✅ LÄUFT NATIV IN FP16 & ALLEN QUANTISIERUNGEN

Qwen2.5-Coder-32B-Instruct on AMD Instinct MI300X 192GB (192GB VRAM)

In 4-bit GGUF Q4_K_M, Qwen2.5-Coder-32B-Instruct consumes 21.23 GB (18.73 GB weights + 2 GB KV cache at 8K context), achieving ~184.1 tokens/sec decode and ~10373 tokens/sec prefill.

🎛️ Interaktiver VRAM-, Kontext- & Offload-Konfigurator

📊 Echtzeit-Speicher- & Performance-Telemetrie

21.23 GB / 192 GB
🟦 Modellgewichte: 18.73 GB 🟪 KV-Cache: 2 GB 🟨 Engine-Puffer: 0.49 GB
Benötigter GPU-VRAM21.23 GB
Benötigter System-RAM (CPU)0.00 GB
Verfügbarer Hardware-VRAM192 GB (1x AMD Instinct MI300X 192GB)
VRAM-Reserve / Defizit170.77 GB
Geschätzte Decode-Geschwindigkeit~184.1 tokens/sec
Geschätzte Prefill-Geschwindigkeit~10373 tokens/sec
Time-to-First-Token (4K Prompt)~395 ms
Transformer-TopologieDense-GQA (64L, 8/40 KV/Q heads)

🚀 Startbefehl (vLLM / llama.cpp / Ollama)

ollama run qwen2.5-coder:32b
# Or llama.cpp server (8K context, Flash Attention):
llama-server -hf Qwen/Qwen2.5-Coder-32B-Instruct-GGUF -c 8192 -ngl 99 -fa
CLOUD-GPU PREISVERGLEICH & MIETEN-VS-KAUFEN RECHNER

Nicht genug lokaler VRAM? Cloud-GPU in 60 Sekunden starten (Qwen2.5-Coder-32B-Instruct: 21.23 GB in 4-bit)

Recommended Cloud Tier: RTX 4090 (24GB) · Full FP16 Needs: 63.96 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: Qwen2.5-Coder-32B-Instruct × 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 61.02 GB 2 GB 63.51 GB Ja (33%) ~55.6 / 10373 tok/s 1x
FP8 (W8A8 / E4M3) 8.25 / 8.5 31.51 GB 2 GB 34 GB Ja (18%) ~107.8 / 10373 tok/s 1x
GGUF Q8_0 (8.50 bpw) 8.5 / 8.5 32.42 GB 2 GB 34.91 GB Ja (18%) ~104.6 / 10373 tok/s 1x
GGUF Q6_K (6.56 bpw) 6.56 / 6.56 25.02 GB 2 GB 27.51 GB Ja (14%) ~135.6 / 10373 tok/s 1x
GGUF Q5_K_M (5.69 bpw) 5.69 / 6.56 21.86 GB 2 GB 24.35 GB Ja (13%) ~156.3 / 10373 tok/s 1x
GGUF Q4_K_M / AWQ (4.83 bpw) 4.83 / 6.56 18.73 GB 2 GB 21.23 GB Ja (11%) ~184.1 / 10373 tok/s 1x
GGUF IQ4_XS (4.25 bpw) 4.25 / 5.5 16.44 GB 2 GB 18.93 GB Ja (10%) ~209.3 / 10373 tok/s 1x
GGUF Q3_K_M (3.91 bpw) 3.91 / 5 15.11 GB 2 GB 17.6 GB Ja (9%) ~227.5 / 10373 tok/s 1x

Dieses Modell auf anderen GPUs testen

Qwen2.5-Coder-32B-Instruct on NVIDIA RTX 5090 (32GB)Qwen2.5-Coder-32B-Instruct on NVIDIA RTX 4090 (24GB)Qwen2.5-Coder-32B-Instruct on NVIDIA RTX 5080 (16GB)Qwen2.5-Coder-32B-Instruct on NVIDIA RTX 4080 Super (16GB)Qwen2.5-Coder-32B-Instruct on NVIDIA RTX 4070 Ti Super (16GB)Qwen2.5-Coder-32B-Instruct on NVIDIA RTX 3090 (24GB)Qwen2.5-Coder-32B-Instruct on NVIDIA RTX 4060 Ti 16GB (16GB)Qwen2.5-Coder-32B-Instruct on NVIDIA RTX 3060 12GB (12GB)Qwen2.5-Coder-32B-Instruct on NVIDIA RTX 6000 Ada (48GB)Qwen2.5-Coder-32B-Instruct on NVIDIA RTX A6000 (48GB)

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