2.5 GB KV cache at 8K context), achieving ~129.1 tokens/sec decode and ~8292 tokens/sec prefill.

🎛️ Interaktiver VRAM-, Kontext- & Offload-Konfigurator

📊 Echtzeit-Speicher- & Performance-Telemetrie

43.09 GB / 192 GB
🟦 Modellgewichte: 40.09 GB 🟪 KV-Cache: 2.5 GB 🟨 Engine-Puffer: 0.5 GB
Benötigter GPU-VRAM43.09 GB
Benötigter System-RAM (CPU)0.00 GB
Verfügbarer Hardware-VRAM192 GB (1x NVIDIA Blackwell B200 192GB)
VRAM-Reserve / Defizit148.91 GB
Geschätzte Decode-Geschwindigkeit~129.1 tokens/sec
Geschätzte Prefill-Geschwindigkeit~8292 tokens/sec
Time-to-First-Token (4K Prompt)~494 ms
Transformer-TopologieDense-GQA (80L, 8/64 KV/Q heads)

🚀 Startbefehl (vLLM / llama.cpp / Ollama)

ollama run llama3.1:70b
# Or llama.cpp server (8K context, Flash Attention):
llama-server -hf meta-llama/Llama-3.1-70B-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-3.1-70B-Instruct: 43.09 GB in 4-bit)

Recommended Cloud Tier: L40S / RTX 6000 Ada (48GB) · Full FP16 Needs: 134.86 GB

Amortisationszeit (Kauf vs. Miete): ~32 months (vs $42,000 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-3.1-70B-Instruct × NVIDIA Blackwell B200 192GB

Quantization Format Core / Emb BPW Weights (GiB) KV Cache (8K) Total VRAM 1x NVIDIA Blackwell B200 192GB (192GB) Decode / Prefill Min GPUs
FP16 / BF16 (Unquantized) 16 / 16 131.41 GB 2.5 GB 134.41 GB Ja (70%) ~39 / 8292 tok/s 1x
FP8 (W8A8 / E4M3) 8.25 / 8.5 67.82 GB 2.5 GB 70.82 GB Ja (37%) ~75.6 / 8292 tok/s 1x
GGUF Q8_0 (8.50 bpw) 8.5 / 8.5 69.81 GB 2.5 GB 72.81 GB Ja (38%) ~73.3 / 8292 tok/s 1x
GGUF Q6_K (6.56 bpw) 6.56 / 6.56 53.88 GB 2.5 GB 56.88 GB Ja (30%) ~95 / 8292 tok/s 1x
GGUF Q5_K_M (5.69 bpw) 5.69 / 6.56 46.95 GB 2.5 GB 49.94 GB Ja (26%) ~109.6 / 8292 tok/s 1x
GGUF Q4_K_M / AWQ (4.83 bpw) 4.83 / 6.56 40.09 GB 2.5 GB 43.09 GB Ja (22%) ~129.1 / 8292 tok/s 1x
GGUF IQ4_XS (4.25 bpw) 4.25 / 5.5 35.21 GB 2.5 GB 38.21 GB Ja (20%) ~146.7 / 8292 tok/s 1x
GGUF Q3_K_M (3.91 bpw) 3.91 / 5 32.38 GB 2.5 GB 35.38 GB Ja (18%) ~159.4 / 8292 tok/s 1x

Dieses Modell auf anderen GPUs testen

Llama-3.1-70B-Instruct on NVIDIA RTX 5090 (32GB)Llama-3.1-70B-Instruct on NVIDIA RTX 4090 (24GB)Llama-3.1-70B-Instruct on NVIDIA RTX 5080 (16GB)Llama-3.1-70B-Instruct on NVIDIA RTX 4080 Super (16GB)Llama-3.1-70B-Instruct on NVIDIA RTX 4070 Ti Super (16GB)Llama-3.1-70B-Instruct on NVIDIA RTX 3090 (24GB)Llama-3.1-70B-Instruct on NVIDIA RTX 4060 Ti 16GB (16GB)Llama-3.1-70B-Instruct on NVIDIA RTX 3060 12GB (12GB)Llama-3.1-70B-Instruct on NVIDIA RTX 6000 Ada (48GB)Llama-3.1-70B-Instruct on NVIDIA RTX A6000 (48GB)

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