# LLM-Benchmark Konfiguration # Modell : Qwen3.8-Flash-Next # Run-ID : run-20261021-093816-13ea4f # GPU : NVIDIA Tesla V100 PCIe 32GB # CPU : 4x Intel(R) Xeon(R) CPU E5-4657L v2 @ 2.40GHz # RAM : 504 GB bench@llm-benchmark:~$ cat benchmark.conf execution_type local

execution_type

local

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 PRO 6000 Blackwell Max-Q Workstation Edition 1.564,6 tok/sNVIDIA RTX A6000 1.171,8 tok/sNVIDIA GeForce RTX 3090 1.132,7 tok/sNVIDIA GB10 (DGX Spark) 492,8 tok/s★ NVIDIA Tesla V100 PCIe 32GB 196,2 tok/s this runAMD Radeon RX 7900 XTX 18,7 tok/s

CPUby processor

AMD Ryzen 9 7945HX with Radeon Graphics 1.564,6 tok/sAMD Ryzen Threadripper PRO 3945WX 12-Cores 1.171,8 tok/sAMD Ryzen 5 5600X 6-Core Processor 1.132,7 tok/sNVIDIA Grace 492,8 tok/sAMD Ryzen 3 PRO 3200GE w/ Radeon Vega Graphics 196,2 tok/sAMD EPYC 7203P 8-Core Processor 79,2 tok/s★ Intel(R) Xeon(R) CPU E5-4657L v2 @ 2.40GHz 63,2 tok/s this runAMD Ryzen Threadripper PRO 3955WX 16-Cores 18,7 tok/s

MBby mainboard

Shenzhen Meigao Electronic Equipment Co.,Ltd DRFXI (MotherBoard Series) 1.564,6 tok/sunbekannt 1.171,8 tok/sASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING 1.132,7 tok/sASUSTeK COMPUTER INC. GX10 492,8 tok/sASUSTeK COMPUTER INC. ROG STRIX X570-F GAMING 196,2 tok/sASRockRack ROMED8-2T/BCM 79,2 tok/s★ Dell Inc. PowerEdge R820 63,2 tok/s this runASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 35,4 tok/s

ENGby engine

strata 1.564,6 tok/sllama.cpp 1.555,3 tok/sunbekannt 1.171,8 tok/svLLM 122,4 tok/s

DRVby driver

unbekannt 1.564,6 tok/sNVIDIA 590.48.01 / CUDA 13.1 492,8 tok/s

💰 Economics

Economics of this run

Operating cost, TCO and comparison with the next-best runs of the same model at identical concurrency (5× 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 35 W

⚡ TDP 289 W

☁️ External LLM (API) show

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

Electricity0.30 EUR/kWh

Avg power (incl. idle)289 W estimated (TDP)GPU 250 + CPU 29 + Board 10 W full load

Avg cost / hourEUR 0.087

Electricity / 1M tokensEUR 0.38

Token / kWh788.32K

Acquisition (system)EUR 2,795 partial priceGPU EUR 600 · CPU EUR 59 · RAM EUR 2,016 · PSU EUR 120

Electricity (2 years)–

TCO (2 years)EUR 4,313

Output tokens (2 years)3.99B

☁️ 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

	Qwen3.8-Flash-NextNVIDIA Tesla V100 PCIe 32GB	Qwen3.8-Flash-NextNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition	Qwen3.8-Flash-NextNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition	Qwen3.8-Flash-NextNVIDIA 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)	–	–	–	–

NVIDIA Tesla V100 PCIe 32GB · this run 63 tok/s NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 804 tok/s NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 784 tok/s NVIDIA RTX A6000 589 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

Marcel Sommer

@marcelsommer