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Contributed byMario AlkaGoogle

gemma-4-12B-it

Performance benchmark · measured on 26.08.2026 11:33

Benchmark-IDrun-20260826-100607-e1befd
Timebench 3 - Kombi (Prefill + Generation)Dense12BRuntime: llama.cppQuantisierung: Q4_K_M
Generation156,81tok/s
Prefill2.575,75tok/s
Time to First Token3.993,00ms
Total duration39,43s
Concurrency5parallel
Ranking in the field
21of 34 systems

Performance benchmark · Primary metric: Generation-Speed (tok/s) · 5× concurrent

This run is better than 39 % of all comparable systems.
Generation 156,8 tok/s
-18 % vs Ø 191,2
Prefill 2.575,8 tok/s
-47 % vs Ø 4.818,1
Time to First Token 3.993 ms
+28 % vs Ø 3.117
Distribution in the field73 – 463 tok/s
Ø 191 Median Dieser Lauf

Wie schlägt sich dieser Benchmark mit anderen Modellen?

How does this benchmark compare on other GPUs?

Same model on different hardware · 5× concurrent · Generation (tok/s)

Hardware

GPU: NVIDIA RTX A6000 · 45 GB VRAM
CPU: AMD Ryzen Threadripper PRO 7955WX 16-Cores
RAM: 184 GB
Mainboard: ASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE

Setup

Runtime: llama.cpp
Quantization: Q4_K_M
Model: gemma-4-12B-it

Configuration

benchmark-konfiguration — run-20260826-100607-e1befd
# LLM-Benchmark Konfiguration # Modell : gemma-4-12B-it # Engine : llama.cpp # Run-ID : run-20260826-100607-e1befd # GPU : NVIDIA RTX A6000 # CPU : AMD Ryzen Threadripper PRO 7955WX 16-Cores # RAM : 184 GB bench@llm-benchmark:~$ /home/godcore/llama.cpp/build/bin/llama-server \ -m gemma-4-12b-it-Q4_K_M.gguf \ -ngl 999 \ -fa on \ -c 49152 \ -np 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.49152
Modellpfad?Pfad zur GGUF-Modelldatei, die geladen und ausgeliefert wird.gemma-4-12b-it-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.999
faon
Kontext?Groesse des Kontextfensters in Token. 0 = der beim Training verwendete Kontext des Modells.49152
np12

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Model comparison

gemma-4-12B-it 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

1.4261.0707133570,002.1104.2206.330Prefill (tok/s)Generation (tok/s)NVIDIA RTX PRO 6000 Blackwell Workstation Edition - 1.104,3 tok/s Generation, 4.570 tok/s Prefill, TTFT 5.144 ms (3 Laufe)NVIDIA RTX PRO 6000 B...NVIDIA GeForce RTX 5090 - 1.058,9 tok/s Generation, 3.140 tok/s Prefill, TTFT 6.383 ms (3 Laufe)NVIDIA GeForce RTX 50...NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition - 1.041,8 tok/s Generation, 5.150 tok/s Prefill, TTFT 5.222 ms (9 Laufe)NVIDIA RTX PRO 6000 B...NVIDIA GeForce RTX 5070 Ti - 677,5 tok/s Generation, 3.217 tok/s Prefill, TTFT 5.812 ms (6 Laufe)NVIDIA GeForce RTX 50...NVIDIA GeForce RTX 3090 Ti - 654,9 tok/s Generation, 2.298 tok/s Prefill, TTFT 7.684 ms (6 Laufe)NVIDIA GeForce RTX 30...NVIDIA GeForce RTX 2060 - 89,8 tok/s Generation, 779 tok/s Prefill, TTFT 13.058 ms (3 Laufe)NVIDIA GeForce RTX 20...NVIDIA Tesla P100 PCIe 16GB - 45,8 tok/s Generation, 312 tok/s Prefill, TTFT 33.496 ms (3 Laufe)NVIDIA Tesla P100 PCI...NVIDIA RTX A6000 - 169,4 tok/s Generation, 2.466 tok/s Prefill, TTFT 3.798 ms (5 Laufe) | DIESER LAUF★ NVIDIA RTX A6000
NVIDIA RTX PRO 6000 Blackwell Workstation Edition 1.104,3 tok/sNVIDIA GeForce RTX 5090 1.058,9 tok/sNVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 1.041,8 tok/sNVIDIA GeForce RTX 5070 Ti 677,5 tok/sNVIDIA GeForce RTX 3090 Ti 654,9 tok/s★ NVIDIA RTX A6000 169,4 tok/s this runNVIDIA GeForce RTX 2060 89,8 tok/sNVIDIA Tesla P100 PCIe 16GB 45,8 tok/s

