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Contributed byMario AlkaQwen (Alibaba)

Qwen3.5-35B-A3B

Performance benchmark · measured on 02.09.2026 11:30

Benchmark-IDrun-20260902-095330-9cdbd5
Timebench 3 - Kombi (Prefill + Generation)MoE35BRuntime: vLLMQuantisierung: AWQ
Generation40,09tok/s
Prefill2.094,27tok/s
Time to First Token943,00ms
Total duration53,07s
Concurrency1parallel
Ranking in the field
853of 1549 systems

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

This run is better than 45 % of all comparable systems.
Generation 40,1 tok/s
-49 % vs Ø 79,0
Prefill 2.094,3 tok/s
-26 % vs Ø 2.837,1
Time to First Token 943 ms
-97 % vs Ø 26.963
Distribution in the field0 – 405 tok/s
Ø 79 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 AMD Radeon PRO W7800 48GB · 48 GB VRAM
CPU: AMD Ryzen Threadripper PRO 5975WX 32-Cores
RAM: 62 GB
Mainboard: ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI

Setup

Runtime: vLLM
Quantization: AWQ
Model: Qwen3.5-35B-A3B

Configuration

benchmark-konfiguration — run-20260902-095330-9cdbd5
# LLM-Benchmark Konfiguration # Modell : Qwen3.5-35B-A3B # Engine : vLLM # Run-ID : run-20260902-095330-9cdbd5 # GPU : 2x AMD Radeon PRO W7800 48GB # CPU : AMD Ryzen Threadripper PRO 5975WX 32-Cores # RAM : 62 GB bench@llm-benchmark:~$ vllm serve \ --tensor-parallel-size 2 \ --max-model-len 16384 '(ROCm' gfx1100, 2x AMD Radeon Pro W7800 '48GB)'
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.vllm
Kontextlaenge?Maximale Anzahl Tokens (Eingabe + erzeugte Ausgabe zusammen), die das Modell pro Anfrage verarbeiten kann.16384
Tensor-Parallel?Anzahl GPUs, auf die JEDER einzelne Modell-Layer aufgeteilt wird (Tensor-Parallelitaet). Mehr GPUs = mehr VRAM und meist mehr Speed, aber die Anzahl der Attention-Heads muss durch diesen Wert teilbar sein (z.B. 32 Heads -> nur 1, 2, 4, 8 ... moeglich, NICHT 3).2
Kontext?Groesse des Kontextfensters in Token. 0 = der beim Training verwendete Kontext des Modells.16384

