# LLM-Benchmark Konfiguration # Modell : Devstral-Small-2507 # Engine : llama.cpp # Run-ID : run-20260817-150144-ca46c5 # GPU : AMD Radeon PRO W7900 Dual Slot # CPU : AMD Ryzen Threadripper PRO 5975WX 32-Cores # RAM : 247 GB bench@llm-benchmark:~$ /home/godcore/llama.cpp/build_hip/bin/llama-server \ -m /home/godcore/.cache/huggingface/hub/models--unsloth--Devstral-Small-2507-GGUF/snapshots/8d0c7eb7bf0142cc3b94c88450eddcc7720e4488/Devstral-Small-2507-Q4_K_M.gguf \ --alias Devstral-Small-2507 \ -ngl 999 \ -fa on \ -c 49152 \ -np 12 \ --jinja \ --host 0.0.0.0 \ --port 8000

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.	Devstral-Small-2507
Kontextlaenge?Maximale Anzahl Tokens (Eingabe + erzeugte Ausgabe zusammen), die das Modell pro Anfrage verarbeiten kann.	49152
Modellpfad?Pfad zur GGUF-Modelldatei, die geladen und ausgeliefert wird.	/home/godcore/.cache/huggingface/hub/models--unsloth--Devstral-Small-2507-GGUF/snapshots/8d0c7eb7bf0142cc3b94c88450eddcc7720e4488/Devstral-Small-2507-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.	49152
np	12

All benchmarks of this model To leaderboard

Model comparison

Devstral-Small-2507 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 647,6 tok/sNVIDIA GeForce RTX 5090 625,1 tok/sNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 572,2 tok/sNVIDIA GeForce RTX 3090 Ti 317,4 tok/sAMD Radeon AI PRO R9700 205,8 tok/s★ AMD Radeon PRO W7900 Dual Slot 137,5 tok/s this runNVIDIA GeForce RTX 5070 Ti 103,9 tok/sNVIDIA Tesla P100 PCIe 16GB 23,2 tok/s

CPUby processor

AMD Ryzen 9 9950X 16-Core Processor 647,6 tok/sAMD Ryzen 7 5800X3D 8-Core Processor 625,1 tok/sAMD Ryzen Threadripper PRO 9965WX 24-Cores 572,2 tok/sAMD Ryzen 9 8945HX with Radeon Graphics 317,4 tok/sAMD Ryzen Threadripper PRO 7955WX 16-Cores 205,8 tok/s★ AMD Ryzen Threadripper PRO 5975WX 32-Cores 137,5 tok/s this runAMD Ryzen 5 5600X 6-Core Processor 57,1 tok/sAMD Ryzen 9 7945HX with Radeon Graphics 23,2 tok/s

MBby mainboard

ASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI 647,6 tok/sASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING 625,1 tok/sASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE 572,2 tok/sMeigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 317,4 tok/s★ ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 137,5 tok/s this runASUSTeK COMPUTER INC. PRIME A520M-K 57,1 tok/sShenzhen Meigao Electronic Equipment Co.,Ltd F1FXM (DeskMini Series) 23,2 tok/s

ENGby engine

★ llama.cpp 647,6 tok/s this rununbekannt 57,1 tok/s

DRVby driver

unbekannt 647,6 tok/sAMD 7.0.0-27-generic 31,4 tok/s

💰 Economics

Economics of this run

Operating cost, TCO and comparison with the next-best runs of the same model at identical concurrency (10× 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 10 W

⚡ TDP 305 W

☁️ External LLM (API) show

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

Electricity0.30 EUR/kWh

Avg power (incl. idle)305 W estimated (TDP)GPU 295 + Board 10 W full load

Avg cost / hourEUR 0.092

Electricity / 1M tokensEUR 0.19

Token / kWh1.54M

Acquisition (system)EUR 2,126 missingRAM EUR 1,976 · PSU EUR 150

Electricity (2 years)–

TCO (2 years)EUR 3,729

Output tokens (2 years)8.24B

☁️ 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 (10 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-2507AMD Radeon PRO W7900 Dual Slot	Devstral-Small-2507NVIDIA RTX PRO 6000 Blackwell Workstation Edition	Devstral-Small-2507NVIDIA GeForce RTX 5090	Devstral-Small-25073x 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)	–	–	–	–

AMD Radeon PRO W7900 Dual Slot · this run 131 tok/s NVIDIA RTX PRO 6000 Blackwell Workstation Edition 648 tok/s NVIDIA GeForce RTX 5090 625 tok/s 3× NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 572 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.