How much VRAM does Gemma 4 26B-A4B (MoE) need?

Gemma 4 26B-A4B (MoE) 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 26B-A4B (MoE) on a consumer GPU?

Yes, Gemma 4 26B-A4B (MoE) can run on consumer GPUs. At Q4_K_M quantization with moderate context length, it fits on a 24 GB GPU. For best performance, use a 24 GB or larger GPU with at least 8 GB VRAM for reasonable context lengths.

What is Gemma 4 26B-A4B (MoE)'s context window?

Gemma 4 26B-A4B (MoE) supports a maximum context window of 262,144 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 26B-A4B (MoE)?

For Gemma 4 26B-A4B (MoE), start with Q4_K_M (~0.0 GB) if VRAM-limited. Q8_0 offers better quality at ~2× the VRAM. If you have enough VRAM, Q6_K (~1.5× Q4 VRAM) provides a good balance. For models this large, Q3_K_M may be necessary to fit on consumer hardware.

How does Gemma 4 26B-A4B (MoE) compare to similar models?

Gemma 4 26B-A4B (MoE) is a 25.2B MoE 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.

GemmaMoEGemma License

Gemma 4 26B-A4B (MoE)

25.2B

Parameters

3.8B

Active

256K

Max Context

MoE

Architecture

—

Released

Text

Modality

About Gemma 4 26B-A4B (MoE)

Gemma 4 26B-A4B (MoE) is a mixture-of-experts (MoE) transformer language model from the Gemma family, containing 25.2B parameters across 30 layers. It has 25.2B total parameters loaded into VRAM with 3.8B active per token. It supports up to 262K tokens of context with a hidden dimension of 4096 and 8 KV heads for efficient grouped-query attention (GQA). MoE: 128 experts, 8 active + 1 shared. Sliding window 1K. 256K ctx.

General PurposeCode

Technical Specifications

Total Parameters25.2B

Active Parameters3.8B per token

ArchitectureMixture of Experts

Total Experts3.8

Attention TypeGQA (MoE)

Hidden Dimensiond = 4,096

Transformer Layers30

Attention Heads32

KV Headsn_kv = 8

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	195K ctx	256K ctx
Q4_K_M0.50 B/W ~97% of FP16	13.14Consumer GPU	35.91Datacenter GPU	43.03Datacenter GPU
Q8_01.00 B/W ~100% of FP16	26.17Datacenter GPU	48.94Datacenter GPU	56.05Datacenter GPU
F162.00 B/W Reference	52.22Datacenter GPU	74.99Datacenter GPU	82.10Cluster / Multi-GPU

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

Open VRAM Calculator Best GPU Guide VRAM Guide