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Gemma 4 E2B

Gemma 4 E2B needs roughly 1.7 GB VRAM at Q4_K_M quantization (4.9 GB at FP16). 106 GPUs we track can run it fully in VRAM at 8k context.

106 GPUs run this natively · 1 with CPU offload

Google2B params125k contextApache 2.0Commercial use ok

Gemma 4 E2B is a 2B parameter dense large language model developed by Google. Released in April 2026, it supports text and vision and audio inputs with a 128K context window, released under the Apache 2.0 license, allowing commercial use.

To run Gemma 4 E2B locally, you need approximately 1.7 GB of VRAM at Q4_K_M quantization with 8k context. 106 of the GPUs we track can run it fully in VRAM, with a further 1 able to offload to system RAM. At Q4_K_M it needs just 1.7 GB, making it accessible even on mid-range 16 GB cards like RTX 4080 and RTX 4070 Ti Super. At Q8_K_M (2.7 GB), you get near-FP16 quality while still fitting on 8, 12, 16, 24, 48 and 80 GB GPUs. FP16 requires 4.9 GB, limiting it to 48 and 80 GB GPUs.

With an MMLU-Pro score of 60, it delivers strong general reasoning for local deployment. The license allows commercial use.

VRAM at each quantization

Assumes 8k context. KV cache grows linearly with context length.

QuantWeightsKV cacheTotal
FP328.0 GB0.40 GB9.4 GB
BF164.0 GB0.40 GB4.9 GB
FP164.0 GB0.40 GB4.9 GB
Q8_0rec2.0 GB0.40 GB2.7 GB
Q6_K1.6 GB0.40 GB2.3 GB
Q5_K_M1.3 GB0.40 GB1.9 GB
Q4_K_M1.1 GB0.40 GB1.7 GB
Q3_K_M0.9 GB0.40 GB1.4 GB
Q2_K0.7 GB0.40 GB1.2 GB
NVFP4cuda1.0 GB0.40 GB1.6 GB

KV cache shown at 8k context (FP16). NVFP4 requires a CUDA GPU. Enable TurboQuant in the calculator to see reduced KV cache estimates.

Benchmarks

MMLU-Pro
60

GPUs that run Gemma 4 E2B natively (106)

Plus 1 GPUs that run it with CPU offload (slower)
Hugging Face ↗Ollama ↗Released 2026-04-02

Frequently asked questions

What are the VRAM requirements for Gemma 4 E2B?
Gemma 4 E2B requires approximately 1.7 GB of VRAM at Q4_K_M quantization, 2.7 GB at Q8, and 4.9 GB at FP16. These numbers assume 8k context window; VRAM scales linearly with context length due to the KV cache.
How many parameters does Gemma 4 E2B have?
Gemma 4 E2B has 2 billion parameters.
How capable is Gemma 4 E2B?
With an MMLU-Pro score of 60, Gemma 4 E2B delivers solid general-purpose performance suitable for most everyday tasks and professional use.
Can Gemma 4 E2B run on a 16 GB GPU?
Yes. Gemma 4 E2B needs 1.7 GB at Q4_K_M, which fits in a 16 GB GPU like the RTX 4080 or RTX 4070 Ti Super.
What is the smallest quantization for Gemma 4 E2B that fits in 24 GB of VRAM?
At FP32, Gemma 4 E2B needs 9.4 GB — the highest-quality quantization that fits in 24 GB of VRAM.
What GPU do I need to run Gemma 4 E2B locally?
A 16 GB GPU is enough. At Q4_K_M, Gemma 4 E2B needs 1.7 GB VRAM. Good options: RTX 4080 (16 GB), RTX 4070 Ti Super (16 GB).