How fast does Gemma 4 E4B run on the NVIDIA RTX 5090 Laptop?

Gemma 4 E4B runs at about 166 tokens/s on the NVIDIA RTX 5090 Laptop (measured, Q4_K_M, CUDA, llama-bench). Based on 1 measured run(s), each linked to its public source.

Does Gemma 4 E4B Q4_K_M fit in the NVIDIA RTX 5090 Laptop's memory?

Estimate: the Q4_K_M file is 5.0 GB and needs about 6.0 GB with an ~8K context; the NVIDIA RTX 5090 Laptop has about 23.5 GB usable, so it fits.

Gemma 4 E4B › NVIDIA RTX 5090 Laptop

Gemma 4 E4B on the NVIDIA RTX 5090 Laptop

Gemma 4 E4B runs at about 166 tokens/s on the NVIDIA RTX 5090 Laptop (measured, Q4_K_M, CUDA, llama-bench).

Best measured: 166 tokens/s generation and 9,556 tokens/s prompt processing (Q4_K_M, CUDA).

Every run

Quant	Backend	Generate tok/s	Prompt tok/s	Test	Build / by / notes	Source
Q4_K_M	CUDA	166	9,556	tg128	e3f542d8 Fazal Majid llama-bench --hf-repo unsloth/gemma-4-E4B-it-GGUF	Fazal Majid llama-bench log (golgotha)

tg128 = generating 128 tokens; pp512 = reading a 512-token prompt (llama-bench). LocalScore averages several prompt and output lengths, so its numbers run lower than tg128. "decode, llama-server" = single-stream generation speed measured against a running llama-server (no speculative decoding).

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