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Contributed byMario AlkaNVIDIA

OpenReasoning-Nemotron-32B

Performance benchmark · measured on 23.07.2026 14:11

Benchmark-IDrun-20260723-142108-cc471c
Timebench 3 - Kombi (Prefill + Generation)Dense32BRuntime: llama.cppQuantisierung: Q4_K_M
For context: Diese Plattform nutzt Unified Memory – die "VRAM" ist gemeinsamer System-RAM (APU/Superchip); das Modell teilt sich den Speicher mit dem System.
Generation16,53tok/s
Prefill691,28tok/s
Time to First Token41.832,00ms
Total duration124,85s
Concurrency10parallel
Ranking in the field
12of 12 systems

Performance benchmark · Primary metric: Generation-Speed (tok/s) · 10× concurrent

This run is better than 0 % of all comparable systems.
Generation 16,5 tok/s
-78 % vs Ø 73,5
Prefill 691,3 tok/s
-45 % vs Ø 1.253,9
Time to First Token 41.832 ms
+85 % vs Ø 22.577
Distribution in the field17 – 132 tok/s
Ø 73 Median Dieser Lauf

Wie schlägt sich dieser Benchmark mit anderen Modellen?

How does this benchmark compare on other GPUs?

Same model on different hardware · 10× concurrent · Generation (tok/s)

Hardware

GPU: AMD Radeon 8060S Graphics
CPU: AMD RYZEN AI MAX+ 395 w/ Radeon 8060S
RAM: 31 GB
Mainboard: Bosgame AXB35-02 (BeyondMax Series)

Setup

Runtime: llama.cpp
Quantization: Q4_K_M
Model: OpenReasoning-Nemotron-32B

Configuration

benchmark-konfiguration — run-20260723-142108-cc471c
# LLM-Benchmark Konfiguration # Modell : OpenReasoning-Nemotron-32B # Engine : llama.cpp # Run-ID : run-20260723-142108-cc471c # GPU : AMD Radeon 8060S Graphics # CPU : AMD RYZEN AI MAX+ 395 w/ Radeon 8060S # RAM : 31 GB bench@llm-benchmark:~$ /home/godcore/llama.cpp/build-rocm/bin/llama-server \ -m /home/godcore/models/dl/nvidia_OpenReasoning-Nemotron-32B-Q4_K_M.gguf \ --host 0.0.0.0 \ --port 8000 \ --gpu-layers 99 \ --flash-attn on \ -c 26624 \ --parallel 12
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
Kontextlaenge?Maximale Anzahl Tokens (Eingabe + erzeugte Ausgabe zusammen), die das Modell pro Anfrage verarbeiten kann.26624
Modellpfad?Pfad zur GGUF-Modelldatei, die geladen und ausgeliefert wird./home/godcore/models/dl/nvidia_OpenReasoning-Nemotron-32B-Q4_K_M.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.99
Flash Attention?FlashAttention fuer schnellere und speichersparende Attention. Wert on/off/auto je nach Build.on
Kontext?Groesse des Kontextfensters in Token. 0 = der beim Training verwendete Kontext des Modells.26624
Parallel?Anzahl paralleler Slots/Sequenzen, die der Server gleichzeitig bedient. Der Kontext wird auf die Slots aufgeteilt.12

