# LLM-Benchmark Konfiguration # Modell : Llama-3.1-Nemotron-Nano-4B-v1.1 # Engine : vLLM # Run-ID : run-20261108-194953-a60716 # GPU : NVIDIA GeForce RTX 3090 # CPU : AMD Ryzen 5 5600X 6-Core Processor # RAM : 31 GB bench@llm-benchmark:~$ vllm serve \ --model Llama-3.1-Nemotron-Nano-4B-v1.1

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
Kontextlaenge?Maximale Anzahl Tokens (Eingabe + erzeugte Ausgabe zusammen), die das Modell pro Anfrage verarbeiten kann.	4096
execution_type	local

All benchmarks of this model To leaderboard

Model comparison

Llama-3.1-Nemotron-Nano-4B-v1.1 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

AMD Radeon RX 7900 XTX 419,4 tok/s★ NVIDIA GeForce RTX 3090 86,3 tok/s this run

CPUby processor

AMD Ryzen Threadripper PRO 3955WX 16-Cores 419,4 tok/s★ AMD Ryzen 5 5600X 6-Core Processor 86,3 tok/s this run

MBby mainboard

ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 419,4 tok/s★ ASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING 86,3 tok/s this run

ENGby engine

llama.cpp 419,4 tok/s★ vLLM 86,3 tok/s this run

DRVby driver

unbekannt 419,4 tok/s

💰 Economics

Economics of this run

Operating cost, TCO and comparison with the next-best runs of the same model at identical concurrency (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 12 W

⚡ TDP 362 W

☁️ External LLM (API) show

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

Electricity0.30 EUR/kWh

Avg power (incl. idle)362 W estimated (TDP)GPU 350 + Board 12 W full load

Avg cost / hourEUR 0.11

Electricity / 1M tokensEUR 0.35

Token / kWh857.73K

Acquisition (system)EUR 1,518 full priceGPU EUR 850 · CPU EUR 121 · Board EUR 149 · RAM EUR 248 · PSU EUR 150

Electricity (2 years)–

TCO (2 years)EUR 3,421

Output tokens (2 years)5.44B

☁️ 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 (12 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

	Llama-3.1-Nemotron-Nano-4B-v1.1NVIDIA GeForce RTX 3090	Llama-3.1-Nemotron-Nano-4B-v1.1AMD Radeon RX 7900 XTX	Llama-3.1-Nemotron-Nano-4B-v1.1AMD Radeon RX 7900 XTX
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 GeForce RTX 3090 · this run 86 tok/s AMD Radeon RX 7900 XTX 139 tok/s AMD Radeon RX 7900 XTX 110 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.