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Contributed byMario AlkaGoogle

gemma-4-12B-it

Performance benchmark · measured on 27.08.2026 09:19

Benchmark-IDrun-20260827-073158-8268ef
Timebench 3 - Kombi (Prefill + Generation)Dense12BRuntime: vLLMQuantisierung: AWQ
Generation610,99tok/s
Prefill14.285,14tok/s
Time to First Token2.134,00ms
Total duration38,00s
Concurrency10parallel
Ranking in the field
14of 120 systems

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

This run is better than 89 % of all comparable systems.
Generation 611,0 tok/s
+74 % vs Ø 351,2
Prefill 14.285,1 tok/s
+26 % vs Ø 11.362,6
Time to First Token 2.134 ms
-66 % vs Ø 6.186
Distribution in the field32 – 1.359 tok/s
Ø 351 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: 2x NVIDIA RTX A6000 · 48 GB VRAM
CPU: AMD Ryzen Threadripper PRO 7955WX 16-Cores
RAM: 184 GB
Mainboard: ASUSTeK COMPUTER INC. Pro WS WRX90E-SAGE SE

Setup

Runtime: vLLM
Quantization: AWQ
Model: gemma-4-12B-it

Configuration

benchmark-konfiguration — run-20260827-073158-8268ef
# LLM-Benchmark Konfiguration # Modell : gemma-4-12B-it # Engine : vLLM # Run-ID : run-20260827-073158-8268ef # GPU : 2x NVIDIA RTX A6000 # CPU : AMD Ryzen Threadripper PRO 7955WX 16-Cores # RAM : 184 GB bench@llm-benchmark:~$ vllm \ --tensor-parallel-size 2 \ --max-model-len 16384 '(2x' RTX A6000 '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

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

1.4261.0707133570,002.5935.1857.778Prefill (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 - 611,0 tok/s Generation, 6.318 tok/s Prefill, TTFT 2.715 ms (20 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 611,0 tok/s this runNVIDIA GeForce RTX 2060 89,8 tok/sNVIDIA Tesla P100 PCIe 16GB 45,8 tok/s

CPUby processor

1.4261.0707133570,002.5935.1857.778Prefill (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 - 611,0 tok/s Generation, 6.318 tok/s Prefill, TTFT 2.715 ms (20 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/s★ AMD Ryzen Threadripper PRO 7955WX 16-Cores 611,0 tok/s this runAMD Ryzen 5 5600X 6-Core Processor 372,4 tok/sIntel(R) Core(TM) i5-7400 CPU @ 3.00GHz 89,8 tok/sAMD Ryzen 9 7945HX with Radeon Graphics 45,8 tok/s

MBby mainboard

1.4261.0707133570,002.4434.8867.329Prefill (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, 5.955 tok/s Prefill, TTFT 3.493 ms (29 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

1.3131.0868586304022.0804.0045.9277.851Prefill (tok/s)Generation (tok/s)llama.cpp - 1.104,3 tok/s Generation, 3.143 tok/s Prefill, TTFT 8.848 ms (35 Laufe)llama.cppvLLM - 611,0 tok/s Generation, 6.788 tok/s Prefill, TTFT 2.601 ms (18 Laufe) | DIESER LAUF★ vLLM
llama.cpp 1.104,3 tok/s★ vLLM 611,0 tok/s this run

DRVby driver

1.2151.1601.1041.0499944.1184.2944.4694.644Prefill (tok/s)Generation (tok/s)unbekannt - 1.104,3 tok/s Generation, 4.381 tok/s Prefill, TTFT 6.726 ms (53 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 (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 95 W
⚡ TDP 671 W
☁️ External LLM (API)
Electricity0.30 EUR/kWh
Avg power (incl. idle)671 W estimated (TDP)GPU 600 + CPU 46 + Board 25 W full load
Avg cost / hourEUR 0.20
Electricity / 1M tokensEUR 0.092
Token / kWh3.28M
Acquisition (system)EUR 11,454 full priceGPU EUR 6,000 · CPU EUR 1,399 · Board EUR 1,299 · RAM EUR 2,576 · PSU EUR 180
Electricity (2 years)
TCO (2 years)EUR 14,981
Output tokens (2 years)38.54B
☁️ 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 (95 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-it2x NVIDIA 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.