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Public note: Rechnerische Variante: Messwerte der NVIDIA RTX PRO 6000 Blackwell Workstation Edition (gemessen auf godcore-ai-002) uebertragen auf die Host-Konfiguration von godcore-worker-004 (AMD Ryzen 5 5600X, 32 GB RAM). Keine eigene Messung dieser Kombination. Quelle: run-20260729-032123-88fa84 (2026-07-29 00:05:24).

Generation1.623,15tok/s

Prefill10.706,95tok/s

Time to First Token7.140,00ms

Total duration40,82s

Concurrency10parallel

Ranking in the field

This GPU All GPUs

28of 112 systems

Performance benchmark · Primary metric: Generation-Speed (tok/s) · 10× concurrent

This run is better than 76 % of all comparable systems.

Generation 1.623,2 tok/s

+53 % vs Ø 1.060,0

Prefill 10.707,0 tok/s

+0 % vs Ø 10.705,3

Time to First Token 7.140 ms

-73 % vs Ø 26.119

Distribution in the field0 – 2.414 tok/s

Ø 1.060 Median Dieser Lauf

Wie schlägt sich dieser Benchmark mit anderen Modellen?

gemma-4-E2B-itNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20260727-181606-b37ed9

2.414,4 tok/s

gemma-4-E2B-itNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20261023-065752-48370d

2.412,3 tok/s

Nemotron-3-Nano-4BNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20260727-181607-4cca42

2.182,7 tok/s

Nemotron-3-Nano-4BNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20261023-065758-96fe6a

2.144,3 tok/s

gpt-oss-20bNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20260730-035052-9c966e

1.995,4 tok/s

gpt-oss-20bNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20260728-140954-8007c4

1.993,5 tok/s

gpt-oss-20bNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20261023-065808-332d1f

1.981,1 tok/s

Nemotron-3-Nano-Omni-30B-A3B-ReasoningNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20260729-032119-311459

1.791,5 tok/s

Nemotron-Cascade-2-30B-A3BNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20261023-065828-5ea30a

1.784,7 tok/s

Nemotron-3-Nano-30B-A3BNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20261023-065821-3781a7

1.775,6 tok/s

Nemotron-3-Nano-Omni-30B-A3B-ReasoningNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20261023-065814-4a1dea

1.774,4 tok/s

Nemotron-3-Nano-30B-A3BNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20260729-032121-e2403f

1.773,5 tok/s

Nemotron-Cascade-2-30B-A3BNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20260729-032120-1b1a49

1.768,1 tok/s

gemma-4-E4B-itNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20260727-181607-866815

1.758,6 tok/s

Qwen3-30B-A3B-Thinking-2507 this runNVIDIA RTX PRO 6000 Blackwell Workstation Edition · run-20261023-065850-7d5629

1.623,2 tok/s

How does this benchmark compare on other GPUs?

Same model on different hardware · 10× concurrent · Generation (tok/s)

NVIDIA GeForce RTX 5090run-20260729-105335-e6f72a

1.683,8 tok/s

NVIDIA RTX PRO 6000 Blackwell Workstation Editionrun-20260729-032123-88fa84

1.670,5 tok/s

NVIDIA RTX PRO 6000 Blackwell Workstation Edition this runrun-20261023-065850-7d5629

1.623,2 tok/s

3× NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Editionrun-20260729-105342-9f1be6

1.521,4 tok/s

3× NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Editionrun-20260729-105342-a89e7f

1.518,4 tok/s

3× NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Editionrun-20260729-105342-bd5e1f

1.465,3 tok/s

NVIDIA GeForce RTX 3090 Tirun-20260729-060803-f260ab

930,6 tok/s

NVIDIA RTX PRO 6000 Blackwell Workstation Editionrun-20261023-071954-1cd2ee

913,3 tok/s

NVIDIA RTX PRO 6000 Blackwell Workstation Editionrun-20260724-004605-f9a3a1

906,0 tok/s

NVIDIA GeForce RTX 3090 Tirun-20260724-004603-ee41df

883,7 tok/s

NVIDIA GeForce RTX 5090run-20260723-045450-14f178

873,7 tok/s

2× NVIDIA RTX A6000run-20260829-124021-f57a00

751,7 tok/s

2× NVIDIA RTX A6000run-20260827-090902-07d874

673,2 tok/s

2× NVIDIA RTX A6000run-20260829-181042-e10c46

554,2 tok/s

Hardware

GPU: NVIDIA RTX PRO 6000 Blackwell Workstation Edition · 96 GB VRAM
CPU: AMD Ryzen 5 5600X 6-Core Processor
RAM: 31 GB
Mainboard: ASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING

