✅ LÄUFT NATIV IN FP16 & ALLEN QUANTISIERUNGEN
Llama-3.2-3B-Instruct on NVIDIA RTX 3060 12GB (12GB VRAM)
In 4-bit GGUF Q4_K_M, Llama-3.2-3B-Instruct consumes 3.33 GB (1.96 GB weights + 0.88 GB KV cache at 8K context), achieving ~127.6 tokens/sec decode and ~2057 tokens/sec prefill.
🎛️ Interaktiver VRAM-, Kontext- & Offload-Konfigurator
📊 Echtzeit-Speicher- & Performance-Telemetrie
3.33 GB / 12 GB
🟦 Modellgewichte: 1.96 GB
🟪 KV-Cache: 0.88 GB
🟨 Engine-Puffer: 0.49 GB
Benötigter GPU-VRAM3.33 GB
Benötigter System-RAM (CPU)0.00 GB
Verfügbarer Hardware-VRAM12 GB (1x NVIDIA RTX 3060 12GB)
VRAM-Reserve / Defizit8.67 GB
Geschätzte Decode-Geschwindigkeit~127.6 tokens/sec
Geschätzte Prefill-Geschwindigkeit~2057 tokens/sec
Time-to-First-Token (4K Prompt)~1991 ms
Transformer-TopologieDense-GQA (28L, 8/24 KV/Q heads)
🚀 Startbefehl (vLLM / llama.cpp / Ollama)
ollama run llama3.2:3b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf meta-llama/Llama-3.2-3B-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-3.2-3B-Instruct: 3.33 GB in 4-bit)
Recommended Cloud Tier: RTX 4090 (24GB) · Full FP16 Needs: 7.78 GB
Amortisationszeit (Kauf vs. Miete): ~32 months (vs $289 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-3.2-3B-Instruct × NVIDIA RTX 3060 12GB
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x NVIDIA RTX 3060 12GB (12GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 5.98 GB | 0.88 GB | 7.34 GB | Ja (61%) | ~38.5 / 2057 tok/s | 1x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 3.11 GB | 0.88 GB | 4.47 GB | Ja (37%) | ~74.7 / 2057 tok/s | 1x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 3.18 GB | 0.88 GB | 4.54 GB | Ja (38%) | ~72.5 / 2057 tok/s | 1x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 2.45 GB | 0.88 GB | 3.81 GB | Ja (32%) | ~94 / 2057 tok/s | 1x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 2.21 GB | 0.88 GB | 3.57 GB | Ja (30%) | ~108.4 / 2057 tok/s | 1x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 1.96 GB | 0.88 GB | 3.33 GB | Ja (28%) | ~127.6 / 2057 tok/s | 1x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 1.7 GB | 0.88 GB | 3.06 GB | Ja (26%) | ~145.1 / 2057 tok/s | 1x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 1.56 GB | 0.88 GB | 2.92 GB | Ja (24%) | ~157.7 / 2057 tok/s | 1x |
Dieses Modell auf anderen GPUs testen
Llama-3.2-3B-Instruct on NVIDIA RTX 5090 (32GB)Llama-3.2-3B-Instruct on NVIDIA RTX 4090 (24GB)Llama-3.2-3B-Instruct on NVIDIA RTX 5080 (16GB)Llama-3.2-3B-Instruct on NVIDIA RTX 4080 Super (16GB)Llama-3.2-3B-Instruct on NVIDIA RTX 4070 Ti Super (16GB)Llama-3.2-3B-Instruct on NVIDIA RTX 3090 (24GB)Llama-3.2-3B-Instruct on NVIDIA RTX 4060 Ti 16GB (16GB)Llama-3.2-3B-Instruct on NVIDIA RTX 6000 Ada (48GB)Llama-3.2-3B-Instruct on NVIDIA RTX A6000 (48GB)Llama-3.2-3B-Instruct on NVIDIA L40S 48GB (48GB)
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