❌ ÜBERSCHREITET EINZEL-GPU VRAM (NEEDS 3x Apple M4 Pro Mac Mini (64GB))
Qwen3-235B-A22B (MoE) on Apple M4 Pro Mac Mini (64GB) (64GB VRAM)
You need 3x Apple M4 Pro Mac Mini (64GB) (192 GB total VRAM) for full GPU execution, or rent an A100/H100 80GB cloud instance.
🎛️ Interaktiver VRAM-, Kontext- & Offload-Konfigurator
📊 Echtzeit-Speicher- & Performance-Telemetrie
134.4 GB / 64 GB
🟦 Modellgewichte: 132.44 GB
🟪 KV-Cache: 1.47 GB
🟨 Engine-Puffer: 0.49 GB
Benötigter GPU-VRAM134.4 GB
Benötigter System-RAM (CPU)0.00 GB
Verfügbarer Hardware-VRAM64 GB (1x Apple M4 Pro Mac Mini (64GB))
VRAM-Reserve / Defizit-70.40 GB
Geschätzte Decode-Geschwindigkeit~6.7 tokens/sec
Geschätzte Prefill-Geschwindigkeit~215 tokens/sec
Time-to-First-Token (4K Prompt)~19007 ms
Transformer-TopologieMoE-GQA (94L, 4/64 KV/Q heads)
🚀 Startbefehl (vLLM / llama.cpp / Ollama)
ollama run qwen3:235b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf Qwen/Qwen3-235B-A22B-GGUF -c 8192 -ngl 99 -fa
CLOUD-GPU PREISVERGLEICH & MIETEN-VS-KAUFEN RECHNER
Nicht genug lokaler VRAM? Cloud-GPU in 60 Sekunden starten (Qwen3-235B-A22B (MoE): 134.4 GB in 4-bit)
Recommended Cloud Tier: Multi-GPU H100 / H200 Node · Full FP16 Needs: 440.31 GB
Amortisationszeit (Kauf vs. Miete): ~32 months (vs $2,199 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: Qwen3-235B-A22B (MoE) × Apple M4 Pro Mac Mini (64GB)
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x Apple M4 Pro Mac Mini (64GB) (64GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 437.91 GB | 1.47 GB | 439.87 GB | Nein (+375.9GB) | ~1.5 / 215 tok/s | 7x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 225.83 GB | 1.47 GB | 227.79 GB | Nein (+163.8GB) | ~3.2 / 215 tok/s | 4x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 232.64 GB | 1.47 GB | 234.6 GB | Nein (+170.6GB) | ~3.1 / 215 tok/s | 4x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 179.54 GB | 1.47 GB | 181.5 GB | Nein (+117.5GB) | ~4.3 / 215 tok/s | 3x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 155.86 GB | 1.47 GB | 157.81 GB | Nein (+93.8GB) | ~5.3 / 215 tok/s | 3x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 132.44 GB | 1.47 GB | 134.4 GB | Nein (+70.4GB) | ~6.7 / 215 tok/s | 3x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 116.5 GB | 1.47 GB | 118.46 GB | Nein (+54.5GB) | ~8.1 / 215 tok/s | 2x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 107.17 GB | 1.47 GB | 109.13 GB | Nein (+45.1GB) | ~9.3 / 215 tok/s | 2x |
Dieses Modell auf anderen GPUs testen
Qwen3-235B-A22B (MoE) on NVIDIA RTX 5090 (32GB)Qwen3-235B-A22B (MoE) on NVIDIA RTX 4090 (24GB)Qwen3-235B-A22B (MoE) on NVIDIA RTX 5080 (16GB)Qwen3-235B-A22B (MoE) on NVIDIA RTX 4080 Super (16GB)Qwen3-235B-A22B (MoE) on NVIDIA RTX 4070 Ti Super (16GB)Qwen3-235B-A22B (MoE) on NVIDIA RTX 3090 (24GB)Qwen3-235B-A22B (MoE) on NVIDIA RTX 4060 Ti 16GB (16GB)Qwen3-235B-A22B (MoE) on NVIDIA RTX 3060 12GB (12GB)Qwen3-235B-A22B (MoE) on NVIDIA RTX 6000 Ada (48GB)Qwen3-235B-A22B (MoE) on NVIDIA RTX A6000 (48GB)
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