# LLM-Benchmark Konfiguration # Modell : Seed-OSS-36B-Instruct # Engine : llama.cpp # Run-ID : run-20260729-032129-c4841f # GPU : NVIDIA RTX PRO 6000 Blackwell Workstation Edition # CPU : AMD Ryzen 9 9950X 16-Core Processor # RAM : 92 GB bench@llm-benchmark:~$ /home/godcore/llama.cpp/build/bin/llama-server \ -m /home/godcore/.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 999 \ -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.	/home/godcore/.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.	999
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/s★ NVIDIA RTX PRO 6000 Blackwell Workstation Edition 460,0 tok/s this runNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 383,9 tok/sNVIDIA RTX A6000 349,2 tok/sNVIDIA GeForce RTX 3090 Ti 111,6 tok/sAMD 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/s★ AMD Ryzen 9 9950X 16-Core Processor 460,0 tok/s this runAMD Ryzen Threadripper PRO 9965WX 24-Cores 383,9 tok/sAMD Ryzen Threadripper PRO 7955WX 16-Cores 349,2 tok/sAMD Ryzen 9 8945HX with Radeon Graphics 111,6 tok/sAMD 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/s★ ASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI 460,0 tok/s this runASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE 383,9 tok/sMeigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 111,6 tok/sASUSTeK 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 (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 70 W

⚡ TDP 644 W

☁️ External LLM (API) show

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

Electricity0.30 EUR/kWh

Avg power (incl. idle)644 W estimated (TDP)GPU 600 + CPU 29 + Board 15 W full load

Avg cost / hourEUR 0.19

Electricity / 1M tokensEUR 0.86

Token / kWh347.05K

Acquisition (system)EUR 15,248 full priceGPU EUR 13,000 · CPU EUR 649 · Board EUR 499 · RAM EUR 920 · PSU EUR 180

Electricity (2 years)–

TCO (2 years)EUR 18,632

Output tokens (2 years)3.91B

☁️ 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 (70 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 RTX PRO 6000 Blackwell Workstation Edition	Seed-OSS-36B-InstructNVIDIA GeForce RTX 5090	Seed-OSS-36B-Instruct3x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition	Seed-OSS-36B-Instruct3x 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)	–	–	–	–

NVIDIA RTX PRO 6000 Blackwell Workstation Edition · this run 62 tok/s NVIDIA GeForce RTX 5090 61 tok/s 3× NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 58 tok/s 3× NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 57 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.