All benchmarks of this model To leaderboard

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Model comparison

Qwen3.5-35B-A3B 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

1.9671.4759844920,005.26410.52815.792Prefill (tok/s)Generation (tok/s)NVIDIA RTX PRO 6000 Blackwell Workstation Edition - 1.520,4 tok/s Generation, 12.809 tok/s Prefill, TTFT 2.176 ms (6 Laufe)NVIDIA RTX PRO 6000 B...NVIDIA GeForce RTX 5090 - 1.416,7 tok/s Generation, 5.541 tok/s Prefill, TTFT 3.907 ms (3 Laufe)NVIDIA GeForce RTX 50...NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition - 1.398,4 tok/s Generation, 6.870 tok/s Prefill, TTFT 4.115 ms (11 Laufe)NVIDIA RTX PRO 6000 B...NVIDIA GeForce RTX 3090 Ti - 866,1 tok/s Generation, 3.371 tok/s Prefill, TTFT 6.651 ms (3 Laufe)NVIDIA GeForce RTX 30...NVIDIA RTX A6000 - 842,1 tok/s Generation, 11.384 tok/s Prefill, TTFT 2.504 ms (30 Laufe)NVIDIA RTX A6000AMD Radeon AI PRO R9700 - 220,5 tok/s Generation, 2.194 tok/s Prefill, TTFT 25.070 ms (27 Laufe)AMD Radeon AI PRO R97...AMD Radeon PRO W7900 Dual Slot - 212,5 tok/s Generation, 2.015 tok/s Prefill, TTFT 5.888 ms (12 Laufe)AMD Radeon PRO W7900 ...NVIDIA GeForce RTX 5070 Ti - 142,2 tok/s Generation, 508 tok/s Prefill, TTFT 31.910 ms (6 Laufe)NVIDIA GeForce RTX 50...NVIDIA GB10 (DGX Spark) - 46,9 tok/s Generation, 3.713 tok/s Prefill, TTFT 537 ms (1 Lauf)NVIDIA GB10 (DGX Spar...AMD Radeon PRO W7800 48GB - 204,6 tok/s Generation, 8.423 tok/s Prefill, TTFT 1.706 ms (3 Laufe) | DIESER LAUF★ AMD Radeon PRO W7800 ...
NVIDIA RTX PRO 6000 Blackwell Workstation Edition 1.520,4 tok/sNVIDIA GeForce RTX 5090 1.416,7 tok/sNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 1.398,4 tok/sNVIDIA GeForce RTX 3090 Ti 866,1 tok/sNVIDIA RTX A6000 842,1 tok/sAMD Radeon AI PRO R9700 220,5 tok/sAMD Radeon PRO W7900 Dual Slot 212,5 tok/s★ AMD Radeon PRO W7800 48GB 204,6 tok/s this runNVIDIA GeForce RTX 5070 Ti 142,2 tok/sNVIDIA GB10 (DGX Spark) 46,9 tok/s

CPUby processor

1.9671.4759844920,005.14410.28915.433Prefill (tok/s)Generation (tok/s)AMD Ryzen 9 9950X 16-Core Processor - 1.520,4 tok/s Generation, 12.809 tok/s Prefill, TTFT 2.176 ms (6 Laufe)AMD Ryzen 9 9950X 16-...AMD Ryzen 7 5800X3D 8-Core Processor - 1.416,7 tok/s Generation, 5.541 tok/s Prefill, TTFT 3.907 ms (3 Laufe)AMD Ryzen 7 5800X3D 8...AMD Ryzen Threadripper PRO 9965WX 24-Cores - 1.398,4 tok/s Generation, 6.870 tok/s Prefill, TTFT 4.115 ms (11 Laufe)AMD Ryzen Threadrippe...AMD Ryzen 9 8945HX with Radeon Graphics - 866,1 tok/s Generation, 3.371 tok/s Prefill, TTFT 6.651 ms (3 Laufe)AMD Ryzen 9 8945HX wi...AMD Ryzen Threadripper PRO 7955WX 16-Cores - 842,1 tok/s Generation, 7.031 tok/s Prefill, TTFT 13.193 ms (57 Laufe)AMD Ryzen Threadrippe...NVIDIA Grace - 46,9 tok/s Generation, 3.713 tok/s Prefill, TTFT 537 ms (1 Lauf)NVIDIA GraceAMD Ryzen Threadripper PRO 5975WX 32-Cores - 212,5 tok/s Generation, 2.500 tok/s Prefill, TTFT 12.726 ms (21 Laufe) | DIESER LAUF★ AMD Ryzen Threadrippe...
AMD Ryzen 9 9950X 16-Core Processor 1.520,4 tok/sAMD Ryzen 7 5800X3D 8-Core Processor 1.416,7 tok/sAMD Ryzen Threadripper PRO 9965WX 24-Cores 1.398,4 tok/sAMD Ryzen 9 8945HX with Radeon Graphics 866,1 tok/sAMD Ryzen Threadripper PRO 7955WX 16-Cores 842,1 tok/s★ AMD Ryzen Threadripper PRO 5975WX 32-Cores 212,5 tok/s this runNVIDIA Grace 46,9 tok/s

