# LLM-Benchmark Konfiguration # Modell : Qwen3.8-Flash-Next # Engine : llama.cpp # Run-ID : run-20260925-112847-aca38f # GPU : 2x NVIDIA RTX A6000 # CPU : AMD EPYC 7203P 8-Core Processor # RAM : 31 GB bench@llm-benchmark:~$ llama-server Qwen3.8-Flash-Next-UD-Q4_K_XL \ -ngl 99 \ --n-cpu-moe 10 \ -fa on '(2x' A6000 NVLink + 31GB RAM 'Offload)'

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
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
n-cpu-moe	10
fa	on

All benchmarks of this model To leaderboard

Model comparison

Qwen3.8-Flash-Next 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 A6000 35,4 tok/s this run

CPUby processor

★ AMD EPYC 7203P 8-Core Processor 35,4 tok/s this run

MBby mainboard

★ ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 35,4 tok/s this run

ENGby engine

★ llama.cpp 35,4 tok/s this run

DRVby driver

unbekannt 35,4 tok/s

💰 Economics

Economics of this run

Operating cost, TCO and comparison with the next-best runs of the same model at identical concurrency (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 40 W

⚡ TDP 610 W

☁️ External LLM (API) show

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

Electricity0.30 EUR/kWh

Avg power (incl. idle)610 W estimated (TDP)GPU 600 + Board 10 W full load

Avg cost / hourEUR 0.18

Electricity / 1M tokensEUR 2.24

Token / kWh133.79K

Acquisition (system)EUR 6,428 partial priceGPU EUR 6,000 · RAM EUR 248 · PSU EUR 180

Electricity (2 years)–

TCO (2 years)EUR 9,634

Output tokens (2 years)1.43B

☁️ 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 (40 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

	Qwen3.8-Flash-Next2x NVIDIA RTX A6000	Qwen3.8-Flash-Next2x NVIDIA RTX A6000
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)	–	–

2× NVIDIA RTX A6000 · this run 23 tok/s 2× NVIDIA RTX A6000 9 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.