How much VRAM does Gemma 3 27B need?

Gemma 3 27B 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 3 27B on a consumer GPU?

Yes, Gemma 3 27B 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 3 27B's context window?

Gemma 3 27B 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 3 27B?

For Gemma 3 27B, 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 3 27B compare to similar models?

Gemma 3 27B is a 27.0B 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 (Apache 2.0 compatible)

Gemma 3 27B

27.0B

Parameters

128K

Max Context

Dense

Architecture

Mar 12, 2025

Released

Text + Vision

Modality

About Gemma 3 27B

Gemma 3 27B is the largest Gemma 3 model — a dense 27B with vision support, 128K context, and strong multilingual coverage. At Q4_K_M it needs ~15 GB VRAM, fitting on 16 GB GPUs with limited context or 24 GB with room to spare. The GeGLU activation and unique head_dim=176 give it a distinctive performance profile. Strong across general knowledge, coding, and multilingual tasks. A solid alternative to Qwen 2.5 32B for users who prefer Google's training methodology.

General PurposeMultilingualVisionCode

Technical Specifications

Total Parameters27.0B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 5,632

Transformer Layers64

Attention Heads32

KV Headsn_kv = 8

Head Dimensiond_head = 176

Activation FunctionGeGLU

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	14.30Consumer GPU	57.96Datacenter GPU
Q8_01.00 B/W ~100% of FP16	28.26Datacenter GPU	71.91Datacenter GPU
F162.00 B/W Reference	56.17Datacenter GPU	99.82Cluster / Multi-GPU

Params

25.2B

Layers

Context

256K

3.8B active

Find the right GPU for Gemma 3 27B

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