# LLM-Benchmark Konfiguration # Modell : Devstral-Small-2-24B-Instruct-2512 # Engine : llama.cpp # Run-ID : run-20260821-162702-cd2d93 # GPU : NVIDIA Tesla P100 PCIe 16GB # CPU : AMD Ryzen 9 7945HX with Radeon Graphics # RAM : 60 GB bench@llm-benchmark:~$ /home/godcore/llama.cpp/build/bin/llama-server \ -m Devstral-Small-2-24B-Instruct-2512-Q4_K_M.gguf \ -ngl 999 \ -fa on \ -c 24000 \ -np 5

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.	24000
Modellpfad?Pfad zur GGUF-Modelldatei, die geladen und ausgeliefert wird.	Devstral-Small-2-24B-Instruct-2512-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
fa	on
Kontext?Groesse des Kontextfensters in Token. 0 = der beim Training verwendete Kontext des Modells.	24000
np	5

All benchmarks of this model To leaderboard

Model comparison

Devstral-Small-2-24B-Instruct-2512 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 725,6 tok/sNVIDIA GeForce RTX 5090 620,3 tok/sNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 569,7 tok/sNVIDIA GeForce RTX 3090 Ti 331,6 tok/sAMD Radeon AI PRO R9700 206,3 tok/sAMD Radeon PRO W7900 Dual Slot 152,1 tok/sNVIDIA GeForce RTX 5070 Ti 108,5 tok/s★ NVIDIA Tesla P100 PCIe 16GB 23,3 tok/s this runAMD Radeon 8060S Graphics 8,9 tok/s

CPUby processor

AMD Ryzen 9 9950X 16-Core Processor 725,6 tok/sAMD Ryzen 7 5800X3D 8-Core Processor 620,3 tok/sAMD Ryzen Threadripper PRO 9965WX 24-Cores 569,7 tok/sAMD Ryzen 9 8945HX with Radeon Graphics 331,6 tok/sAMD Ryzen 5 5600X 6-Core Processor 247,7 tok/sAMD Ryzen Threadripper PRO 7955WX 16-Cores 206,3 tok/sAMD Ryzen Threadripper PRO 5975WX 32-Cores 152,1 tok/s★ AMD Ryzen 9 7945HX with Radeon Graphics 23,3 tok/s this runAMD RYZEN AI MAX+ 395 w/ Radeon 8060S 8,9 tok/s

MBby mainboard

ASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI 725,6 tok/sASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING 620,3 tok/sASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE 569,7 tok/sMeigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 331,6 tok/sASUSTeK COMPUTER INC. PRIME A520M-K 247,7 tok/sASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 152,1 tok/s★ Shenzhen Meigao Electronic Equipment Co.,Ltd F1FXM (DeskMini Series) 23,3 tok/s this runMeigao Innovation Technology (Shen Zhen) Co., Ltd SHWSA (MS-S1 MAX) 8,9 tok/sBosgame AXB35-02 (BeyondMax Series) 8,9 tok/s

ENGby engine

vLLM 725,6 tok/s★ llama.cpp 645,1 tok/s this rununbekannt 247,7 tok/s

DRVby driver

unbekannt 725,6 tok/sAMD 7.0.0-27-generic 29,4 tok/sROCm 7.2.0 8,9 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 0 W

⚡ TDP 250 W

☁️ External LLM (API) show

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

Electricity0.30 EUR/kWh

Avg power (incl. idle)250 W estimated (TDP)GPU 250 W full load

Avg cost / hourEUR 0.075

Electricity / 1M tokensEUR 0.89

Token / kWh335.38K

Acquisition (system)EUR 120 missingPSU EUR 120

Electricity (2 years)–

TCO (2 years)EUR 1,434

Output tokens (2 years)1.47B

☁️ 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 (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

	Devstral-Small-2-24B-Instruct-2512NVIDIA Tesla P100 PCIe 16GB	Devstral-Small-2-24B-Instruct-2512NVIDIA RTX PRO 6000 Blackwell Workstation Edition	Devstral-Small-2-24B-Instruct-2512NVIDIA RTX PRO 6000 Blackwell Workstation Edition	Devstral-Small-2-24B-Instruct-2512NVIDIA 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 Tesla P100 PCIe 16GB · this run 23 tok/s NVIDIA RTX PRO 6000 Blackwell Workstation Edition 423 tok/s NVIDIA RTX PRO 6000 Blackwell Workstation Edition 358 tok/s NVIDIA GeForce RTX 5090 340 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.