CPUby processor

1.4261.0707133570,002.1104.2206.330Prefill (tok/s)Generation (tok/s)AMD Ryzen 9 9950X 16-Core Processor - 1.104,3 tok/s Generation, 4.570 tok/s Prefill, TTFT 5.144 ms (3 Laufe)AMD Ryzen 9 9950X 16-...AMD Ryzen 7 5800X3D 8-Core Processor - 1.058,9 tok/s Generation, 3.140 tok/s Prefill, TTFT 6.383 ms (3 Laufe)AMD Ryzen 7 5800X3D 8...AMD Ryzen Threadripper PRO 9965WX 24-Cores - 1.041,8 tok/s Generation, 5.150 tok/s Prefill, TTFT 5.222 ms (9 Laufe)AMD Ryzen Threadrippe...AMD Ryzen Threadripper PRO 5975WX 32-Cores - 677,5 tok/s Generation, 3.217 tok/s Prefill, TTFT 5.812 ms (6 Laufe)AMD Ryzen Threadrippe...AMD Ryzen 9 8945HX with Radeon Graphics - 654,9 tok/s Generation, 2.617 tok/s Prefill, TTFT 8.920 ms (3 Laufe)AMD Ryzen 9 8945HX wi...AMD Ryzen 5 5600X 6-Core Processor - 372,4 tok/s Generation, 1.979 tok/s Prefill, TTFT 6.448 ms (3 Laufe)AMD Ryzen 5 5600X 6-C...Intel(R) Core(TM) i5-7400 CPU @ 3.00GHz - 89,8 tok/s Generation, 779 tok/s Prefill, TTFT 13.058 ms (3 Laufe)Intel(R) Core(TM) i5-...AMD Ryzen 9 7945HX with Radeon Graphics - 45,8 tok/s Generation, 312 tok/s Prefill, TTFT 33.496 ms (3 Laufe)AMD Ryzen 9 7945HX wi...AMD Ryzen Threadripper PRO 7955WX 16-Cores - 169,4 tok/s Generation, 2.466 tok/s Prefill, TTFT 3.798 ms (5 Laufe) | DIESER LAUF★ AMD Ryzen Threadrippe...
AMD Ryzen 9 9950X 16-Core Processor 1.104,3 tok/sAMD Ryzen 7 5800X3D 8-Core Processor 1.058,9 tok/sAMD Ryzen Threadripper PRO 9965WX 24-Cores 1.041,8 tok/sAMD Ryzen Threadripper PRO 5975WX 32-Cores 677,5 tok/sAMD Ryzen 9 8945HX with Radeon Graphics 654,9 tok/sAMD Ryzen 5 5600X 6-Core Processor 372,4 tok/s★ AMD Ryzen Threadripper PRO 7955WX 16-Cores 169,4 tok/s this runIntel(R) Core(TM) i5-7400 CPU @ 3.00GHz 89,8 tok/sAMD Ryzen 9 7945HX with Radeon Graphics 45,8 tok/s

