# LLM-Benchmark Konfiguration # Modell : gemma-4-E2B-it # Engine : llama.cpp # Run-ID : run-20260820-202113-931ee2 # GPU : 2x NVIDIA GeForce RTX 2060 # CPU : Intel(R) Core(TM) i5-7400 CPU @ 3.00GHz # RAM : 7 GB bench@llm-benchmark:~$ /home/godcore/llama.cpp/build/bin/llama-server \ -m gemma-4-E2B-it-Q8_0.gguf \ -ngl 999 \ -ts 1,1 \ -fa on \ -c 4800 \ -np 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.	llamacpp
Kontextlaenge?Maximale Anzahl Tokens (Eingabe + erzeugte Ausgabe zusammen), die das Modell pro Anfrage verarbeiten kann.	4800
Modellpfad?Pfad zur GGUF-Modelldatei, die geladen und ausgeliefert wird.	gemma-4-E2B-it-Q8_0.gguf
GPU-Layer?Anzahl der auf die GPU ausgelagerten Modell-Layer. Hoeher = mehr VRAM und schneller; der Rest laeuft auf der CPU. 999 = alles auf GPU.	999
Tensor-Split?Verhaeltnis, in dem die Modell-Layer auf mehrere GPUs verteilt werden, z.B. 3,1.	1,1
fa	on
Kontext?Groesse des Kontextfensters in Token. 0 = der beim Training verwendete Kontext des Modells.	4800
np	1

All benchmarks of this model To leaderboard

Model comparison

gemma-4-E2B-it 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 2.491,2 tok/sNVIDIA RTX PRO 6000 Blackwell Workstation Edition 2.414,4 tok/sNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 2.143,6 tok/sNVIDIA GeForce RTX 5070 Ti 1.744,0 tok/sNVIDIA GeForce RTX 3090 Ti 1.492,8 tok/sNVIDIA GB10 (DGX Spark) 903,3 tok/sIntel Arc Pro B70 826,2 tok/s★ NVIDIA GeForce RTX 2060 15,3 tok/s this run

CPUby processor

AMD Ryzen 7 5800X3D 8-Core Processor 2.491,2 tok/sAMD Ryzen 9 9950X 16-Core Processor 2.414,4 tok/sAMD Ryzen Threadripper PRO 9965WX 24-Cores 2.143,6 tok/sAMD Ryzen Threadripper PRO 5975WX 32-Cores 1.744,0 tok/sAMD Ryzen 9 8945HX with Radeon Graphics 1.492,8 tok/sNVIDIA Grace 903,3 tok/sAMD Ryzen 9 7945HX with Radeon Graphics 826,2 tok/sAMD Ryzen 5 5600X 6-Core Processor 451,1 tok/s★ Intel(R) Core(TM) i5-7400 CPU @ 3.00GHz 15,3 tok/s this run

MBby mainboard

ASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING 2.491,2 tok/sASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI 2.414,4 tok/sASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE 2.143,6 tok/sASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 1.744,0 tok/sMeigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 1.492,8 tok/sASUSTeK COMPUTER INC. GX10 903,3 tok/sShenzhen Meigao Electronic Equipment Co.,Ltd DRFXI (MotherBoard Series) 826,2 tok/sASUSTeK COMPUTER INC. PRIME A520M-K 451,1 tok/s★ ASRock H110 Pro BTC+ 15,3 tok/s this run

ENGby engine

★ llama.cpp 2.491,2 tok/s this runvLLM 1.608,5 tok/s

DRVby driver

unbekannt 2.491,2 tok/sNVIDIA 590.48.01 / CUDA 13.1 903,3 tok/sIntel 26.18.38308.4 826,2 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 %

🔌 Idle 0 W

⚡ TDP 10 W

☁️ External LLM (API) show

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

Electricity0.30 EUR/kWh

Avg power (incl. idle)0 W missing

Avg cost / hour–

Electricity / 1M tokens–

Token / kWh–

Acquisition (system)– missing

Electricity (2 years)–

TCO (2 years)–

Output tokens (2 years)913.91M

☁️ External LLM (API) – comparison

External LLM cost (2 years)–

Savings vs. external (2 years)–

No power draw measured – values estimated from GPU TDP + CPU (idle + 15 %) + board.

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 (0 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

	gemma-4-E2B-it2x NVIDIA GeForce RTX 2060	gemma-4-E2B-itNVIDIA RTX PRO 6000 Blackwell Workstation Edition	gemma-4-E2B-itNVIDIA GeForce RTX 5090	gemma-4-E2B-it3x 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)	–	–	–	–

2× NVIDIA GeForce RTX 2060 · this run 14 tok/s NVIDIA RTX PRO 6000 Blackwell Workstation Edition 379 tok/s NVIDIA GeForce RTX 5090 378 tok/s 3× NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 344 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.