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Gemma 4 31B VRAM Requirements

Developed by Google

Find out exactly how much VRAM you need to run Gemma 4 31B locally. Calculate the memory footprint of different GGUF quantization variants (like Q4_K_M or Q8_0), estimate your context length KV cache VRAM footprint, and determine if your hardware supports a full GPU VRAM offload or if you will need to rely on slow partial CPU offloading to avoid a CUDA Out of Memory (OOM) error.

Hugging Face Repository

Hardware Configuration

Adjust settings to check compatibility with your system in real time.

GB VRAM

Available: 14.50 GB

GB RAM

Available: 29.00 GB

8,192 tokens
CPU Offloading
Compatibility Verdict
Fits with CPU Offload

KV Cache & Overhead (3.0 GB) plus 11.5 GB weights fit in GPU VRAM (14.5 GB VRAM used). Remaining 7.3 GB weights are offloaded to System RAM (10.3 GB RAM required with OS).

Requires 21.82 GB total memory (weights: 18.80 GB, context overhead: 1.41 GB, activation overhead: 1.62 GB).

Memory Margin+21.68 GB
CPU Offload Memory Partitioning
GPU Dedicated VRAM
Model Weights11.48 GB
KV Cache1.41 GB
Activation & Runtime Overhead1.62 GB
Total VRAM Required14.50 GB
System RAM
Offloaded Model Weights7.32 GB
Total RAM Required7.32 GB

Gemma 4 31B Quantization Formats & VRAM Compatibility

Select a format to set it as active and calculate your system fit dynamically.

QuantWeightsKV CacheOverheadTotal VRAMStatusLinks
Base (Unquantized)58.25 GB1.41 GB4.77 GB
64.43 GB
Too Large
HF weights
UD-Q4_K_XL18.80 GB1.41 GB1.62 GB
21.82 GB
CPU Offload
GGUF
UD-Q8_K_XL35.00 GB1.41 GB2.91 GB
39.32 GB
CPU Offload
GGUF

Gemma 4 31B KV Cache Memory Breakdown

How to Setup and Run Gemma 4 31B Locally

1

Method A: Ollama (Recommended)

Ollama is the easiest way to run models in the background. First, download it from ollama.com, then execute this terminal command:

ollama run <model-name>
2

Method B: LM Studio (GUI)

If you prefer a full graphical interface with chat UI and local server hosting:

  • Download and install LM Studio.
  • Search for Gemma 4 31B in the home page search tab.
  • Select a quantization level (like Q4_K_M) that fits your VRAM, click download, and load it to chat.

Model Specs

  • DeveloperGoogle
  • Parameter Count30.7B
  • Base File Size58.3 GB
  • AvailabilityLocal-Only
  • Input Modalities
    TextImageVideo

Standard Benchmark Scores

Coding (LiveCodeBench)80%
Reasoning (GPQA Diamond)84.3%

Commercial API Pricing

This model is self-hosted only or official API pricing is not available.

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