Contributed byMario AlkaNVIDIA Nemotron-3-Nano-Omni-30B-A3B-Reasoning
Performance benchmark · measured on 08.10.2026 10:37
Benchmark-ID
run-20261009-081230-662182Timebench 3 - Kombi (Prefill + Generation)MoE30BRuntime: llama.cppQuantisierung: Q4_K_M
ℹ
Public note: Dell PowerEdge R820 . 2x AMD Radeon RX 7900 XTX (48 GB VRAM gesamt, ROCm/HIP) . RAM: 32x 16 GB SK Hynix DDR3-1600 ECC RDIMM (HMT42GR7AFR4A-PB) = 512 GB @ 1600 MT/s
Generation76,68tok/s
Prefill2.233,69tok/s
Time to First Token966,00ms
Total duration28,65s
Concurrency1parallel
Ranking in the field
45of 122 systems
Performance benchmark · Primary metric: Generation-Speed (tok/s) · 1× concurrent
This run is better than 64 % of all comparable systems.
Generation
76,7 tok/s
+12 % vs Ø 68,2
Prefill
2.233,7 tok/s
-31 % vs Ø 3.254,3
Time to First Token
966 ms
-79 % vs Ø 4.539
Distribution in the field1 – 244 tok/s
Wie schlägt sich dieser Benchmark mit anderen Modellen?
Qwen3-0.6BAMD Radeon RX 7900 XTX · run-20260922-095735-545662
243,7 tok/s
Llama-3.2-1B-Instruct2× 2x AMD Radeon RX 7900 XTX · run-20260927-001710-d4b69b
209,0 tok/s
Llama-3.2-1B-Instruct2× 2x AMD Radeon RX 7900 XTX · run-20261008-105916-ae59c6
207,1 tok/s
gpt-oss-20bAMD Radeon RX 7900 XTX · run-20260922-115731-433d6d
154,9 tok/s
gpt-oss-20bAMD Radeon RX 7900 XTX · run-20260922-115731-0214a1
150,5 tok/s
Qwen3-0.6B2× 2x AMD Radeon RX 7900 XTX · run-20261008-105914-0699ab
143,4 tok/s
Llama-3.1-Nemotron-Nano-4B-v1.1AMD Radeon RX 7900 XTX · run-20260922-163818-53557b
139,3 tok/s
Qwen3-1.7B2× 2x AMD Radeon RX 7900 XTX · run-20261008-105915-12cae0
136,1 tok/s
gemma-4-E2B-itAMD Radeon RX 7900 XTX · run-20260922-115730-6b8875
133,2 tok/s
Qwen3-1.7B2× 2x AMD Radeon RX 7900 XTX · run-20260926-214312-91d307
132,3 tok/s
Qwen3-Coder-30B-A3B-InstructAMD Radeon RX 7900 XTX · run-20260922-140931-cdaee1
117,3 tok/s
Qwen3-30B-A3B-Instruct-2507AMD Radeon RX 7900 XTX · run-20260922-133638-f3fdcb
115,0 tok/s
Qwen3-30B-A3B-Thinking-2507AMD Radeon RX 7900 XTX · run-20260922-133638-425bd9
114,7 tok/s
Qwen3-Omni-30B-A3B-ThinkingAMD Radeon RX 7900 XTX · run-20260922-140931-defe2e
112,9 tok/s
Nemotron-3-Nano-Omni-30B-A3B-Reasoning this run2× 2x AMD Radeon RX 7900 XTX · run-20261009-081230-662182
76,7 tok/s
How does this benchmark compare on other GPUs?
