# LLM-Benchmark Konfiguration # Modell : Qwen3-Coder-30B-A3B-Instruct # Engine : llama.cpp # Run-ID : run-20260922-115730-578652 # GPU : AMD Radeon AI PRO R9700 # CPU : AMD Ryzen 3 3100 4-Core Processor # RAM : 31 GB bench@llm-benchmark:~$ llama-server \ -m /home/godcore/models/Qwen3-Coder-30B-A3B-Instruct-Q4_K_M.gguf \ --alias Qwen3-Coder-30B-A3B-Instruct \ -ngl 999 \ -fa on \ -c 49152 \ -np 12 '(AMD' Radeon AI PRO R9700, ROCm 7.2.4, 'HIP_VISIBLE_DEVICES=0)'

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.	Qwen3-Coder-30B-A3B-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/models/Qwen3-Coder-30B-A3B-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

Qwen3-Coder-30B-A3B-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 GeForce RTX 5090 1.716,6 tok/sNVIDIA RTX PRO 6000 Blackwell Workstation Edition 1.690,0 tok/sNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 1.545,3 tok/sNVIDIA GeForce RTX 3090 Ti 928,5 tok/sNVIDIA RTX A6000 789,2 tok/sAMD Radeon RX 7900 XTX 324,9 tok/sAMD Radeon PRO W7800 48GB 289,5 tok/sAMD Radeon PRO W7900 Dual Slot 277,0 tok/s★ AMD Radeon AI PRO R9700 250,2 tok/s this runNVIDIA GB10 (DGX Spark) 182,9 tok/sNVIDIA GeForce RTX 5070 Ti 157,0 tok/sCPU-only 6,7 tok/s

CPUby processor

AMD Ryzen 7 5800X3D 8-Core Processor 1.716,6 tok/sAMD Ryzen 9 9950X 16-Core Processor 1.690,0 tok/sAMD Ryzen Threadripper PRO 9965WX 24-Cores 1.545,3 tok/sAMD Ryzen 9 8945HX with Radeon Graphics 928,5 tok/sAMD Ryzen Threadripper PRO 7955WX 16-Cores 789,2 tok/sAMD Ryzen Threadripper PRO 3955WX 16-Cores 324,9 tok/sAMD Ryzen Threadripper PRO 5975WX 32-Cores 289,5 tok/s★ AMD Ryzen 3 3100 4-Core Processor 250,2 tok/s this runNVIDIA Grace 182,9 tok/sIntel(R) Xeon(R) CPU E5-4620 v2 @ 2.60GHz 6,7 tok/s

MBby mainboard

ASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING 1.716,6 tok/sASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI 1.690,0 tok/sASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE 1.545,3 tok/sMeigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 928,5 tok/sASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 324,9 tok/s★ ASUSTeK COMPUTER INC. ROG STRIX X570-F GAMING 250,2 tok/s this runASUSTeK COMPUTER INC. GX10 182,9 tok/sDell Inc. PowerEdge R820 6,7 tok/s

ENGby engine

★ llama.cpp 1.716,6 tok/s this runvLLM 1.070,6 tok/sunbekannt 88,2 tok/s

DRVby driver

unbekannt 1.716,6 tok/sNVIDIA 590.48.01 / CUDA 13.1 182,9 tok/sAMD 7.0.0-27-generic 88,2 tok/s

💰 Economics

Economics of this run

Operating cost, TCO and comparison with the next-best runs of the same model at identical concurrency (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 20 W

⚡ TDP 300 W

☁️ External LLM (API) show

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

Electricity0.30 EUR/kWh

Avg power (incl. idle)300 W estimated (TDP)GPU 300 W full load

Avg cost / hourEUR 0.090

Electricity / 1M tokensEUR 0.100

Token / kWh3.00M

Acquisition (system)EUR 1,520 partial priceGPU EUR 1,400 · PSU EUR 120

Electricity (2 years)–

TCO (2 years)EUR 3,097

Output tokens (2 years)15.78B

☁️ 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 (20 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

	Qwen3-Coder-30B-A3B-InstructAMD Radeon AI PRO R9700	Qwen3-Coder-30B-A3B-InstructNVIDIA GeForce RTX 5090	Qwen3-Coder-30B-A3B-InstructNVIDIA RTX PRO 6000 Blackwell Workstation Edition	Qwen3-Coder-30B-A3B-Instruct3x NVIDIA RTX PRO 6000 Blackwell Max-Q 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 AI PRO R9700 · this run 250 tok/s NVIDIA GeForce RTX 5090 1,717 tok/s NVIDIA RTX PRO 6000 Blackwell Workstation Edition 1,690 tok/s 3× NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 1,545 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.