Setup

Runtime: llama.cpp
Quantization: Q4_K_M
Model: Qwen3-30B-A3B-Thinking-2507

Configuration

benchmark-konfiguration — run-20261023-065850-7d5629

# LLM-Benchmark Konfiguration # Modell : Qwen3-30B-A3B-Thinking-2507 # Engine : llama.cpp # Run-ID : run-20261023-065850-7d5629 # GPU : NVIDIA RTX PRO 6000 Blackwell Workstation Edition # CPU : AMD Ryzen 5 5600X 6-Core Processor # RAM : 31 GB bench@llm-benchmark:~$ llama-server \ -m /home/godcore/.cache/huggingface/hub/models--unsloth--Qwen3-30B-A3B-Thinking-2507-GGUF/snapshots/a9b37aaac12b2bd0098783a443429543dd76a14d/Qwen3-30B-A3B-Thinking-2507-Q4_K_M.gguf \ -c 16384 \ -np 12

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.	Qwen3-30B-A3B-Thinking-2507
Kontextlaenge?Maximale Anzahl Tokens (Eingabe + erzeugte Ausgabe zusammen), die das Modell pro Anfrage verarbeiten kann.	16384
Modellpfad?Pfad zur GGUF-Modelldatei, die geladen und ausgeliefert wird.	/home/godcore/.cache/huggingface/hub/models--unsloth--Qwen3-30B-A3B-Thinking-2507-GGUF/snapshots/a9b37aaac12b2bd0098783a443429543dd76a14d/Qwen3-30B-A3B-Thinking-2507-Q4_K_M.gguf
Kontext?Groesse des Kontextfensters in Token. 0 = der beim Training verwendete Kontext des Modells.	16384
np	12
execution_type	local

All benchmarks of this model To leaderboard

Model comparison

Qwen3-30B-A3B-Thinking-2507 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 1.683,8 tok/s★ NVIDIA RTX PRO 6000 Blackwell Workstation Edition 1.670,5 tok/s this runNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 1.521,4 tok/sNVIDIA GeForce RTX 3090 Ti 930,6 tok/sNVIDIA RTX A6000 751,7 tok/sAMD Radeon RX 7900 XTX 303,1 tok/sAMD Radeon PRO W7800 48GB 258,3 tok/sAMD Radeon PRO W7900 Dual Slot 254,6 tok/sAMD Radeon AI PRO R9700 238,8 tok/sNVIDIA GeForce RTX 5070 Ti 155,8 tok/sCPU-only 8,5 tok/s

CPUby processor

AMD Ryzen 7 5800X3D 8-Core Processor 1.683,8 tok/sAMD Ryzen 9 9950X 16-Core Processor 1.670,5 tok/s★ AMD Ryzen 5 5600X 6-Core Processor 1.623,2 tok/s this runAMD Ryzen Threadripper PRO 9965WX 24-Cores 1.521,4 tok/sAMD Ryzen 9 8945HX with Radeon Graphics 930,6 tok/sAMD Ryzen Threadripper PRO 7955WX 16-Cores 751,7 tok/sAMD Ryzen Threadripper PRO 3955WX 16-Cores 303,1 tok/sAMD Ryzen Threadripper PRO 5975WX 32-Cores 258,3 tok/sAMD Ryzen 3 3100 4-Core Processor 238,8 tok/sIntel(R) Xeon(R) CPU E5-4657L v2 @ 2.40GHz 223,3 tok/sIntel(R) Xeon(R) CPU E5-4620 v2 @ 2.60GHz 8,5 tok/s

MBby mainboard

★ ASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING 1.683,8 tok/s this runASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI 1.670,5 tok/sASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE 1.521,4 tok/sMeigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 930,6 tok/sASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 303,1 tok/sASUSTeK COMPUTER INC. ROG STRIX X570-F GAMING 238,8 tok/sDell Inc. PowerEdge R820 223,3 tok/s

ENGby engine

★ llama.cpp 1.683,8 tok/s this runvLLM 913,3 tok/sunbekannt 91,4 tok/s

DRVby driver

unbekannt 1.683,8 tok/sAMD 7.0.0-27-generic 91,4 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 42 W

⚡ TDP 612 W

☁️ External LLM (API) show

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

Electricity0.30 EUR/kWh

Avg power (incl. idle)612 W estimated (TDP)GPU 600 + Board 12 W full load

Avg cost / hourEUR 0.18

Electricity / 1M tokensEUR 0.031

Token / kWh9.55M

Acquisition (system)EUR 13,698 full priceGPU EUR 13,000 · CPU EUR 121 · Board EUR 149 · RAM EUR 248 · PSU EUR 180

Electricity (2 years)–

TCO (2 years)EUR 16,915

Output tokens (2 years)102.38B

☁️ 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 (42 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

	Qwen3-30B-A3B-Thinking-2507NVIDIA RTX PRO 6000 Blackwell Workstation Edition	Qwen3-30B-A3B-Thinking-2507NVIDIA GeForce RTX 5090	Qwen3-30B-A3B-Thinking-2507NVIDIA RTX PRO 6000 Blackwell Workstation Edition	Qwen3-30B-A3B-Thinking-25073x 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)	–	–	–	–

NVIDIA RTX PRO 6000 Blackwell Workstation Edition · this run 1,623 tok/s NVIDIA GeForce RTX 5090 1,684 tok/s NVIDIA RTX PRO 6000 Blackwell Workstation Edition 1,670 tok/s 3× NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 1,521 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.