# LLM-Benchmark Konfiguration # Modell : Ornith-1.0-9B # Engine : vLLM # Run-ID : run-20260923-143546-f692ed # GPU : NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition # CPU : AMD Ryzen 9 7945HX with Radeon Graphics # RAM : 60 GB bench@llm-benchmark:~$ docker vllm/vllm-openai serve Ornith-1.0-9B-FP8 \ --served-model-name Ornith-1.0-9B \ --gpu-memory-utilization 0.90 \ --max-model-len 16384 \ --max-num-seqs 12 '(NVIDIA' RTX PRO 6000 Blackwell '96GB)'

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
Modellalias?Der Name, unter dem das Modell ueber die API angesprochen wird. Genau dieser Wert muss im Request-Feld 'model' stehen.	Ornith-1.0-9B
Kontextlaenge?Maximale Anzahl Tokens (Eingabe + erzeugte Ausgabe zusammen), die das Modell pro Anfrage verarbeiten kann.	16384
Alias?Anzeigename des Modells nach aussen (served model name), unabhaengig vom Dateinamen.	Ornith-1.0-9B
GPU-Speicher?Anteil des GPU-Speichers (0 bis 1), den vLLM belegen darf. 0.92 = 92 %. Hoeher = mehr Platz fuer den KV-Cache (mehr/laengere parallele Anfragen), aber groesseres Risiko fuer 'Out of Memory'.	0.90
Kontext?Groesse des Kontextfensters in Token. 0 = der beim Training verwendete Kontext des Modells.	16384
Max-Sequenzen?Maximale Anzahl gleichzeitig bearbeiteter Anfragen (Batch-Groesse). Hoeher = mehr Durchsatz, braucht aber mehr KV-Cache-Speicher.	12

All benchmarks of this model To leaderboard

Model comparison

Ornith-1.0-9B 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 1.560,5 tok/sNVIDIA GeForce RTX 5090 1.505,6 tok/s★ NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 1.349,7 tok/s this runNVIDIA RTX A6000 1.062,5 tok/sNVIDIA GeForce RTX 5070 Ti 883,4 tok/sNVIDIA GeForce RTX 3090 Ti 786,2 tok/sAMD Radeon AI PRO R9700 487,6 tok/sIntel Arc Pro B70 351,6 tok/sAMD Radeon PRO W7800 48GB 294,0 tok/sNVIDIA Tesla V100 PCIe 32GB 248,7 tok/sAMD Radeon RX 7900 XTX 242,4 tok/sAMD Radeon PRO W7900 Dual Slot 235,1 tok/sNVIDIA GeForce RTX 2060 108,0 tok/sNVIDIA Tesla P100 PCIe 16GB 64,5 tok/s

CPUby processor

AMD Ryzen 9 9950X 16-Core Processor 1.560,5 tok/sAMD Ryzen 7 5800X3D 8-Core Processor 1.505,6 tok/sAMD Ryzen Threadripper PRO 9965WX 24-Cores 1.349,7 tok/s★ AMD Ryzen 9 7945HX with Radeon Graphics 1.100,7 tok/s this runAMD Ryzen Threadripper PRO 7955WX 16-Cores 1.062,5 tok/sAMD Ryzen Threadripper PRO 5975WX 32-Cores 883,4 tok/sAMD Ryzen 9 8945HX with Radeon Graphics 786,2 tok/sAMD Ryzen 3 PRO 3200GE w/ Radeon Vega Graphics 248,7 tok/sAMD Ryzen Threadripper PRO 3955WX 16-Cores 242,4 tok/sIntel(R) Core(TM) i5-7400 CPU @ 3.00GHz 108,0 tok/s

MBby mainboard

ASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI 1.560,5 tok/sASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING 1.505,6 tok/sASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE 1.349,7 tok/s★ Shenzhen Meigao Electronic Equipment Co.,Ltd DRFXI (MotherBoard Series) 1.100,7 tok/s this runASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 883,4 tok/sMeigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 786,2 tok/sASUSTeK COMPUTER INC. ROG STRIX X570-F GAMING 248,7 tok/sASRock H110 Pro BTC+ 108,0 tok/sShenzhen Meigao Electronic Equipment Co.,Ltd F1FXM (DeskMini Series) 64,5 tok/s

ENGby engine

llama.cpp 1.560,5 tok/s★ vLLM 1.100,7 tok/s this run

DRVby driver

unbekannt 1.560,5 tok/sIntel 26.18.38308.4 351,6 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 30 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 0.19

Token / kWh1.57M

Acquisition (system)EUR 13,120 partial priceGPU EUR 13,000 · PSU EUR 120

Electricity (2 years)–

TCO (2 years)EUR 14,697

Output tokens (2 years)8.25B

☁️ 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 (30 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

	Ornith-1.0-9BNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition	Ornith-1.0-9BNVIDIA GeForce RTX 5090	Ornith-1.0-9BNVIDIA RTX PRO 6000 Blackwell Workstation Edition	Ornith-1.0-9B3x 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 Max-Q Workstation Edition · this run 131 tok/s NVIDIA GeForce RTX 5090 221 tok/s NVIDIA RTX PRO 6000 Blackwell Workstation Edition 217 tok/s 3× NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 205 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.