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Contributed byMario AlkaMistral AI

Devstral-2-123B-Instruct-2512

Performance benchmark · measured on 09.10.2026 02:43

Benchmark-IDrun-20261009-090853-c1975a
Timebench 3 - Kombi (Prefill + Generation)Dense123BRuntime: llama.cppQuantisierung: Q4_K_M
ℹ
Public note: Dell PowerEdge R820 . 2x AMD Radeon RX 7900 XTX (48 GB VRAM gesamt, ROCm/HIP) . RAM: 32x 16 GB SK Hynix DDR3-1600 ECC RDIMM (HMT42GR7AFR4A-PB) = 512 GB @ 1600 MT/s
Generation0,78tok/s
Prefill70,01tok/s
Time to First Token30.322,00ms
Total duration1.200,00s
Concurrency1parallel
Ranking in the field
1758of 1808 systems

Performance benchmark · Primary metric: Generation-Speed (tok/s) · 1× concurrent

This run is better than 3 % of all comparable systems.
Generation 0,8 tok/s
-99 % vs Ø 77,1
Prefill 70,0 tok/s
-98 % vs Ø 2.836,4
Time to First Token 30.322 ms
+21 % vs Ø 25.162
Distribution in the field0 – 405 tok/s
Ø 77 Median Dieser Lauf

Wie schlägt sich dieser Benchmark mit anderen Modellen?

How does this benchmark compare on other GPUs?

Same model on different hardware · 1× concurrent · Generation (tok/s)

Hardware

GPU: 2x AMD Radeon RX 7900 XTX · 24 GB VRAM
CPU: 4x Intel(R) Xeon(R) CPU E5-4657L v2 @ 2.40GHz
RAM: 504 GB
Mainboard: Dell Inc. PowerEdge R820

Setup

Runtime: llama.cpp
Quantization: Q4_K_M
Model: Devstral-2-123B-Instruct-2512

Configuration

benchmark-konfiguration — run-20261009-090853-c1975a
# LLM-Benchmark Konfiguration # Modell : Devstral-2-123B-Instruct-2512 # Engine : llama.cpp # Run-ID : run-20261009-090853-c1975a # GPU : 2x AMD Radeon RX 7900 XTX # CPU : 4x Intel(R) Xeon(R) CPU E5-4657L v2 @ 2.40GHz # RAM : 504 GB bench@llm-benchmark:~$ llama-server \ -m mistralai_Devstral-2-123B-Instruct-2512-IQ4_XS/mistralai_Devstral-2-123B-Instruct-2512-IQ4_XS-00001-of-00002.gguf \ --alias Devstral-2-123B-Instruct-2512 \ -ngl 40 \ -sm layer \ -fa on \ -c 40960 \ -np 10 '(2x' RX 7900 XTX ROCm/HIP, 'dense-ngl40)'
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-2-123B-Instruct-2512
Kontextlaenge?Maximale Anzahl Tokens (Eingabe + erzeugte Ausgabe zusammen), die das Modell pro Anfrage verarbeiten kann.40960
Modellpfad?Pfad zur GGUF-Modelldatei, die geladen und ausgeliefert wird.mistralai_Devstral-2-123B-Instruct-2512-IQ4_XS/mistralai_Devstral-2-123B-Instruct-2512-IQ4_XS-00001-of-00002.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.40
Split-Mode?Verteilung ueber mehrere GPUs: none (nur eine GPU), layer (Layer aufteilen) oder row (Tensoren zeilenweise).layer
faon
Kontext?Groesse des Kontextfensters in Token. 0 = der beim Training verwendete Kontext des Modells.40960
np10
execution_typelocal

All benchmarks of this model To leaderboard

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Model comparison

Devstral-2-123B-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

18613992,846,40,005731.1471.720Prefill (tok/s)Generation (tok/s)NVIDIA RTX PRO 6000 Blackwell Workstation Edition - 143,1 tok/s Generation, 1.403 tok/s Prefill, TTFT 31.598 ms (3 Laufe)NVIDIA RTX PRO 6000 B...NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition - 123,5 tok/s Generation, 1.389 tok/s Prefill, TTFT 39.803 ms (9 Laufe)NVIDIA RTX PRO 6000 B...AMD Radeon AI PRO R9700 - 50,0 tok/s Generation, 335 tok/s Prefill, TTFT 137.442 ms (29 Laufe)AMD Radeon AI PRO R97...NVIDIA GeForce RTX 5070 Ti - 4,5 tok/s Generation, 127 tok/s Prefill, TTFT 54.082 ms (3 Laufe)NVIDIA GeForce RTX 50...NVIDIA GeForce RTX 3090 Ti - 2,9 tok/s Generation, 129 tok/s Prefill, TTFT 54.597 ms (3 Laufe)NVIDIA GeForce RTX 30...NVIDIA GeForce RTX 5090 - 2,1 tok/s Generation, 113 tok/s Prefill, TTFT 62.542 ms (3 Laufe)NVIDIA GeForce RTX 50...AMD Radeon RX 7900 XTX - 2,1 tok/s Generation, 113 tok/s Prefill, TTFT 118.087 ms (3 Laufe) | DIESER LAUF★ AMD Radeon RX 7900 XTX
NVIDIA RTX PRO 6000 Blackwell Workstation Edition 143,1 tok/sNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 123,5 tok/sAMD Radeon AI PRO R9700 50,0 tok/sNVIDIA GeForce RTX 5070 Ti 4,5 tok/sNVIDIA GeForce RTX 3090 Ti 2,9 tok/s★ AMD Radeon RX 7900 XTX 2,1 tok/s this runNVIDIA GeForce RTX 5090 2,1 tok/s

