What Gemma 4 Model Can I Run with 12GB CUDA VRAM? Complete Guide

Apr 3, 2026

I fired up my RTX 3060 12GB, ready to run Google’s new Gemma 4 model. Downloaded the 31B variant, tried to load it, and boom—OOM error. Then I tried the 26B MoE. Another OOM. What’s going on?

I have 12GB of VRAM. That should be enough for most models, right? After hours of trial and error, I finally figured out which Gemma 4 models actually work on my hardware. Here’s what I learned.

The Core Problem: Not All VRAM Is Usable

When you see “12GB VRAM”, you think you have 12GB to work with. But here’s what actually happens:

Total VRAM:         12 GB
- OS overhead:       ~0.5 GB
- Display buffers:   ~0.3 GB
- Safety margin:     ~0.2 GB
──────────────────────────
Actually usable:     ~11 GB

And that’s before loading any model. Now let’s see what each Gemma 4 variant actually requires.

Gemma 4 Model Tiers: What They Need

Google released four Gemma 4 variants on April 3, 2026. Each targets different hardware:

┌──────────────┬────────────┬─────────────┬─────────────────┐
│ Model        │ Parameters │ VRAM (FP16) │ My 12GB Status  │
├──────────────┼────────────┼─────────────┼─────────────────┤
│ E2B          │ 2B         │ ~4 GB       │ ✓ Perfect fit   │
│ E4B          │ 4B         │ ~8 GB       │ ✓ Perfect fit   │
│ 26B MoE      │ 26B (3.8B) │ ~52 GB      │ ✗ Won't fit     │
│ 31B Dense    │ 31B        │ ~62 GB      │ ✗ Won't fit     │
└──────────────┴────────────┴─────────────┴─────────────────┘

Note: MoE only activates 3.8B params during inference,
      but still needs to load all 26B weights into memory.

The 26B and 31B numbers look impossible. But what about quantization? Can’t I just use 4-bit quantization to shrink them down?

The Quantization Trap: It’s Not Just About Weights

I thought 4-bit quantization would save me. I was wrong.

Here’s the math for the 31B model:

Model weights (Q4):     ~16 GB
KV cache (256K ctx):    ~20 GB
Activation buffers:     ~4 GB
─────────────────────────────
Total needed:           ~40 GB

My available VRAM:      ~11 GB
Result:                 OOM with a vengeance

The KV cache is the killer. A 256K context window requires massive memory for attention operations. Even with aggressive quantization, the 31B model needs 35GB+ at minimum.

I found this out the hard way:

$ ollama run gemma4:31b-q4
loading model...
CUDA out of memory error

What Actually Works: E2B and E4B

After my failures with the larger models, I tried the E-series models. These are Google’s “Efficient” variants, designed specifically for edge devices.

$ ollama run gemma4:e4b
pulling manifest
pulling 4b3a2c1d... 100% ▕██████████▏ 8.2 GB/8.2 GB
verifying sha256 digest
writing manifest
success

>>> Hello, how are you?
Hello! I'm doing well, thank you for asking. I'm ready to help you with...

The E4B loaded in seconds and ran at 12-15 tokens per second. Perfect.

Then I tried the E2B:

$ ollama run gemma4:e2b
>>> Explain quantum computing

Quantum computing harnesses the principles of quantum mechanics...
[Generated 256 tokens in 12.5 seconds]
Speed: ~20 tokens/s

Even faster—nearly 20 tokens per second.