MBby mainboard

1.9671.4759844920,005.14410.28915.433Prefill (tok/s)Generation (tok/s)ASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI - 1.520,4 tok/s Generation, 12.809 tok/s Prefill, TTFT 2.176 ms (6 Laufe)ASUSTeK COMPUTER INC....ASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING - 1.416,7 tok/s Generation, 5.541 tok/s Prefill, TTFT 3.907 ms (3 Laufe)ASUSTeK COMPUTER INC....ASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE - 1.398,4 tok/s Generation, 7.005 tok/s Prefill, TTFT 11.724 ms (68 Laufe)ASUSTeK COMPUTER INC....Meigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) - 866,1 tok/s Generation, 3.371 tok/s Prefill, TTFT 6.651 ms (3 Laufe)Meigao Innovation Tec...ASUSTeK COMPUTER INC. GX10 - 46,9 tok/s Generation, 3.713 tok/s Prefill, TTFT 537 ms (1 Lauf)ASUSTeK COMPUTER INC....ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI - 212,5 tok/s Generation, 2.500 tok/s Prefill, TTFT 12.726 ms (21 Laufe) | DIESER LAUF★ ASUSTeK COMPUTER INC....
ASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI 1.520,4 tok/sASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING 1.416,7 tok/sASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE 1.398,4 tok/sMeigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 866,1 tok/s★ ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 212,5 tok/s this runASUSTeK COMPUTER INC. GX10 46,9 tok/s

ENGby engine

1.9611.4719804900,0395.18810.33715.486Prefill (tok/s)Generation (tok/s)llama.cpp - 1.520,4 tok/s Generation, 3.670 tok/s Prefill, TTFT 15.058 ms (68 Laufe)llama.cppunbekannt - 78,5 tok/s Generation, 2.652 tok/s Prefill, TTFT 858 ms (5 Laufe)unbekanntvLLM - 1.028,6 tok/s Generation, 12.874 tok/s Prefill, TTFT 2.812 ms (29 Laufe) | DIESER LAUF★ vLLM
llama.cpp 1.520,4 tok/s★ vLLM 1.028,6 tok/s this rununbekannt 78,5 tok/s

DRVby driver

1.9671.4759844920,01.5813.5615.5407.520Prefill (tok/s)Generation (tok/s)unbekannt - 1.520,4 tok/s Generation, 6.450 tok/s Prefill, TTFT 11.510 ms (96 Laufe)unbekanntAMD 7.0.0-27-generic - 78,5 tok/s Generation, 2.652 tok/s Prefill, TTFT 858 ms (5 Laufe)AMD 7.0.0-27-genericNVIDIA 590.48.01 / CUDA 13.1 - 46,9 tok/s Generation, 3.713 tok/s Prefill, TTFT 537 ms (1 Lauf)NVIDIA 590.48.01 / CU...
unbekannt 1.520,4 tok/sAMD 7.0.0-27-generic 78,5 tok/sNVIDIA 590.48.01 / CUDA 13.1 46,9 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 10 W
⚡ TDP 530 W
☁️ External LLM (API)
Electricity0.30 EUR/kWh
Avg power (incl. idle)530 W estimated (TDP)GPU 520 + Board 10 W full load
Avg cost / hourEUR 0.16
Electricity / 1M tokensEUR 1.10
Token / kWh272.31K
Acquisition (system)EUR 8,172 partial priceGPU EUR 5,616 · CPU EUR 1,880 · RAM EUR 496 · PSU EUR 180
Electricity (2 years)
TCO (2 years)EUR 10,958
Output tokens (2 years)2.53B
☁️ 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 (10 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

Qwen3.5-35B-A3B2x AMD Radeon PRO W7800 48GBQwen3.5-35B-A3BNVIDIA GeForce RTX 5090Qwen3.5-35B-A3BNVIDIA RTX PRO 6000 Blackwell Workstation EditionQwen3.5-35B-A3B3x 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)

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.