# LLM-Benchmark Konfiguration # Modell : glm-5.2-colibri # Engine : colibri # Run-ID : run-20260727-151333-b860f7 # GPU : NVIDIA RTX PRO 6000 Blackwell Workstation Edition # CPU : AMD Ryzen 9 9950X 16-Core Processor # RAM : 92 GB bench@llm-benchmark:~$ python3 /usr/local/bin/coli serve \ --model /opt/models/glm52-colibri-int4-g64 \ --host 10.220.4.11 \ --port 8000 \ --model-id glm-5.2-colibri \ --ram 70 \ --auto-tier \ --queue-timeout 600

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.	colibri
Modellalias?Der Name, unter dem das Modell ueber die API angesprochen wird. Genau dieser Wert muss im Request-Feld 'model' stehen.	glm-5.2-colibri
Modellpfad?Pfad zur GGUF-Modelldatei, die geladen und ausgeliefert wird.	/opt/models/glm52-colibri-int4-g64
model-id	glm-5.2-colibri
ram	70
auto-tier	aktiv
queue-timeout	600

All benchmarks of this model To leaderboard

Model comparison

glm-5.2-colibri 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 0,0 tok/s this run

CPUby processor

★ AMD Ryzen 9 9950X 16-Core Processor 0,0 tok/s this run

MBby mainboard

★ ASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI 0,0 tok/s this run

ENGby engine

unbekannt 0,0 tok/s★ colibri 0,0 tok/s this run

DRVby driver

unbekannt 0,0 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 70 W

⚡ TDP 644 W

☁️ External LLM (API) show

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

Electricity0.30 EUR/kWh

Avg power (incl. idle)644 W estimated (TDP)GPU 600 + CPU 29 + Board 15 W full load

Avg cost / hourEUR 0.19

Electricity / 1M tokensEUR 1,788.19

Token / kWh168

Acquisition (system)EUR 15,248 full priceGPU EUR 13,000 · CPU EUR 649 · Board EUR 499 · RAM EUR 920 · PSU EUR 180

Electricity (2 years)–

TCO (2 years)EUR 18,632

Output tokens (2 years)1.89M

☁️ 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 (70 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

	glm-5.2-colibriNVIDIA RTX PRO 6000 Blackwell 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 0 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.