# LLM-Benchmark Konfiguration # Modell : Qwen3-Coder-30B-A3B-Instruct # Engine : llama.cpp # Run-ID : run-20260723-015519-32e1d8 # GPU : Keine GPU (CPU-only) # CPU : 4x Intel(R) Xeon(R) CPU E5-4620 v2 @ 2.60GHz # RAM : 504 GB bench@llm-benchmark:~$ /opt/llama.cpp/build/bin/llama-server \ -m /opt/models/Qwen3-Coder-30B-A3B-Instruct-Q4_K_M.gguf \ -a Qwen3-Coder-30B-A3B-Instruct \ --host 0.0.0.0 \ --port 8080 \ --numa distribute \ -t 64 \ -tb 64 \ -c 8192 \ --parallel 4

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.	8192
Modellpfad?Pfad zur GGUF-Modelldatei, die geladen und ausgeliefert wird.	/opt/models/Qwen3-Coder-30B-A3B-Instruct-Q4_K_M.gguf
Alias?Anzeigename des Modells nach aussen (served model name), unabhaengig vom Dateinamen.	Qwen3-Coder-30B-A3B-Instruct
NUMA?NUMA-Optimierung fuer Multi-Socket-CPUs: distribute/isolate/numactl. Verbessert die Speicherlokalitaet.	distribute
Threads?Anzahl CPU-Threads fuer die Token-Generierung (Decode).	64
Batch-Threads?Anzahl CPU-Threads fuer Prompt-Verarbeitung und Batch (Prefill).	64
Kontext?Groesse des Kontextfensters in Token. 0 = der beim Training verwendete Kontext des Modells.	8192
Parallel?Anzahl paralleler Slots/Sequenzen, die der Server gleichzeitig bedient. Der Kontext wird auf die Slots aufgeteilt.	4
execution_type	local

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/sNVIDIA GeForce RTX 4090 400,4 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/sAMD Radeon AI PRO R9700 250,2 tok/sNVIDIA GB10 (DGX Spark) 182,9 tok/sNVIDIA GeForce RTX 5070 Ti 157,0 tok/s★ CPU-only 6,7 tok/s this run

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 3 3100 4-Core Processor 400,4 tok/sAMD Ryzen Threadripper PRO 3955WX 16-Cores 324,9 tok/sAMD Ryzen Threadripper PRO 5975WX 32-Cores 289,5 tok/sIntel(R) Xeon(R) CPU E5-4657L v2 @ 2.40GHz 234,1 tok/sNVIDIA Grace 182,9 tok/s★ Intel(R) Xeon(R) CPU E5-4620 v2 @ 2.60GHz 6,7 tok/s this run

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/sMicro-Star International Co., Ltd. B550M PRO-VDH WIFI (MS-7C95) 400,4 tok/sASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 324,9 tok/sASUSTeK COMPUTER INC. ROG STRIX X570-F GAMING 250,2 tok/s★ Dell Inc. PowerEdge R820 234,1 tok/s this runASUSTeK COMPUTER INC. GX10 182,9 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 35 W

⚡ TDP 105 W

☁️ External LLM (API) show

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

Electricity0.30 EUR/kWh

Avg power (incl. idle)105 W estimated (TDP)CPU 95 + Board 10 W full load

Avg cost / hourEUR 0.032

Electricity / 1M tokensEUR 8.84

Token / kWh33.94K

Acquisition (system)EUR 2,175 partial priceCPU EUR 39 · RAM EUR 2,016 · PSU EUR 120

Electricity (2 years)–

TCO (2 years)EUR 2,727

Output tokens (2 years)62.44M

☁️ 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 (35 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-InstructKeine GPU (CPU-only)	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)	–	–	–	–

Keine GPU (CPU-only) · this run 1 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.