# LLM-Benchmark Konfiguration # Modell : Seed-OSS-36B-Instruct # Engine : llama.cpp # Run-ID : run-20260729-105338-5865d1 # GPU : NVIDIA GeForce RTX 3090 Ti # CPU : AMD Ryzen 9 8945HX with Radeon Graphics # RAM : 92 GB bench@llm-benchmark:~$ /root/llama.cpp/build/bin/llama-server \ -m /root/.cache/huggingface/hub/models--yarikdevcom--Seed-OSS-36B-Instruct-GGUF/snapshots/cced0086e699de401821cce4b6fa69d3c6e87011/Seed_OSS_36B_Instruct_Q4_K_M.gguf \ --alias Seed-OSS-36B-Instruct \ --host 0.0.0.0 \ --port 8000 \ -ngl 60 \ -c 16384 \ -np 4 \ --jinja

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
Modellalias?Der Name, unter dem das Modell ueber die API angesprochen wird. Genau dieser Wert muss im Request-Feld 'model' stehen.	Seed-OSS-36B-Instruct
Kontextlaenge?Maximale Anzahl Tokens (Eingabe + erzeugte Ausgabe zusammen), die das Modell pro Anfrage verarbeiten kann.	16384
Modellpfad?Pfad zur GGUF-Modelldatei, die geladen und ausgeliefert wird.	/root/.cache/huggingface/hub/models--yarikdevcom--Seed-OSS-36B-Instruct-GGUF/snapshots/cced0086e699de401821cce4b6fa69d3c6e87011/Seed_OSS_36B_Instruct_Q4_K_M.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.	60
Kontext?Groesse des Kontextfensters in Token. 0 = der beim Training verwendete Kontext des Modells.	16384
np	4

All benchmarks of this model To leaderboard

Model comparison

Seed-OSS-36B-Instruct 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 839,7 tok/sNVIDIA RTX PRO 6000 Blackwell Workstation Edition 460,0 tok/sNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 383,9 tok/sNVIDIA RTX A6000 349,2 tok/s★ NVIDIA GeForce RTX 3090 Ti 111,6 tok/s this runAMD Radeon AI PRO R9700 107,3 tok/sAMD Radeon PRO W7900 Dual Slot 88,6 tok/sAMD Radeon PRO W7800 48GB 57,7 tok/sNVIDIA GeForce RTX 5070 Ti 39,2 tok/sNVIDIA GeForce RTX 2060 1,0 tok/s

CPUby processor

AMD Ryzen 7 5800X3D 8-Core Processor 839,7 tok/sAMD Ryzen 9 9950X 16-Core Processor 460,0 tok/sAMD Ryzen Threadripper PRO 9965WX 24-Cores 383,9 tok/sAMD Ryzen Threadripper PRO 7955WX 16-Cores 349,2 tok/s★ AMD Ryzen 9 8945HX with Radeon Graphics 111,6 tok/s this runAMD Ryzen Threadripper PRO 5975WX 32-Cores 88,6 tok/sIntel(R) Xeon(R) CPU E5-4620 v2 @ 2.60GHz 1,0 tok/s

MBby mainboard

ASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING 839,7 tok/sASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI 460,0 tok/sASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE 383,9 tok/s★ Meigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 111,6 tok/s this runASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 88,6 tok/sDell Inc. PowerEdge R820 1,0 tok/s

ENGby engine

★ llama.cpp 839,7 tok/s this runvLLM 349,2 tok/s

DRVby driver

unbekannt 839,7 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 50 W

⚡ TDP 477 W

☁️ External LLM (API) show

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

Electricity0.30 EUR/kWh

Avg power (incl. idle)477 W estimated (TDP)GPU 450 + CPU 17 + Board 10 W full load

Avg cost / hourEUR 0.14

Electricity / 1M tokensEUR 0.68

Token / kWh443.24K

Acquisition (system)EUR 2,986 partial priceGPU EUR 999 · CPU EUR 549 · RAM EUR 1,288 · PSU EUR 150

Electricity (2 years)–

TCO (2 years)EUR 5,494

Output tokens (2 years)3.71B

☁️ 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 (50 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

	Seed-OSS-36B-InstructNVIDIA GeForce RTX 3090 Ti	Seed-OSS-36B-InstructNVIDIA RTX PRO 6000 Blackwell Workstation Edition	Seed-OSS-36B-InstructNVIDIA GeForce RTX 5090	Seed-OSS-36B-Instruct2x NVIDIA RTX A6000
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 59 tok/s NVIDIA RTX PRO 6000 Blackwell Workstation Edition 243 tok/s NVIDIA GeForce RTX 5090 237 tok/s 2× NVIDIA RTX A6000 219 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.