# LLM-Benchmark Konfiguration # Modell : Qwen2.5-Coder-32B-Instruct # Engine : llama.cpp # Run-ID : run-20260817-142935-1fb150 # GPU : AMD Radeon PRO W7900 Dual Slot # CPU : AMD Ryzen Threadripper PRO 5975WX 32-Cores # RAM : 247 GB bench@llm-benchmark:~$ /home/godcore/llama.cpp/build_hip/bin/llama-server \ -m /home/godcore/.cache/huggingface/hub/models--bartowski--Qwen2.5-Coder-32B-Instruct-GGUF/snapshots/40b525506a4f98ed425882fa6dfc90cc8139065e/Qwen2.5-Coder-32B-Instruct-Q4_K_M.gguf \ --alias Qwen2.5-Coder-32B-Instruct \ -ngl 999 \ -fa on \ -c 49152 \ -np 12 \ --jinja \ --host 0.0.0.0 \ --port 8000

Engine?Die Inferenz-Software, die das Modell ausliefert (z.B. vLLM oder llama.cpp). Sie bestimmt Geschwindigkeit, unterstuetzte Modellformate und welche Parameter ueberhaupt verfuegbar sind.	llamacpp
Modellalias?Der Name, unter dem das Modell ueber die API angesprochen wird. Genau dieser Wert muss im Request-Feld 'model' stehen.	Qwen2.5-Coder-32B-Instruct
Kontextlaenge?Maximale Anzahl Tokens (Eingabe + erzeugte Ausgabe zusammen), die das Modell pro Anfrage verarbeiten kann.	49152
Modellpfad?Pfad zur GGUF-Modelldatei, die geladen und ausgeliefert wird.	/home/godcore/.cache/huggingface/hub/models--bartowski--Qwen2.5-Coder-32B-Instruct-GGUF/snapshots/40b525506a4f98ed425882fa6dfc90cc8139065e/Qwen2.5-Coder-32B-Instruct-Q4_K_M.gguf
GPU-Layer?Anzahl der auf die GPU ausgelagerten Modell-Layer. Hoeher = mehr VRAM und schneller; der Rest laeuft auf der CPU. 999 = alles auf GPU.	999
fa	on
Kontext?Groesse des Kontextfensters in Token. 0 = der beim Training verwendete Kontext des Modells.	49152
np	12

All benchmarks of this model To leaderboard

Model comparison

Qwen2.5-Coder-32B-Instruct on various hardware

All published performance runs of this model – each bubble a variant: position = prefill (X) × generation (Y), bubble size = number of runs. Closer to the top right = faster. ★ Marked gold = this benchmark.

GPUby graphics card

NVIDIA RTX PRO 6000 Blackwell Workstation Edition 500,6 tok/sNVIDIA GeForce RTX 5090 479,6 tok/sNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 412,8 tok/sNVIDIA GeForce RTX 3090 Ti 236,4 tok/s★ AMD Radeon PRO W7900 Dual Slot 102,1 tok/s this runNVIDIA GeForce RTX 5070 Ti 45,3 tok/sAMD Radeon AI PRO R9700 21,2 tok/s

CPUby processor

AMD Ryzen 9 9950X 16-Core Processor 500,6 tok/sAMD Ryzen 7 5800X3D 8-Core Processor 479,6 tok/sAMD Ryzen Threadripper PRO 9965WX 24-Cores 412,8 tok/sAMD Ryzen 9 8945HX with Radeon Graphics 236,4 tok/s★ AMD Ryzen Threadripper PRO 5975WX 32-Cores 102,1 tok/s this runAMD Ryzen Threadripper PRO 7955WX 16-Cores 21,2 tok/s

MBby mainboard

ASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI 500,6 tok/sASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING 479,6 tok/sASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE 412,8 tok/sMeigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 236,4 tok/s★ ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 102,1 tok/s this run

ENGby engine

vLLM 500,6 tok/s★ llama.cpp 479,6 tok/s this rununbekannt 21,2 tok/s

DRVby driver

unbekannt 500,6 tok/sAMD 7.0.0-27-generic 21,2 tok/s

💰 Economics

Economics of this run

Operating cost, TCO and comparison with the next-best runs of the same model at identical concurrency (1× concurrent). Methodology →

⚙️ ConfigurationAll metrics and charts below follow these settings – based on a 24-month runtime.Save to URL Reset

⚡ Electricity price EUR/kWh

⚙️ System utilization 100 %

🖥️ Acquisition EUR

🔌 Idle 10 W

⚡ TDP 305 W

☁️ External LLM (API) show

Input €/1M Output €/1M Input per output token

Electricity0.30 EUR/kWh

Avg power (incl. idle)305 W estimated (TDP)GPU 295 + Board 10 W full load

Avg cost / hourEUR 0.092

Electricity / 1M tokensEUR 0.92

Token / kWh326.01K

Acquisition (system)EUR 2,126 missingRAM EUR 1,976 · PSU EUR 150

Electricity (2 years)–

TCO (2 years)EUR 3,729

Output tokens (2 years)1.74B

☁️ External LLM (API) – comparison

External LLM cost (2 years)–

Savings vs. external (2 years)–

All values above and the charts below take the configured system utilization into account: at X% the system generates only X% of the time, the rest it idles (10 W). Cost per hour drops (more idle), cost per token rises.

Cost over 2 years – electricity only

Cost over 2 years – incl. acquisition (TCO)

Speed vs. tokens per euro

Euro per 1M tokens

Comparison vs. API – economics per benchmark

	Qwen2.5-Coder-32B-InstructAMD Radeon PRO W7900 Dual Slot	Qwen2.5-Coder-32B-InstructNVIDIA RTX PRO 6000 Blackwell Workstation Edition	Qwen2.5-Coder-32B-InstructNVIDIA GeForce RTX 5090	Qwen2.5-Coder-32B-InstructNVIDIA RTX PRO 6000 Blackwell Workstation Edition
Electricity cost (24 mo.)	–	–	–	–
Acquisition cost	–	–	–	–
Total cost (TCO)	–	–	–	–
Generated tokens (24 mo.)	–	–	–	–
Token price via API	–	–	–	–
Break-even point (days)	–	–	–	–
Result (savings / extra cost)	–	–	–	–

AMD Radeon PRO W7900 Dual Slot · this run 28 tok/s NVIDIA RTX PRO 6000 Blackwell Workstation Edition 70 tok/s NVIDIA GeForce RTX 5090 69 tok/s NVIDIA RTX PRO 6000 Blackwell Workstation Edition 68 tok/s

Comparison with up to 3 next-best runs of this model at the same concurrency (at least one on different hardware). Power = GPU TDP + CPU (idle + 15 %) + board (estimated), acquisition = full system (GPU + CPU + board + RAM + PSU), prices = stored market prices.

Contributed by

Mario Alka Administrator

@marioalka

Ich bin Unternehmer, Softwareentwickler und KI-Enthusiast. Seit vielen Jahren entwickle ich Unternehmenssoftware und beschäftige mich inzwischen fast täglich mit lokalen LLMs, KI-Agenten und leistungsfähiger KI-Hardware.

Mit LLM-Benchmark.de möchte ich eine Plattform schaffen, auf der Modelle, GPUs und Agenten objektiv und reproduzierbar miteinander verglichen werden.