# LLM-Benchmark Konfiguration # Modell : NVIDIA-Nemotron-3.5-Super-VL-120B-A12B # Engine : vLLM # Run-ID : run-20261016-101741-a6fae7 # GPU : NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition # CPU : AMD Ryzen 9 7945HX with Radeon Graphics # RAM : 60 GB bench@llm-benchmark:~$ vllm serve Nemotron-3.5-Super-VL NVFP4 \ --max-num-seqs 16 '(RTX' PRO 6000 Blackwell 96GB, kein 'Offload)'

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
Max-Sequenzen?Maximale Anzahl gleichzeitig bearbeiteter Anfragen (Batch-Groesse). Hoeher = mehr Durchsatz, braucht aber mehr KV-Cache-Speicher.	16
execution_type	local

All benchmarks of this model To leaderboard

Model comparison

NVIDIA-Nemotron-3.5-Super-VL-120B-A12B 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 Max-Q Workstation Edition 1.562,3 tok/s this runNVIDIA GB10 (DGX Spark) 79,8 tok/s

CPUby processor

★ AMD Ryzen 9 7945HX with Radeon Graphics 1.562,3 tok/s this runNVIDIA Grace 79,8 tok/s

MBby mainboard

★ Shenzhen Meigao Electronic Equipment Co.,Ltd DRFXI (MotherBoard Series) 1.562,3 tok/s this runASUSTeK COMPUTER INC. GX10 79,8 tok/s

ENGby engine

★ vLLM 1.562,3 tok/s this run

DRVby driver

unbekannt 1.562,3 tok/sNVIDIA 590.48.01 / CUDA 13.1 79,8 tok/s

💰 Economics

Economics of this run

Operating cost, TCO and comparison with the next-best runs of the same model at identical concurrency (5× 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 0.031

Token / kWh9.54M

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)50.14B

☁️ 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

	NVIDIA-Nemotron-3.5-Super-VL-120B-A12BNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition	NVIDIA-Nemotron-3.5-Super-VL-120B-A12BNVIDIA GB10 (DGX Spark)
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 795 tok/s NVIDIA GB10 (DGX Spark) 54 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.