Card × model benchmark

Gemma 2 2B on RTX 4070 Ti Q4_K_M

Smooth

On an RTX 4070 Ti (12 GB), Gemma 2 2B in Q4_K_M generates 202 tokens/s and reads the prompt at 15 576 tokens/s, using 2,5 Go of video memory.

Median of 1 run · latest on 25/09/26

202 tok/s

Generation

15 576 tok/s

Prompt processing

2,5 Go

VRAM used (peak)

104 W

Average power

1,95 tok/s/W

Efficiency

0,04 €/M tok

Electricity / M tokens · 0,25 €/kWh

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RTX 4070 Ti page All cards on Gemma 2 2B Feel the speed Rent in the cloud

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What about a longer context?

An estimate, not a measurement: the KV cache grows with context, takes VRAM and slows generation down.

Context	KV cache	Estimated VRAM	Fits in 12 GB?	Estimated speed
4 096	0,2 Go	2,7 Go	yes	≈ 184 tok/s
8 192	0,4 Go	2,9 Go	yes	≈ 166 tok/s

KV cache per token = 2 × 13 layers × 4 KV heads × 256 × 2 bytes (f16). Estimated VRAM = measured VRAM + added KV cache. Speed ≈ measured speed × weights ÷ (weights + added KV cache) — attention compute is not modelled, the real drop is slightly larger. Fine-tune it in the “Configurator”

Other models measured on the RTX 4070 Ti

Gemma 3 270M Q4_K_M 728 tok/s LFM2.5 1.2B Q4_K_M 473 tok/s Llama 3.2 1B Q4_K_M 442 tok/s R1 Distill 1.5B Qwen Q4_K_M 328 tok/s Llama 3.2 3B Instruct Q4_K_M 189 tok/s Ministral 3 3B Q4_K_M 173 tok/s Granite 4.1 3B Q4_K_M 166 tok/s Phi-4 mini 3.8B Q4_K_M 159 tok/s Gemma 3n E2B Q4_K_M 163 tok/s Gemma 4 E2B Q4_K_M 198 tok/s Gemma 3n E4B Q4_K_M 107 tok/s Mistral 7B 7B Q4_K_M 98,1 tok/s Qwen2.5 Coder 7B Q4_K_M 96,7 tok/s R1 Distill 7B Qwen Q4_K_M 97,6 tok/s Qwen2.5 7B Instruct Q4_K_M 98,8 tok/s Meta Llama 3.1 8B Instruct Q4_K_M 91,9 tok/s R1 0528 (Qwen3) 8B Q4_K_M 90,1 tok/s Ministral 3 8B Q4_K_M 84,0 tok/s Gemma 4 E4B Q4_K_M 114 tok/s Gemma 2 9B Q4_K_M 72,1 tok/s Gemma 4 12B Q4_K_M 53,8 tok/s Ministral 3 14B Q4_K_M 53,8 tok/s DeepSeek R1 Distill Qwen 14B Q4_K_M 51,1 tok/s Qwen2.5 Coder 14B Q4_K_M 50,3 tok/s Qwen2.5 14B Instruct Q4_K_M 50,5 tok/s Phi-4 Q4_K_M 50,8 tok/s Coder V2 Lite 16B-A2.4B Q4_K_M 189 tok/s

All measurements

Date	Source	Type	Generation	Prompt	VRAM	Max temp.	Power	llama.cpp
25/09/26	SephiGame	context 8 192ASUS ROG Strix	157	11 828	3,5 Go	57 °C	129 W	llama.cpp 96278e39f
25/09/26	SephiGame	standardASUS ROG Strix	202	15 576	2,5 Go	55 °C	104 W	llama.cpp 96278e39f

Protocol: llama-bench (llama.cpp), prompt 512 + generation 128 tokens, model fully loaded in VRAM. VRAM used = system peak (includes the OS, ~0.5 to 1 GB more than the model alone). Measure my card