How much VRAM does Gemma 4 31B need?

Gemma 4 31B 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 31B on a consumer GPU?

Yes, Gemma 4 31B 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 31B's context window?

Gemma 4 31B 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 31B?

For Gemma 4 31B, 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 31B compare to similar models?

Gemma 4 31B is a 30.7B 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 31B

30.7B

Parameters

256K

Max Context

Dense

Architecture

—

Released

Text

Modality

About Gemma 4 31B

Gemma 4 31B is a dense transformer language model from the Gemma family, containing 30.7B parameters across 60 layers. It supports up to 262K tokens of context with a hidden dimension of 5632 and 8 KV heads for efficient grouped-query attention (GQA). Dense 31B. Hybrid local+global attn. Dual RoPE. TurboQuant 3-bit KV. 256K ctx. #3 open model on Arena.

General PurposeCode

Technical Specifications

Total Parameters30.7B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 5,632

Transformer Layers60

Attention Heads44

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	16.10Consumer GPU	61.64Datacenter GPU	75.87Datacenter GPU
Q8_01.00 B/W ~100% of FP16	31.97Datacenter GPU	77.51Datacenter GPU	91.74Cluster / Multi-GPU
F162.00 B/W Reference	63.71Datacenter GPU	109.2Cluster / Multi-GPU	123.5Cluster / 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