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Contributed byMario AlkaGoogle

gemma-4-E4B-it

Performance benchmark · measured on 27.07.2026 13:47

Benchmark-IDrun-20260727-142111-abcb0a
Timebench 3 - Kombi (Prefill + Generation)Dense8BRuntime: llama.cppQuantisierung: Q4_K_M
Generation155,81tok/s
Prefill6.094,08tok/s
Time to First Token324,50ms
Total duration13,79s
Concurrency1parallel
Ranking in the field
This GPUAll GPUs
10of 15 systems

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

This run is better than 36 % of all comparable systems.
Generation 155,8 tok/s
+6 % vs Ø 146,3
Prefill 6.094,1 tok/s
+20 % vs Ø 5.063,5
Time to First Token 325 ms
-85 % vs Ø 2.179
Distribution in the field1 – 246 tok/s
Ø 146 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)

NVIDIA GeForce RTX 5070 Tirun-20260727-090152-82df54
165,0 tok/s
NVIDIA GeForce RTX 3090 Ti this runrun-20260727-142111-abcb0a
155,8 tok/s
NVIDIA GeForce RTX 5070 Tirun-20260727-032758-68f60a
144,6 tok/s

Hardware

GPU: NVIDIA GeForce RTX 3090 Ti · 24 GB VRAM
CPU: AMD Ryzen 9 8945HX with Radeon Graphics
RAM: 92 GB
Mainboard: Meigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series)

Setup

Runtime: llama.cpp
Quantization: Q4_K_M
Model: gemma-4-E4B-it

Configuration

benchmark-konfiguration — run-20260727-142111-abcb0a
# LLM-Benchmark Konfiguration # Modell : gemma-4-E4B-it # Engine : llama.cpp # Run-ID : run-20260727-142111-abcb0a # GPU : NVIDIA GeForce RTX 3090 Ti # CPU : AMD Ryzen 9 8945HX with Radeon Graphics # RAM : 92 GB bench@llm-benchmark:~$ /root/llama.cpp/build/bin/llama-server \ -m /root/.cache/huggingface/hub/models--unsloth--gemma-4-E4B-it-GGUF/snapshots/bfc15c382204943c3a8fff0c750b94ae2364d7a3/gemma-4-E4B-it-Q4_K_M.gguf \ --alias gemma-4-E4B-it \ --host 0.0.0.0 \ --port 8000 \ -ngl 999 \ -c 16384 \ -np 4 \ --jinja
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.gemma-4-E4B-it
Kontextlaenge?Maximale Anzahl Tokens (Eingabe + erzeugte Ausgabe zusammen), die das Modell pro Anfrage verarbeiten kann.16384
Modellpfad?Pfad zur GGUF-Modelldatei, die geladen und ausgeliefert wird./root/.cache/huggingface/hub/models--unsloth--gemma-4-E4B-it-GGUF/snapshots/bfc15c382204943c3a8fff0c750b94ae2364d7a3/gemma-4-E4B-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
Kontext?Groesse des Kontextfensters in Token. 0 = der beim Training verwendete Kontext des Modells.16384
np4

All benchmarks of this model To leaderboard

Model comparison

gemma-4-E4B-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

1.2101.1221.0339448565.6547.1668.67710.189Prefill (tok/s)Generation (tok/s)NVIDIA GeForce RTX 5070 Ti - 1.082,4 tok/s Generation, 9.183 tok/s Prefill, TTFT 2.840 ms (6 Laufe)NVIDIA GeForce RTX 50...NVIDIA GeForce RTX 3090 Ti - 983,7 tok/s Generation, 6.659 tok/s Prefill, TTFT 4.857 ms (3 Laufe) | DIESER LAUF★ NVIDIA GeForce RTX 30...
NVIDIA GeForce RTX 5070 Ti 1.082,4 tok/s★ NVIDIA GeForce RTX 3090 Ti 983,7 tok/s this run

CPUby processor

1.2101.1221.0339448565.6547.1668.67710.189Prefill (tok/s)Generation (tok/s)AMD Ryzen Threadripper PRO 5975WX 32-Cores - 1.082,4 tok/s Generation, 9.183 tok/s Prefill, TTFT 2.840 ms (6 Laufe)AMD Ryzen Threadrippe...AMD Ryzen 9 8945HX with Radeon Graphics - 983,7 tok/s Generation, 6.659 tok/s Prefill, TTFT 4.857 ms (3 Laufe) | DIESER LAUF★ AMD Ryzen 9 8945HX wi...
AMD Ryzen Threadripper PRO 5975WX 32-Cores 1.082,4 tok/s★ AMD Ryzen 9 8945HX with Radeon Graphics 983,7 tok/s this run

MBby mainboard

1.2101.1221.0339448565.6547.1668.67710.189Prefill (tok/s)Generation (tok/s)ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI - 1.082,4 tok/s Generation, 9.183 tok/s Prefill, TTFT 2.840 ms (6 Laufe)ASUSTeK COMPUTER INC....Meigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) - 983,7 tok/s Generation, 6.659 tok/s Prefill, TTFT 4.857 ms (3 Laufe) | DIESER LAUF★ Meigao Innovation Tec...
ASUSTeK COMPUTER INC. Pro WS WRX80E-SAGE SE WIFI 1.082,4 tok/s★ Meigao Innovation Technology (Shen Zhen) Co., Ltd DRFXL (MotherBoard Series) 983,7 tok/s this run

ENGby engine

1.2031.1281.0539789033.8097.64111.47215.304Prefill (tok/s)Generation (tok/s)vLLM - 1.022,8 tok/s Generation, 13.200 tok/s Prefill, TTFT 1.096 ms (3 Laufe)vLLMllama.cpp - 1.082,4 tok/s Generation, 5.913 tok/s Prefill, TTFT 4.721 ms (6 Laufe) | DIESER LAUF★ llama.cpp
★ llama.cpp 1.082,4 tok/s this runvLLM 1.022,8 tok/s

DRVby driver

1.1911.1371.0821.0289747.8418.1758.5098.842Prefill (tok/s)Generation (tok/s)unbekannt - 1.082,4 tok/s Generation, 8.342 tok/s Prefill, TTFT 3.513 ms (9 Laufe)unbekannt
unbekannt 1.082,4 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 50 W
⚡ TDP 477 W
☁️ External LLM (API)
Electricity0.30 EUR/kWh
Avg power (incl. idle)477 W estimated (TDP)GPU 450 + CPU 17 + Board 10 W full load
Avg cost / hourEUR 0.14
Electricity / 1M tokensEUR 0.26
Token / kWh1.18M
Acquisition (system)EUR 2,986 partial priceGPU EUR 999 · CPU EUR 549 · RAM EUR 1,288 · PSU EUR 150
Electricity (2 years)–
TCO (2 years)EUR 5,494
Output tokens (2 years)9.83B
☁️ 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 (50 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

NVIDIA GeForce RTX 3090 Ti · this run 156 tok/sNVIDIA GeForce RTX 5070 Ti 165 tok/sNVIDIA GeForce RTX 5070 Ti 145 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.