Quantization Options: Squeezing More Performance

Both E2B and E4B support 4-bit quantization, which cuts memory usage in half:

┌─────────┬──────────┬──────────────┬─────────────────┐
│ Model   │ Quant    │ VRAM Usage   │ Speed (tok/s)   │
├─────────┼──────────┼──────────────┼─────────────────┤
│ E2B     │ FP16     │ ~4 GB        │ 18-22           │
│ E2B     │ Q4_K_M   │ ~1 GB        │ 25-30           │
│ E4B     │ FP16     │ ~8 GB        │ 12-15           │
│ E4B     │ Q4_K_M   │ ~2.5 GB      │ 18-22           │
└─────────┴──────────┴──────────────┴─────────────────┘

To use quantized models with llama.cpp:

# Download E4B Q4_K_M quantization
wget https://huggingface.co/google/gemma-4-e4b-gguf/resolve/main/gemma-4-e4b-q4_k_m.gguf

# Run with full GPU offload
./llama-cli -m gemma-4-e4b-q4_k_m.gguf -ngl 99 -c 8192 --temp 0.7

The -ngl 99 flag offloads all 99 layers to GPU. Without it, some layers run on CPU and performance tanks.

The 26B MoE Edge Case: Can You Run It?

Technically, yes. But it’s risky.

The 26B MoE (Mixture of Experts) model only activates 3.8B parameters during inference, making it fast. But all 26B parameters must still be loaded into memory.

With Q4 quantization:

Model weights (Q4):     ~13-15 GB
KV cache (4K ctx):      ~2 GB
─────────────────────────────
Total needed:           ~15-17 GB

My VRAM:                ~11 GB
Gap:                    Need 4-6 GB more

I tried it anyway:

$ ollama run gemma4:26b-moe-q4
loading model...
[Warning] GPU memory insufficient, using CPU fallback
Speed: 3-5 tokens/s (painfully slow due to CPU)

It loaded, but performance was terrible because the CPU handled most of the work. Not worth it.

If you have 16GB VRAM (RTX 4080, RTX 3090), the 26B MoE becomes viable:

Model weights (Q4):     ~13 GB
KV cache:               ~2 GB
─────────────────────────────
Total needed:           ~15 GB

Available VRAM:         ~15 GB
Result:                 Tight but workable

Why E-Series Models Work: Google’s Optimization Strategy

Google designed the E2B and E4B specifically for edge deployment. Here’s what makes them efficient:

1. Architecture Optimization

Fewer layers, wider hidden dimensions
Efficient attention mechanisms
Reduced KV cache footprint

2. Native Multimodal Support

Vision and audio capabilities built-in
No separate encoder models needed
Single model handles text, images, audio

3. 128K Context Window

Full 128K context support
Optimized attention for long sequences
Memory-efficient position encoding

I tested the multimodal capabilities:

>>> Describe this image: [uploaded photo of sunset]

This image shows a stunning sunset over what appears to be...
[Generated detailed description with colors, composition, and mood]

Works perfectly. And it runs entirely on my 12GB GPU.

Monitoring Your VRAM Usage

Before running any model, check your available VRAM:

$ nvidia-smi --query-gpu=memory.free --format=csv
memory.free [MiB]
11420 MiB

I have about 11.4 GB free. Good.

During inference, monitor in real-time:

$ watch -n 1 nvidia-smi

Every 1.0s: nvidia-smi

┌─────────────────────────────────────┐
│ GPU  Memory-Usage                  │
├─────────────────────────────────────┤
│   0  7824MiB / 12288MiB (64%)      │
└─────────────────────────────────────┘

The E4B at FP16 uses about 7.8 GB. Plenty of headroom.

Or in Python:

import torch

def print_memory():
    allocated = torch.cuda.memory_allocated() / 1024**3
    reserved = torch.cuda.memory_reserved() / 1024**3
    print(f"VRAM allocated: {allocated:.2f} GB")
    print(f"VRAM reserved: {reserved:.2f} GB")

# Load model
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-4-e4b",
    torch_dtype=torch.float16,
    device_map="auto"
)
print_memory()
# Output:
# VRAM allocated: 7.82 GB
# VRAM reserved: 8.00 GB

Common Mistakes I Made

Mistake 1: Ignoring the KV Cache

I thought model weights were the only memory cost. Wrong. The KV cache for attention operations grows with context length:

Context 4K:    ~0.5 GB
Context 32K:   ~4 GB
Context 128K:  ~16 GB (for larger models)

Mistake 2: Assuming MoE = Lower Memory

The 26B MoE only activates 3.8B parameters during inference, but all 26B parameters must still be loaded. The “3.8B active” marketing confused me initially.