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Model comparison

OpenReasoning-Nemotron-32B 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

6254693131560,001.8603.7215.581Prefill (tok/s)Generation (tok/s)NVIDIA GeForce RTX 5090 - 481,0 tok/s Generation, 3.983 tok/s Prefill, TTFT 10.140 ms (3 Laufe)NVIDIA GeForce RTX 50...NVIDIA RTX PRO 6000 Blackwell Workstation Edition - 480,0 tok/s Generation, 4.502 tok/s Prefill, TTFT 9.693 ms (3 Laufe)NVIDIA RTX PRO 6000 B...NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition - 411,7 tok/s Generation, 4.317 tok/s Prefill, TTFT 11.939 ms (9 Laufe)NVIDIA RTX PRO 6000 B...NVIDIA GeForce RTX 3090 Ti - 236,8 tok/s Generation, 2.036 tok/s Prefill, TTFT 20.847 ms (3 Laufe)NVIDIA GeForce RTX 30...NVIDIA RTX A6000 - 144,0 tok/s Generation, 1.970 tok/s Prefill, TTFT 10.842 ms (15 Laufe)NVIDIA RTX A6000NVIDIA GeForce RTX 5070 Ti - 49,9 tok/s Generation, 621 tok/s Prefill, TTFT 94.653 ms (3 Laufe)NVIDIA GeForce RTX 50...AMD Radeon AI PRO R9700 - 23,3 tok/s Generation, 214 tok/s Prefill, TTFT 162.122 ms (7 Laufe)AMD Radeon AI PRO R97...NVIDIA GeForce RTX 2060 - 1,1 tok/s Generation, 8 tok/s Prefill, TTFT 266.668 ms (1 Lauf)NVIDIA GeForce RTX 20...AMD Radeon 8060S Graphics - 16,5 tok/s Generation, 691 tok/s Prefill, TTFT 41.832 ms (1 Lauf) | DIESER LAUF★ AMD Radeon 8060S Grap...
NVIDIA GeForce RTX 5090 481,0 tok/sNVIDIA RTX PRO 6000 Blackwell Workstation Edition 480,0 tok/sNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 411,7 tok/sNVIDIA GeForce RTX 3090 Ti 236,8 tok/sNVIDIA RTX A6000 144,0 tok/sNVIDIA GeForce RTX 5070 Ti 49,9 tok/sAMD Radeon AI PRO R9700 23,3 tok/s★ AMD Radeon 8060S Graphics 16,5 tok/s this runNVIDIA GeForce RTX 2060 1,1 tok/s

CPUby processor

6254693131560,001.8603.7215.581Prefill (tok/s)Generation (tok/s)AMD Ryzen 7 5800X3D 8-Core Processor - 481,0 tok/s Generation, 3.983 tok/s Prefill, TTFT 10.140 ms (3 Laufe)AMD Ryzen 7 5800X3D 8...AMD Ryzen 9 9950X 16-Core Processor - 480,0 tok/s Generation, 4.502 tok/s Prefill, TTFT 9.693 ms (3 Laufe)AMD Ryzen 9 9950X 16-...AMD Ryzen Threadripper PRO 9965WX 24-Cores - 411,7 tok/s Generation, 4.317 tok/s Prefill, TTFT 11.939 ms (9 Laufe)AMD Ryzen Threadrippe...AMD Ryzen 9 8945HX with Radeon Graphics - 236,8 tok/s Generation, 2.036 tok/s Prefill, TTFT 20.847 ms (3 Laufe)AMD Ryzen 9 8945HX wi...AMD Ryzen Threadripper PRO 7955WX 16-Cores - 144,0 tok/s Generation, 1.411 tok/s Prefill, TTFT 58.977 ms (22 Laufe)AMD Ryzen Threadrippe...AMD Ryzen Threadripper PRO 5975WX 32-Cores - 49,9 tok/s Generation, 621 tok/s Prefill, TTFT 94.653 ms (3 Laufe)AMD Ryzen Threadrippe...Intel(R) Xeon(R) CPU E5-4620 v2 @ 2.60GHz - 1,1 tok/s Generation, 8 tok/s Prefill, TTFT 266.668 ms (1 Lauf)Intel(R) Xeon(R) CPU ...AMD RYZEN AI MAX+ 395 w/ Radeon 8060S - 16,5 tok/s Generation, 691 tok/s Prefill, TTFT 41.832 ms (1 Lauf) | DIESER LAUF★ AMD RYZEN AI MAX+ 395...
AMD Ryzen 7 5800X3D 8-Core Processor 481,0 tok/sAMD Ryzen 9 9950X 16-Core Processor 480,0 tok/sAMD Ryzen Threadripper PRO 9965WX 24-Cores 411,7 tok/sAMD Ryzen 9 8945HX with Radeon Graphics 236,8 tok/sAMD Ryzen Threadripper PRO 7955WX 16-Cores 144,0 tok/sAMD Ryzen Threadripper PRO 5975WX 32-Cores 49,9 tok/s★ AMD RYZEN AI MAX+ 395 w/ Radeon 8060S 16,5 tok/s this runIntel(R) Xeon(R) CPU E5-4620 v2 @ 2.60GHz 1,1 tok/s

