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Contributed byMario AlkaQwen (Alibaba)

Qwen3.8-Flash-Next

Performance benchmark · measured on 25.09.2026 13:08

Benchmark-IDrun-20260925-112847-aca38f
Timebench 3 - Kombi (Prefill + Generation)MoE180BRuntime: llama.cppQuantisierung: UD-Q4_K_XL
Generation22,67tok/s
Prefill123,91tok/s
Time to First Token18.211,00ms
Total duration126,80s
Concurrency1parallel
Ranking in the field
315of 340 systems

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

This run is better than 7 % of all comparable systems.
Generation 22,7 tok/s
-77 % vs Ø 98,0
Prefill 123,9 tok/s
-97 % vs Ø 4.706,1
Time to First Token 18.211 ms
+1.799 % vs Ø 959
Distribution in the field5 – 218 tok/s
Ø 98 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 NVIDIA RTX A6000 · 48 GB VRAM
CPU: AMD EPYC 7203P 8-Core Processor
RAM: 31 GB
Mainboard: ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI

Setup

Runtime: llama.cpp
Quantization: UD-Q4_K_XL
Model: Qwen3.8-Flash-Next

Configuration

benchmark-konfiguration — run-20260925-112847-aca38f
# 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-moe10
faon
execution_typelocal

All benchmarks of this model To leaderboard

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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

2.0301.5231.0155080,009451.8902.835Prefill (tok/s)Generation (tok/s)NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition - 1.564,6 tok/s Generation, 2.312 tok/s Prefill, TTFT 17.248 ms (6 Laufe)NVIDIA RTX PRO 6000 B...AMD Radeon RX 7900 XTX - 18,7 tok/s Generation, 180 tok/s Prefill, TTFT 55.956 ms (3 Laufe)AMD Radeon RX 7900 XTXNVIDIA RTX A6000 - 71,7 tok/s Generation, 282 tok/s Prefill, TTFT 43.934 ms (12 Laufe) | DIESER LAUF★ NVIDIA RTX A6000
NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 1.564,6 tok/s★ NVIDIA RTX A6000 71,7 tok/s this runAMD Radeon RX 7900 XTX 18,7 tok/s

CPUby processor

2.0301.5231.0155080,009451.8902.835Prefill (tok/s)Generation (tok/s)AMD Ryzen 9 7945HX with Radeon Graphics - 1.564,6 tok/s Generation, 2.312 tok/s Prefill, TTFT 17.248 ms (6 Laufe)AMD Ryzen 9 7945HX wi...AMD Ryzen Threadripper PRO 3955WX 16-Cores - 18,7 tok/s Generation, 180 tok/s Prefill, TTFT 55.956 ms (3 Laufe)AMD Ryzen Threadrippe...AMD EPYC 7203P 8-Core Processor - 71,7 tok/s Generation, 282 tok/s Prefill, TTFT 43.934 ms (12 Laufe) | DIESER LAUF★ AMD EPYC 7203P 8-Core...
AMD Ryzen 9 7945HX with Radeon Graphics 1.564,6 tok/s★ AMD EPYC 7203P 8-Core Processor 71,7 tok/s this runAMD Ryzen Threadripper PRO 3955WX 16-Cores 18,7 tok/s

MBby mainboard

2.0271.5201.0135070,009451.8912.836Prefill (tok/s)Generation (tok/s)Shenzhen Meigao Electronic Equipment Co.,Ltd DRFXI (MotherBoard Series) - 1.564,6 tok/s Generation, 2.312 tok/s Prefill, TTFT 17.248 ms (6 Laufe)Shenzhen Meigao Elect...ASRockRack ROMED8-2T/BCM - 71,7 tok/s Generation, 399 tok/s Prefill, TTFT 25.947 ms (6 Laufe)ASRockRack ROMED8-2T/...ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI - 35,4 tok/s Generation, 170 tok/s Prefill, TTFT 59.933 ms (9 Laufe) | DIESER LAUF★ ASUSTeK COMPUTER INC....
Shenzhen Meigao Electronic Equipment Co.,Ltd DRFXI (MotherBoard Series) 1.564,6 tok/sASRockRack ROMED8-2T/BCM 71,7 tok/s★ ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 35,4 tok/s this run

ENGby engine

1.7231.6411.5601.4781.3971721.0201.8672.714Prefill (tok/s)Generation (tok/s)llm - 1.564,6 tok/s Generation, 2.277 tok/s Prefill, TTFT 17.310 ms (3 Laufe)llmllama.cpp - 1.555,3 tok/s Generation, 609 tok/s Prefill, TTFT 41.480 ms (18 Laufe) | DIESER LAUF★ llama.cpp
llm 1.564,6 tok/s★ llama.cpp 1.555,3 tok/s this run

DRVby driver

1.7211.6431.5651.4861.408797830864898Prefill (tok/s)Generation (tok/s)unbekannt - 1.564,6 tok/s Generation, 847 tok/s Prefill, TTFT 38.027 ms (21 Laufe)unbekannt
unbekannt 1.564,6 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 40 W
⚡ TDP 610 W
☁️ External LLM (API)
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 A6000Qwen3.8-Flash-NextNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation EditionQwen3.8-Flash-NextNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation EditionQwen3.8-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)––––

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.