# LLM-Benchmark Konfiguration # Modell : Qwen2.5-32B-Instruct-AWQ # Engine : vLLM # Run-ID : run-20260827-094103-05ffd9 # GPU : 2x NVIDIA RTX A6000 # CPU : AMD Ryzen Threadripper PRO 7955WX 16-Cores # RAM : 184 GB bench@llm-benchmark:~$ vllm \ --tensor-parallel-size 2 \ --max-model-len 16384 '(2x' RTX A6000 '48GB)'

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.	vllm
Kontextlaenge?Maximale Anzahl Tokens (Eingabe + erzeugte Ausgabe zusammen), die das Modell pro Anfrage verarbeiten kann.	16384
Tensor-Parallel?Anzahl GPUs, auf die JEDER einzelne Modell-Layer aufgeteilt wird (Tensor-Parallelitaet). Mehr GPUs = mehr VRAM und meist mehr Speed, aber die Anzahl der Attention-Heads muss durch diesen Wert teilbar sein (z.B. 32 Heads -> nur 1, 2, 4, 8 ... moeglich, NICHT 3).	2
Kontext?Groesse des Kontextfensters in Token. 0 = der beim Training verwendete Kontext des Modells.	16384

All benchmarks of this model To leaderboard

Model comparison

Qwen2.5-32B-Instruct-AWQ 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 510,3 tok/sNVIDIA GeForce RTX 5090 473,9 tok/sNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 412,6 tok/s★ NVIDIA RTX A6000 332,1 tok/s this runNVIDIA GeForce RTX 3090 Ti 223,8 tok/sAMD Radeon PRO W7900 Dual Slot 89,6 tok/sNVIDIA GeForce RTX 5070 Ti 45,0 tok/sAMD Radeon AI PRO R9700 22,3 tok/s

CPUby processor

AMD Ryzen 9 9950X 16-Core Processor 510,3 tok/sAMD Ryzen 7 5800X3D 8-Core Processor 473,9 tok/sAMD Ryzen Threadripper PRO 9965WX 24-Cores 412,6 tok/s★ AMD Ryzen Threadripper PRO 7955WX 16-Cores 332,1 tok/s this runAMD Ryzen 9 8945HX with Radeon Graphics 223,8 tok/sAMD Ryzen Threadripper PRO 5975WX 32-Cores 89,6 tok/s

MBby mainboard

ASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI 510,3 tok/sASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING 473,9 tok/s★ ASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE 412,6 tok/s this runMeigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 223,8 tok/sASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 89,6 tok/s

ENGby engine

★ vLLM 510,3 tok/s this runllama.cpp 474,0 tok/s

DRVby driver

unbekannt 510,3 tok/s

💰 Economics

Economics of this run

Operating cost, TCO and comparison with the next-best runs of the same model at identical concurrency (10× 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 95 W

⚡ TDP 671 W

☁️ External LLM (API) show

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

Electricity0.30 EUR/kWh

Avg power (incl. idle)671 W estimated (TDP)GPU 600 + CPU 46 + Board 25 W full load

Avg cost / hourEUR 0.20

Electricity / 1M tokensEUR 0.17

Token / kWh1.78M

Acquisition (system)EUR 11,454 full priceGPU EUR 6,000 · CPU EUR 1,399 · Board EUR 1,299 · RAM EUR 2,576 · PSU EUR 180

Electricity (2 years)–

TCO (2 years)EUR 14,981

Output tokens (2 years)20.94B

☁️ 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 (95 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

	Qwen2.5-32B-Instruct-AWQ2x NVIDIA RTX A6000	Qwen2.5-32B-Instruct-AWQNVIDIA RTX PRO 6000 Blackwell Workstation Edition	Qwen2.5-32B-Instruct-AWQNVIDIA RTX PRO 6000 Blackwell Workstation Edition	Qwen2.5-32B-Instruct-AWQNVIDIA GeForce RTX 5090
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)	–	–	–	–

2× NVIDIA RTX A6000 · this run 332 tok/s NVIDIA RTX PRO 6000 Blackwell Workstation Edition 510 tok/s NVIDIA RTX PRO 6000 Blackwell Workstation Edition 474 tok/s NVIDIA GeForce RTX 5090 474 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.