Mistake 3: Forcing GPU-Only Mode

When I tried -ngl 99 on the 26B MoE, it crashed. I should have used partial GPU offload:

# Offload only 40 layers to GPU, rest to CPU
./llama-cli -m gemma-4-26b-moe-q4_k_m.gguf -ngl 40 -c 4096

This works, but performance suffers. Better to stick with E-series models.

Mistake 4: Not Using Quantization-Aware Training (QAT) Models

Google released QAT-optimized variants that maintain quality at lower bitrates:

# QAT models have quantization baked into training
# Better quality than post-training quantization
./llama-cli -m gemma-4-e4b-it-qat-q4_0.gguf -ngl 99 -c 128000

The QAT versions provide near-original quality at 4-bit precision.

Running E-Series Models: Three Approaches

Option 1: Ollama (Easiest)

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Run E2B
ollama run gemma4:e2b

# Run E4B (recommended)
ollama run gemma4:e4b

Ollama handles quantization automatically. Zero configuration needed.

Option 2: llama.cpp (More Control)

# Clone and build
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && make

# Download E4B Q4_K_M
wget https://huggingface.co/google/gemma-4-e4b-gguf/resolve/main/gemma-4-e4b-q4_k_m.gguf

# Run with full GPU offload
./llama-cli -m gemma-4-e4b-q4_k_m.gguf -ngl 99 -c 8192 --temp 0.7

Option 3: Hugging Face Transformers (Python)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load E4B
model_name = "google/gemma-4-e4b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Generate
prompt = "Explain quantum computing in simple terms."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0]))

All three approaches work perfectly on 12GB VRAM.

Performance Expectations

On my RTX 3060 12GB:

┌─────────┬──────────┬──────────┬──────────┬─────────────┐
│ Model    │ Quant    │ Speed    │ Context  │ Multimodal? │
├─────────┼──────────┼──────────┼──────────┼─────────────┤
│ E2B      │ FP16     │ 18-22/s  │ 128K     │ Yes         │
│ E2B      │ Q4_K_M   │ 25-30/s  │ 128K     │ Yes         │
│ E4B      │ FP16     │ 12-15/s  │ 128K     │ Yes         │
│ E4B      │ Q4_K_M   │ 18-22/s  │ 128K     │ Yes         │
│ 26B MoE  │ Q4_K_M   │ 8-12/s*  │ 32K max  │ Partial     │
│ 31B      │ Any      │ N/A      │ N/A      │ OOM         │
└─────────┴──────────┴──────────┴──────────┴─────────────┘

* With CPU fallback, much slower

The E-series models deliver consistent, reliable performance. The 26B MoE technically loads but struggles with memory pressure.

The Bottom Line

If you have 12GB VRAM, here’s what you should do:

Use Gemma 4 E4B for the best balance of capability and speed
Use Gemma 4 E2B if you want maximum speed or need to run other processes
Don’t waste time on 26B MoE or 31B—they won’t fit, period

For 16GB+ VRAM owners:

26B MoE Q4 becomes viable with careful context management
31B still won’t fit—you need 24GB+ for that

For 24GB+ VRAM (RTX 4090, A5000):

31B Q4 works but expect 16GB+ VRAM usage
Full FP16 31B needs 48GB+ VRAM

Quick Start Command

Don’t overthink it. Just run:

ollama run gemma4:e4b

This gives you:

4B parameter model
Full multimodal capabilities (text, vision, audio)
128K context window
12-15 tokens/second
Zero configuration
Guaranteed to work on your 12GB GPU

If you need more power, upgrade your GPU. Software won’t solve hardware limitations.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!