⚠️ KNAPP (<12% RESERVE) (GGUF IQ4_XS (4.25 bpw))
Qwen3-235B-A22B (MoE) on Apple M4 Max (128GB Unified) (128GB VRAM)
Standard Q4_K_M (134.4 GB) exceeds 128 GB by 6.40 GB. Use GGUF IQ4_XS (4.25 bpw), offload layers to system RAM, or use 2x GPUs.
🎛️ Interaktiver VRAM-, Kontext- & Offload-Konfigurator
📊 Echtzeit-Speicher- & Performance-Telemetrie
134.4 GB / 128 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-VRAM128 GB (1x Apple M4 Max (128GB Unified))
VRAM-Reserve / Defizit-6.40 GB
Geschätzte Decode-Geschwindigkeit~23.9 tokens/sec
Geschätzte Prefill-Geschwindigkeit~431 tokens/sec
Time-to-First-Token (4K Prompt)~9504 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 $4,699 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 Max (128GB Unified)
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x Apple M4 Max (128GB Unified) (128GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 437.91 GB | 1.47 GB | 439.87 GB | Nein (+311.9GB) | ~1.8 / 431 tok/s | 4x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 225.83 GB | 1.47 GB | 227.79 GB | Nein (+99.8GB) | ~5 / 431 tok/s | 2x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 232.64 GB | 1.47 GB | 234.6 GB | Nein (+106.6GB) | ~4.7 / 431 tok/s | 2x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 179.54 GB | 1.47 GB | 181.5 GB | Nein (+53.5GB) | ~8.2 / 431 tok/s | 2x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 155.86 GB | 1.47 GB | 157.81 GB | Nein (+29.8GB) | ~12.2 / 431 tok/s | 2x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 132.44 GB | 1.47 GB | 134.4 GB | Nein (+6.4GB) | ~23.9 / 431 tok/s | 2x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 116.5 GB | 1.47 GB | 118.46 GB | KNAPP (<12% RESERVE) | ~34.8 / 431 tok/s | 1x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 107.17 GB | 1.47 GB | 109.13 GB | Ja (85%) | ~37.8 / 431 tok/s | 1x |
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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