# LLM-Benchmark Konfiguration # Modell : Meta-Llama-3.1-8B-Instruct # Engine : llama.cpp # Run-ID : run-20260807-053423-490903 # GPU : Intel Arc Pro B70 # CPU : AMD Ryzen 9 7945HX with Radeon Graphics # RAM : 29 GB bench@llm-benchmark:~$ /root/llama.cpp/build/bin/llama-server \ -m /root/models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf \ --host 0.0.0.0 \ --port 8000 \ --alias meta-llama-3-1-8b-instruct \ -c 8192 \ -ngl 99

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.	meta-llama-3-1-8b-instruct
Kontextlaenge?Maximale Anzahl Tokens (Eingabe + erzeugte Ausgabe zusammen), die das Modell pro Anfrage verarbeiten kann.	8192
Modellpfad?Pfad zur GGUF-Modelldatei, die geladen und ausgeliefert wird.	/root/models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf
Kontext?Groesse des Kontextfensters in Token. 0 = der beim Training verwendete Kontext des Modells.	8192
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.	99

All benchmarks of this model To leaderboard

Model comparison

Meta-Llama-3.1-8B-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 1.593,1 tok/sNVIDIA RTX PRO 6000 Blackwell Workstation Edition 1.533,0 tok/sNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 1.433,0 tok/sNVIDIA GeForce RTX 5070 Ti 913,4 tok/sNVIDIA GeForce RTX 3090 Ti 837,9 tok/sAMD Radeon AI PRO R9700 518,3 tok/sNVIDIA GeForce RTX 2060 130,9 tok/sNVIDIA Tesla P100 PCIe 16GB 67,6 tok/s★ Intel Arc Pro B70 66,4 tok/s this runCPU-only 3,6 tok/s

CPUby processor

AMD Ryzen 7 5800X3D 8-Core Processor 1.593,1 tok/sAMD Ryzen 9 9950X 16-Core Processor 1.533,0 tok/sAMD Ryzen Threadripper PRO 9965WX 24-Cores 1.433,0 tok/sAMD Ryzen Threadripper PRO 5975WX 32-Cores 913,4 tok/sAMD Ryzen 9 8945HX with Radeon Graphics 837,9 tok/sAMD Ryzen Threadripper PRO 7955WX 16-Cores 518,3 tok/sIntel(R) Core(TM) i5-7400 CPU @ 3.00GHz 130,9 tok/s★ AMD Ryzen 9 7945HX with Radeon Graphics 67,6 tok/s this runIntel(R) Xeon(R) CPU E5-4620 v2 @ 2.60GHz 3,6 tok/s

MBby mainboard

ASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING 1.593,1 tok/sASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI 1.533,0 tok/sASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE 1.433,0 tok/sASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 913,4 tok/sMeigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 837,9 tok/sASRock H110 Pro BTC+ 130,9 tok/sShenzhen Meigao Electronic Equipment Co.,Ltd F1FXM (DeskMini Series) 67,6 tok/s★ Shenzhen Meigao Electronic Equipment Co.,Ltd DRFXI (MotherBoard Series) 66,4 tok/s this runDell Inc. PowerEdge R820 3,6 tok/s

ENGby engine

★ llama.cpp 1.593,1 tok/s this rununbekannt 42,1 tok/s

DRVby driver

unbekannt 1.593,1 tok/s★ Intel 26.18.38308.4 66,4 tok/s this run

💰 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 0 W

⚡ TDP 230 W

☁️ External LLM (API) show

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

Electricity0.30 EUR/kWh

Avg power (incl. idle)230 W estimated (TDP)GPU 230 W full load

Avg cost / hourEUR 0.069

Electricity / 1M tokensEUR 0.29

Token / kWh1.04M

Acquisition (system)EUR 120 missingPSU EUR 120

Electricity (2 years)–

TCO (2 years)EUR 1,329

Output tokens (2 years)4.19B

☁️ 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

	Meta-Llama-3.1-8B-InstructIntel Arc Pro B70	Meta-Llama-3.1-8B-InstructNVIDIA GeForce RTX 5090	Meta-Llama-3.1-8B-InstructNVIDIA RTX PRO 6000 Blackwell Workstation Edition	Meta-Llama-3.1-8B-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)	–	–	–	–

Intel Arc Pro B70 · this run 66 tok/s NVIDIA GeForce RTX 5090 246 tok/s NVIDIA RTX PRO 6000 Blackwell Workstation Edition 239 tok/s 3× NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 225 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.