MBby mainboard

1.4261.0707133570,001.8703.7405.610Prefill (tok/s)Generation (tok/s)ASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI - 1.104,3 tok/s Generation, 4.570 tok/s Prefill, TTFT 5.144 ms (3 Laufe)ASUSTeK COMPUTER INC....ASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING - 1.058,9 tok/s Generation, 3.140 tok/s Prefill, TTFT 6.383 ms (3 Laufe)ASUSTeK COMPUTER INC....ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI - 677,5 tok/s Generation, 3.217 tok/s Prefill, TTFT 5.812 ms (6 Laufe)ASUSTeK COMPUTER INC....Meigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) - 654,9 tok/s Generation, 2.617 tok/s Prefill, TTFT 8.920 ms (3 Laufe)Meigao Innovation Tec...ASUSTeK COMPUTER INC. PRIME A520M-K - 372,4 tok/s Generation, 1.979 tok/s Prefill, TTFT 6.448 ms (3 Laufe)ASUSTeK COMPUTER INC....ASRock H110 Pro BTC+ - 89,8 tok/s Generation, 779 tok/s Prefill, TTFT 13.058 ms (3 Laufe)ASRock H110 Pro BTC+Shenzhen Meigao Electronic Equipment Co.,Ltd F1FXM (DeskMini Series) - 45,8 tok/s Generation, 312 tok/s Prefill, TTFT 33.496 ms (3 Laufe)Shenzhen Meigao Elect...ASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE - 1.041,8 tok/s Generation, 4.191 tok/s Prefill, TTFT 4.713 ms (14 Laufe) | DIESER LAUF★ ASUSTeK COMPUTER INC....
ASUSTeK COMPUTER INC. ProArt X870E-CREATOR WIFI 1.104,3 tok/sASUSTeK COMPUTER INC. ROG STRIX B550-A GAMING 1.058,9 tok/s★ ASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE 1.041,8 tok/s this runASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 677,5 tok/sMeigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 654,9 tok/sASUSTeK COMPUTER INC. PRIME A520M-K 372,4 tok/sASRock H110 Pro BTC+ 89,8 tok/sShenzhen Meigao Electronic Equipment Co.,Ltd F1FXM (DeskMini Series) 45,8 tok/s

ENGby engine

1.3381.0677955242522.5912.8633.1363.408Prefill (tok/s)Generation (tok/s)vLLM - 486,2 tok/s Generation, 2.838 tok/s Prefill, TTFT 4.765 ms (6 Laufe)vLLMllama.cpp - 1.104,3 tok/s Generation, 3.161 tok/s Prefill, TTFT 9.144 ms (32 Laufe) | DIESER LAUF★ llama.cpp
★ llama.cpp 1.104,3 tok/s this runvLLM 486,2 tok/s

DRVby driver

1.2151.1601.1041.0499942.9233.0483.1723.296Prefill (tok/s)Generation (tok/s)unbekannt - 1.104,3 tok/s Generation, 3.110 tok/s Prefill, TTFT 8.453 ms (38 Laufe)unbekannt
unbekannt 1.104,3 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 URLReset
⚡ Electricity price EUR/kWh
⚙️ System utilization 100 %
🖥️ Acquisition EUR
🔌 Idle 65 W
⚡ TDP 71 W
☁️ External LLM (API)
Electricity0.30 EUR/kWh
Avg power (incl. idle)71 W missingCPU 46 + Board 25 W full load
Avg cost / hourEUR 0.021
Electricity / 1M tokensEUR 0.038
Token / kWh7.95M
Acquisition (system)EUR 5,394 missingCPU EUR 1,399 · Board EUR 1,299 · RAM EUR 2,576 · PSU EUR 120
Electricity (2 years)
TCO (2 years)EUR 5,767
Output tokens (2 years)9.89B
☁️ External LLM (API) – comparison
External LLM cost (2 years)
Savings vs. external (2 years)
No power draw measured – values estimated from GPU TDP + CPU (idle + 15 %) + board.

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 (65 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

gemma-4-12B-itNVIDIA RTX A6000gemma-4-12B-itNVIDIA RTX PRO 6000 Blackwell Workstation Editiongemma-4-12B-itNVIDIA GeForce RTX 5090gemma-4-12B-it3x 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.