CPUby processor

18613992,846,40,005731.1471.720Prefill (tok/s)Generation (tok/s)AMD Ryzen 9 9950X 16-Core Processor - 143,1 tok/s Generation, 1.403 tok/s Prefill, TTFT 31.598 ms (3 Laufe)AMD Ryzen 9 9950X 16-...AMD Ryzen Threadripper PRO 9965WX 24-Cores - 123,5 tok/s Generation, 1.389 tok/s Prefill, TTFT 39.803 ms (9 Laufe)AMD Ryzen Threadrippe...AMD Ryzen Threadripper PRO 7955WX 16-Cores - 50,0 tok/s Generation, 335 tok/s Prefill, TTFT 137.442 ms (29 Laufe)AMD Ryzen Threadrippe...AMD Ryzen Threadripper PRO 5975WX 32-Cores - 4,5 tok/s Generation, 127 tok/s Prefill, TTFT 54.082 ms (3 Laufe)AMD Ryzen Threadrippe...AMD Ryzen 9 8945HX with Radeon Graphics - 2,9 tok/s Generation, 129 tok/s Prefill, TTFT 54.597 ms (3 Laufe)AMD Ryzen 9 8945HX wi...AMD Ryzen 7 5800X3D 8-Core Processor - 2,1 tok/s Generation, 113 tok/s Prefill, TTFT 62.542 ms (3 Laufe)AMD Ryzen 7 5800X3D 8...Intel(R) Xeon(R) CPU E5-4657L v2 @ 2.40GHz - 2,1 tok/s Generation, 113 tok/s Prefill, TTFT 118.087 ms (3 Laufe) | DIESER LAUF★ Intel(R) Xeon(R) CPU ...
AMD Ryzen 9 9950X 16-Core Processor 143,1 tok/sAMD Ryzen Threadripper PRO 9965WX 24-Cores 123,5 tok/sAMD Ryzen Threadripper PRO 7955WX 16-Cores 50,0 tok/sAMD Ryzen Threadripper PRO 5975WX 32-Cores 4,5 tok/sAMD Ryzen 9 8945HX with Radeon Graphics 2,9 tok/s★ Intel(R) Xeon(R) CPU E5-4657L v2 @ 2.40GHz 2,1 tok/s this runAMD Ryzen 7 5800X3D 8-Core Processor 2,1 tok/s

MBby mainboard

18613992,846,40,005731.1471.720Prefill (tok/s)Generation (tok/s)ASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI - 143,1 tok/s Generation, 1.403 tok/s Prefill, TTFT 31.598 ms (3 Laufe)ASUSTeK COMPUTER INC....ASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE - 123,5 tok/s Generation, 585 tok/s Prefill, TTFT 114.317 ms (38 Laufe)ASUSTeK COMPUTER INC....ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI - 4,5 tok/s Generation, 127 tok/s Prefill, TTFT 54.082 ms (3 Laufe)ASUSTeK COMPUTER INC....Meigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) - 2,9 tok/s Generation, 129 tok/s Prefill, TTFT 54.597 ms (3 Laufe)Meigao Innovation Tec...ASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING - 2,1 tok/s Generation, 113 tok/s Prefill, TTFT 62.542 ms (3 Laufe)ASUSTeK COMPUTER INC....Dell Inc. PowerEdge R820 - 2,1 tok/s Generation, 113 tok/s Prefill, TTFT 118.087 ms (3 Laufe) | DIESER LAUF★ Dell Inc. PowerEdge R...
ASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI 143,1 tok/sASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE 123,5 tok/sASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 4,5 tok/sMeigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 2,9 tok/s★ Dell Inc. PowerEdge R820 2,1 tok/s this runASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING 2,1 tok/s

ENGby engine

18513892,346,20,0254369484599Prefill (tok/s)Generation (tok/s)unbekannt - 6,5 tok/s Generation, 323 tok/s Prefill, TTFT 6.557 ms (1 Lauf)unbekanntllama.cpp - 143,1 tok/s Generation, 530 tok/s Prefill, TTFT 101.927 ms (52 Laufe) | DIESER LAUF★ llama.cpp
★ llama.cpp 143,1 tok/s this rununbekannt 6,5 tok/s

DRVby driver

18513892,346,20,0254369484599Prefill (tok/s)Generation (tok/s)unbekannt - 143,1 tok/s Generation, 530 tok/s Prefill, TTFT 101.927 ms (52 Laufe)unbekanntAMD 7.0.0-27-generic - 6,5 tok/s Generation, 323 tok/s Prefill, TTFT 6.557 ms (1 Lauf)AMD 7.0.0-27-generic
unbekannt 143,1 tok/sAMD 7.0.0-27-generic 6,5 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 URLReset
⚡ Electricity price EUR/kWh
⚙️ System utilization 100 %
🖥️ Acquisition EUR
🔌 Idle 35 W
⚡ TDP 749 W
☁️ External LLM (API)
Electricity0.30 EUR/kWh
Avg power (incl. idle)749 W estimated (TDP)GPU 710 + CPU 29 + Board 10 W full load
Avg cost / hourEUR 0.22
Electricity / 1M tokensEUR 79.99
Token / kWh3.75K
Acquisition (system)EUR 4,353 partial priceGPU EUR 2,098 · CPU EUR 59 · RAM EUR 2,016 · PSU EUR 180
Electricity (2 years)–
TCO (2 years)EUR 8,288
Output tokens (2 years)49.20M
☁️ 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 (35 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-2-123B-Instruct-25122x AMD Radeon RX 7900 XTXDevstral-2-123B-Instruct-2512NVIDIA RTX PRO 6000 Blackwell Workstation EditionDevstral-2-123B-Instruct-25123x NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation EditionDevstral-2-123B-Instruct-25123x 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)––––

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.