# LLM-Benchmark Konfiguration # Modell : Phi-3.5-mini-instruct # Engine : lmstudio-mlx # Run-ID : run-20261112-144801-d3ca7e # GPU : Keine GPU (CPU-only) # CPU : Apple M6 (unified CPU/GPU SoC, ARM64, AdvSIMD) # RAM : 24 GB bench@llm-benchmark:~$ lmstudio-mlx \ --model phi-3.5-mini-instruct

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.	lmstudio-mlx
Kontextlaenge?Maximale Anzahl Tokens (Eingabe + erzeugte Ausgabe zusammen), die das Modell pro Anfrage verarbeiten kann.	8192
execution_type	local

All benchmarks of this model To leaderboard

Model comparison

Phi-3.5-mini-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

AMD Radeon RX 7900 XTX 302,0 tok/s★ CPU-only 41,6 tok/s this run

CPUby processor

AMD Ryzen Threadripper PRO 3955WX 16-Cores 302,0 tok/sIntel(R) Xeon(R) CPU E5-4657L v2 @ 2.40GHz 272,9 tok/s★ Apple M6 (unified CPU/GPU SoC, ARM64, AdvSIMD) 41,6 tok/s this run

MBby mainboard

ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 302,0 tok/sDell Inc. PowerEdge R820 272,9 tok/s★ Apple Mac mini 41,6 tok/s this run

ENGby engine

vLLM 302,0 tok/sllama.cpp 272,9 tok/s★ lmstudio-mlx 41,6 tok/s this run

DRVby driver

unbekannt 302,0 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 %

🔌 Idle 0 W

⚡ TDP 10 W

☁️ External LLM (API) show

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

Electricity0.30 EUR/kWh

Avg power (incl. idle)0 W missing

Avg cost / hour–

Electricity / 1M tokens–

Token / kWh–

Acquisition (system)– partial price

Electricity (2 years)–

TCO (2 years)–

Output tokens (2 years)2.62B

☁️ External LLM (API) – comparison

External LLM cost (2 years)–

Savings vs. external (2 years)–

No power draw measured – values estimated from GPU TDP + CPU (idle + 15 %) + board.

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

	Phi-3.5-mini-instructKeine GPU (CPU-only)	Phi-3.5-mini-instruct2x AMD Radeon RX 7900 XTX	Phi-3.5-mini-instruct2x AMD Radeon RX 7900 XTX
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)	–	–	–

Keine GPU (CPU-only) · this run 42 tok/s 2× AMD Radeon RX 7900 XTX 99 tok/s 2× AMD Radeon RX 7900 XTX 39 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.