# LLM-Benchmark Konfiguration # Modell : gpt-oss-20b # Engine : vLLM # Run-ID : run-20260804-052134-02d2cd # GPU : NVIDIA GeForce RTX 3090 Ti # CPU : AMD Ryzen 5 5600X 6-Core Processor # RAM : 30 GB bench@llm-benchmark:~$ /opt/vllm-gemma/venv/bin/python /opt/vllm-gemma/venv/bin/vllm serve openai/gpt-oss-20b \ --served-model-name gpt-oss-20b \ --host 192.168.41.116 \ --port 8000 \ --max-model-len 8192 \ --gpu-memory-utilization 0.95 \ --enforce-eager

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.	gpt-oss-20b
Kontextlaenge?Maximale Anzahl Tokens (Eingabe + erzeugte Ausgabe zusammen), die das Modell pro Anfrage verarbeiten kann.	8192
Alias?Anzeigename des Modells nach aussen (served model name), unabhaengig vom Dateinamen.	gpt-oss-20b
Kontext?Groesse des Kontextfensters in Token. 0 = der beim Training verwendete Kontext des Modells.	8192
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'.	0.95
Enforce-Eager?Schaltet die optimierte Graph-Ausfuehrung (CUDA-/HIP-Graphs) AB und rechnet Schritt fuer Schritt. Startet schneller und spart etwas VRAM, ist im laufenden Betrieb aber meist langsamer als mit Graphs.	aktiv

All benchmarks of this model To leaderboard

Model comparison

gpt-oss-20b 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 1.995,4 tok/sNVIDIA GeForce RTX 5090 1.890,7 tok/sNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 1.835,7 tok/sNVIDIA GeForce RTX 5070 Ti 1.210,3 tok/s★ NVIDIA GeForce RTX 3090 Ti 1.113,5 tok/s this runAMD Radeon AI PRO R9700 340,5 tok/sAMD Radeon 8060S Graphics 173,3 tok/sCPU-only 14,5 tok/s

CPUby processor

AMD Ryzen 9 9950X 16-Core Processor 1.995,4 tok/sAMD Ryzen 7 5800X3D 8-Core Processor 1.890,7 tok/sAMD Ryzen Threadripper PRO 9965WX 24-Cores 1.835,7 tok/sAMD Ryzen Threadripper PRO 5975WX 32-Cores 1.210,3 tok/sAMD Ryzen 9 8945HX with Radeon Graphics 1.113,5 tok/s★ AMD Ryzen 5 5600X 6-Core Processor 572,1 tok/s this runAMD Ryzen Threadripper PRO 7955WX 16-Cores 340,5 tok/sAMD RYZEN AI MAX+ 395 w/ Radeon 8060S 173,3 tok/sIntel(R) Xeon(R) CPU E5-4620 v2 @ 2.60GHz 14,5 tok/s

MBby mainboard

ASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI 1.995,4 tok/sASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING 1.890,7 tok/sASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE 1.835,7 tok/sASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 1.210,3 tok/sMeigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 1.113,5 tok/s★ ASUSTeK COMPUTER INC. PRIME A520M-K 572,1 tok/s this runBosgame AXB35-02 (BeyondMax Series) 173,3 tok/sDell Inc. PowerEdge R820 14,5 tok/s

ENGby engine

llama.cpp 1.995,4 tok/s★ vLLM 1.443,9 tok/s this run

DRVby driver

unbekannt 1.995,4 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 25 W

⚡ TDP 450 W

☁️ External LLM (API) show

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

Electricity0.30 EUR/kWh

Avg power (incl. idle)450 W estimated (TDP)GPU 450 W full load

Avg cost / hourEUR 0.14

Electricity / 1M tokensEUR 0.61

Token / kWh493.12K

Acquisition (system)EUR 1,149 partial priceGPU EUR 999 · PSU EUR 150

Electricity (2 years)–

TCO (2 years)EUR 3,514

Output tokens (2 years)3.89B

☁️ 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 (25 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

	gpt-oss-20bNVIDIA GeForce RTX 3090 Ti	gpt-oss-20bNVIDIA GeForce RTX 5090	gpt-oss-20bNVIDIA RTX PRO 6000 Blackwell Workstation Edition	gpt-oss-20bNVIDIA GeForce RTX 5090
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 Ti · this run 62 tok/s NVIDIA GeForce RTX 5090 405 tok/s NVIDIA RTX PRO 6000 Blackwell Workstation Edition 395 tok/s NVIDIA GeForce RTX 5090 389 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

Marcel Sommer

@marcelsommer