Same model on different hardware · 1× concurrent · Generation (tok/s)
NVIDIA GeForce RTX 5090run-20260729-105334-2c4905
359,3 tok/s
NVIDIA RTX PRO 6000 Blackwell Workstation Editionrun-20260729-032119-4852bf
357,1 tok/s
3× NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Editionrun-20260729-105339-e213a2
331,0 tok/s
3× NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Editionrun-20260729-105339-133b6a
330,1 tok/s
3× NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Editionrun-20260729-105340-0a8541
326,8 tok/s
NVIDIA GeForce RTX 3090 Tirun-20260729-035835-89abde
221,8 tok/s
2× NVIDIA RTX A6000run-20260828-084107-7e4501
160,2 tok/s
2× NVIDIA RTX A6000run-20260830-015225-290f60
159,9 tok/s
NVIDIA RTX A6000run-20260826-131708-823486
146,2 tok/s
2× NVIDIA RTX A6000run-20260828-073525-4f434b
145,5 tok/s
2× NVIDIA RTX A6000run-20260829-212822-8a1077
145,2 tok/s
NVIDIA RTX A6000run-20260826-131708-6158f8
132,4 tok/s
2× NVIDIA RTX A6000run-20260827-024202-60bd13
129,4 tok/s
NVIDIA RTX A6000run-20260826-225451-804f23
120,9 tok/s
Hardware
GPU: 2x AMD Radeon RX 7900 XTX · 24 GB VRAM
CPU: 4x Intel(R) Xeon(R) CPU E5-4657L v2 @ 2.40GHz
RAM: 504 GB
Mainboard: Dell Inc. PowerEdge R820
Setup
Runtime: llama.cpp
Quantization: Q4_K_M
Model: Nemotron-3-Nano-Omni-30B-A3B-Reasoning
Configuration
# LLM-Benchmark Konfiguration
# Modell : Nemotron-3-Nano-Omni-30B-A3B-Reasoning
# Engine : llama.cpp
# Run-ID : run-20261009-081230-662182
# GPU : 2x AMD Radeon RX 7900 XTX
# CPU : 4x Intel(R) Xeon(R) CPU E5-4657L v2 @ 2.40GHz
# RAM : 504 GB
bench@llm-benchmark:~$ llama-server \
-m NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-UD-IQ4_XS.gguf \
--alias Nemotron-3-Nano-Omni-30B-A3B-Reasoning \
-ngl 999 \
-sm layer \
-fa on \
-c 40960 \
-np 10 '(2x' RX 7900 XTX ROCm/HIP, 'gpu)'
| 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 |
| Modellalias?Der Name, unter dem das Modell ueber die API angesprochen wird. Genau dieser Wert muss im Request-Feld 'model' stehen. | Nemotron-3-Nano-Omni-30B-A3B-Reasoning |
| Kontextlaenge?Maximale Anzahl Tokens (Eingabe + erzeugte Ausgabe zusammen), die das Modell pro Anfrage verarbeiten kann. | 40960 |
| Modellpfad?Pfad zur GGUF-Modelldatei, die geladen und ausgeliefert wird. | NVIDIA-Nemotron-3-Nano-Omni-30B-A3B-Reasoning-UD-IQ4_XS.gguf |
| 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. | 999 |
| Split-Mode?Verteilung ueber mehrere GPUs: none (nur eine GPU), layer (Layer aufteilen) oder row (Tensoren zeilenweise). | layer |
| fa | on |
| Kontext?Groesse des Kontextfensters in Token. 0 = der beim Training verwendete Kontext des Modells. | 40960 |
| np | 10 |
| execution_type | local |
Anzeige
Model comparison
Nemotron-3-Nano-Omni-30B-A3B-Reasoning 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 Workstation Edition 1.791,5 tok/sNVIDIA GeForce RTX 5090 1.706,7 tok/sNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 1.651,0 tok/sNVIDIA GeForce RTX 3090 Ti 896,6 tok/sNVIDIA RTX A6000 527,0 tok/sNVIDIA GeForce RTX 5070 Ti 262,5 tok/sAMD Radeon AI PRO R9700 122,4 tok/s★ AMD Radeon RX 7900 XTX 76,7 tok/s this run
CPUby processor
AMD Ryzen 9 9950X 16-Core Processor 1.791,5 tok/sAMD Ryzen 7 5800X3D 8-Core Processor 1.706,7 tok/sAMD Ryzen Threadripper PRO 9965WX 24-Cores 1.651,0 tok/sAMD Ryzen 9 8945HX with Radeon Graphics 896,6 tok/sAMD Ryzen Threadripper PRO 7955WX 16-Cores 527,0 tok/sAMD Ryzen Threadripper PRO 5975WX 32-Cores 262,5 tok/s★ Intel(R) Xeon(R) CPU E5-4657L v2 @ 2.40GHz 76,7 tok/s this run
MBby mainboard
ASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI 1.791,5 tok/sASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING 1.706,7 tok/sASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE 1.651,0 tok/sMeigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 896,6 tok/sASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 262,5 tok/s★ Dell Inc. PowerEdge R820 76,7 tok/s this run
DRVby driver
unbekannt 1.791,5 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
35 W
⚡ TDP
749 W
☁️ External LLM (API)
Electricity0.30 EUR/kWh
Avg power (incl. idle)749 W estimated (TDP)GPU 710 + CPU 29 + Board 10 W full load
Avg cost / hourEUR 0.22
Electricity / 1M tokensEUR 0.81
Token / kWh368.68K
Acquisition (system)EUR 4,353 partial priceGPU EUR 2,098 · CPU EUR 59 · RAM EUR 2,016 · PSU EUR 180
Electricity (2 years)–
TCO (2 years)EUR 8,288
Output tokens (2 years)4.84B
☁️ 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
| Nemotron-3-Nano-Omni-30B-A3B-Reasoning2x AMD Radeon RX 7900 XTX | Nemotron-3-Nano-Omni-30B-A3B-ReasoningNVIDIA GeForce RTX 5090 | Nemotron-3-Nano-Omni-30B-A3B-ReasoningNVIDIA RTX PRO 6000 Blackwell Workstation Edition | Nemotron-3-Nano-Omni-30B-A3B-Reasoning3x 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) | – | – | – | – |
2× AMD Radeon RX 7900 XTX · this run 77 tok/sNVIDIA GeForce RTX 5090 359 tok/sNVIDIA RTX PRO 6000 Blackwell Workstation Edition 357 tok/s3× NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 331 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.