MBby mainboard

6254693131560,001.8603.7215.581Prefill (tok/s)Generation (tok/s)ASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING - 481,0 tok/s Generation, 3.983 tok/s Prefill, TTFT 10.140 ms (3 Laufe)ASUSTeK COMPUTER INC....ASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI - 480,0 tok/s Generation, 4.502 tok/s Prefill, TTFT 9.693 ms (3 Laufe)ASUSTeK COMPUTER INC....ASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE - 411,7 tok/s Generation, 2.255 tok/s Prefill, TTFT 45.320 ms (31 Laufe)ASUSTeK COMPUTER INC....Meigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) - 236,8 tok/s Generation, 2.036 tok/s Prefill, TTFT 20.847 ms (3 Laufe)Meigao Innovation Tec...ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI - 49,9 tok/s Generation, 621 tok/s Prefill, TTFT 94.653 ms (3 Laufe)ASUSTeK COMPUTER INC....Dell Inc. PowerEdge R820 - 1,1 tok/s Generation, 8 tok/s Prefill, TTFT 266.668 ms (1 Lauf)Dell Inc. PowerEdge R...Bosgame AXB35-02 (BeyondMax Series) - 16,5 tok/s Generation, 691 tok/s Prefill, TTFT 41.832 ms (1 Lauf) | DIESER LAUF★ Bosgame AXB35-02 (Bey...
ASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING 481,0 tok/sASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI 480,0 tok/sASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE 411,7 tok/sMeigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 236,8 tok/sASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 49,9 tok/s★ Bosgame AXB35-02 (BeyondMax Series) 16,5 tok/s this runDell Inc. PowerEdge R820 1,1 tok/s

ENGby engine

5295054814574332.1732.2652.3582.450Prefill (tok/s)Generation (tok/s)llama.cpp - 481,0 tok/s Generation, 2.312 tok/s Prefill, TTFT 47.098 ms (45 Laufe) | DIESER LAUF★ llama.cpp
★ llama.cpp 481,0 tok/s this run

DRVby driver

5295054814574332.1732.2652.3582.450Prefill (tok/s)Generation (tok/s)unbekannt - 481,0 tok/s Generation, 2.312 tok/s Prefill, TTFT 47.098 ms (45 Laufe)unbekannt
unbekannt 481,0 tok/s
💰 Economics

Economics of this run

Operating cost, TCO and comparison with the next-best runs of the same model at identical concurrency (10× 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 18 W
⚡ TDP 120 W
☁️ External LLM (API)
Electricity0.30 EUR/kWh
Avg power (incl. idle)120 W estimated (TDP)GPU 120 W full load
Avg cost / hourEUR 0.036
Electricity / 1M tokensEUR 0.60
Token / kWh495.90K
Acquisition (system)EUR 7,054 partial priceGPU EUR 3,000 · Board EUR 3,500 · RAM EUR 434 · PSU EUR 120
Electricity (2 years)
TCO (2 years)EUR 7,685
Output tokens (2 years)1.04B
☁️ 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 (18 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

OpenReasoning-Nemotron-32BAMD Radeon 8060S GraphicsOpenReasoning-Nemotron-32BNVIDIA GeForce RTX 5090OpenReasoning-Nemotron-32BNVIDIA RTX PRO 6000 Blackwell Workstation EditionOpenReasoning-Nemotron-32B3x 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.