How much VRAM does Gemma 4 E2B need?

Gemma 4 E2B requires approximately 0.0 GB VRAM at Q4_K_M quantization with 1K context and 10% overhead. At Q8_0 it needs ~0.0 GB, and at FP16 ~0.0 GB. Use the PCPARTGUIDE VRAM Calculator for exact requirements at any context length and overhead setting.

Can I run Gemma 4 E2B on a consumer GPU?

Yes, Gemma 4 E2B can run on consumer GPUs. At Q4_K_M quantization with moderate context length, it fits on a 12 GB GPU. For best performance, use a 12 GB or larger GPU with at least 8 GB VRAM for reasonable context lengths.

What is Gemma 4 E2B's context window?

Gemma 4 E2B supports a maximum context window of 131,072 tokens. The default context is 8,192 tokens. Larger contexts require proportionally more VRAM for the KV cache — use the VRAM Calculator to estimate exact requirements at your target context length.

What quantization level should I use for Gemma 4 E2B?

For Gemma 4 E2B, Q4_K_M is the recommended quantization — it provides an excellent quality-to-VRAM ratio at ~0.0 GB. Q8_0 offers slightly better quality at ~2× the VRAM. Q4_K_M is the sweet spot for most users.

How does Gemma 4 E2B compare to similar models?

Gemma 4 E2B is a 5.1B Dense model from the Gemma family. Browse other Gemma models on the PCPARTGUIDE directory for comparable alternatives with different parameter counts and VRAM requirements. Use the Model Transparency scores to assess documentation quality and reproducibility.

GemmaDenseGemma License

Gemma 4 E2B

Gemma 4 E2B is a dense transformer language model from the Gemma family, containing 5.1B parameters across 35 layers. It supports up to 131K tokens of context with a hidden dimension of 2560 and 4 KV heads for efficient grouped-query attent…

5.1B

Parameters

128K

Max Context

Dense

Architecture

—

Released

Text + Audio + Vision

Modality

About Gemma 4 E2B

General PurposeChat

Technical Specifications

Total Parameters5.1B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 2,560

Transformer Layers35

Attention Heads16

KV Headsn_kv = 4

Head Dimensiond_head = 128

Activation FunctionSwiGLU

NormalizationRMSNorm

Position EmbeddingRoPE

System Requirements

Estimated VRAM at 10% overhead for different quantization methods and context sizes.

Quantization	1K ctx	128K ctx
Q4_K_M0.50 B/W ~97% of FP16	2.70Consumer GPU	11.39Consumer GPU
Q8_01.00 B/W ~100% of FP16	5.34Consumer GPU	14.02Consumer GPU
F162.00 B/W Reference	10.61Consumer GPU	19.29Consumer GPU

Fits 24 GB consumer GPU

Fits 80 GB datacenter GPU

Requires cluster / multi-GPU

Model Transparency

Scores how well-documented and reproducible Gemma 4 E2B is across 12 provenance dimensions. Higher is more transparent.

Param Density72

Tokenizer68

Dataset59

Training63

Reproducibility66

Upstream73

Architecture75

Versioning69

Hardware80

Downstream70

License73

Identity73

85+ Transparent

70+ Adequate

<70 Opaque

Context

256K

3.8B active

Find the right GPU for Gemma 4 E2B

Use the interactive VRAM Calculator to see exactly how much memory you need at any quantization level, context length, and overhead setting.

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