How to Run Gemini 3 Pro with Ollama for Free (2026)
Run Google's latest open-weight model locally on your hardware
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How to Run Gemini 3 Pro with Ollama for Free (2026)
Google made waves in the AI community by releasing open weights for Gemini 3 Pro, making it one of the most capable models available for local inference. Combined with Ollama, you can run Gemini 3 Pro on your own hardware entirely for free -- no API keys, no rate limits, no per-token costs, and complete data privacy.
This guide covers the complete process: hardware requirements, installation, configuration, optimization, and practical usage examples.
Why Run Gemini 3 Pro Locally?
Running a model locally instead of using a cloud API offers several concrete advantages:
- Zero cost: No per-token charges, no monthly subscriptions
- Complete privacy: Your data never leaves your machine
- No rate limits: Generate as many tokens as your hardware allows
- Offline access: Works without an internet connection after initial download
- Full control: Customize parameters, system prompts, and behavior
- Low latency: No network round-trips for each request
The trade-off is that you need capable hardware, and local inference is typically slower than cloud-hosted inference on high-end GPU clusters.
Hardware Requirements
Gemini 3 Pro comes in several quantization levels. Here is what you need for each:
| Quantization | Model Size | RAM Required | GPU VRAM Required | Quality Impact |
|---|---|---|---|---|
| Q2_K | ~5.5 GB | 8 GB | 6 GB | Noticeable degradation |
| Q4_K_M | ~9.5 GB | 12 GB | 10 GB | Minor quality loss, great balance |
| Q5_K_M | ~11 GB | 14 GB | 12 GB | Near-original quality |
| Q6_K | ~13 GB | 16 GB | 14 GB | Minimal quality loss |
| Q8_0 | ~17 GB | 20 GB | 18 GB | Virtually lossless |
| FP16 (full) | ~32 GB | 36 GB | 34 GB | Original quality |
Recommended setups:
| Hardware | Best Quantization | Expected Speed |
|---|---|---|
| MacBook Air M2 (16 GB) | Q4_K_M | ~15-20 tokens/sec |
| MacBook Pro M3 Pro (36 GB) | Q6_K or Q8_0 | ~25-35 tokens/sec |
| MacBook Pro M4 Max (64 GB) | FP16 | ~30-40 tokens/sec |
| RTX 4060 (8 GB) | Q2_K or Q4_K_M (partial) | ~20-30 tokens/sec |
| RTX 4070 Ti (12 GB) | Q4_K_M | ~35-45 tokens/sec |
| RTX 4090 (24 GB) | Q6_K | ~50-70 tokens/sec |
| RTX 5090 (32 GB) | Q8_0 or FP16 | ~60-80 tokens/sec |
Apple Silicon Macs are particularly good for local LLM inference because their unified memory architecture allows the GPU to access the full system RAM.
Step 1: Install Ollama
If you do not have Ollama installed yet:
macOS
brew install ollama
Linux
curl -fsSL https://ollama.com/install.sh | sh
Windows
Download the installer from ollama.com.
Verify your installation:
ollama --version
Step 2: Pull Gemini 3 Pro
Pull the model from the Ollama registry:
# Default quantization (Q4_K_M - recommended for most users)
ollama pull gemini3-pro
# Specific quantization variants
ollama pull gemini3-pro:q2_k # Smallest, fits 8 GB RAM
ollama pull gemini3-pro:q4_k_m # Best balance (recommended)
ollama pull gemini3-pro:q5_k_m # Higher quality
ollama pull gemini3-pro:q6_k # Near-original
ollama pull gemini3-pro:q8_0 # Highest quality quantized
The download will take several minutes depending on your internet connection and the selected quantization level.
Verify the Download
ollama list
You should see something like:
NAME ID SIZE MODIFIED
gemini3-pro:latest a1b2c3d4e5f6 9.5 GB 2 minutes ago
Step 3: Run Gemini 3 Pro
Interactive Chat
Start an interactive chat session:
ollama run gemini3-pro
You will get a prompt where you can type messages:
>>> Explain the difference between async/await and Promises in JavaScript.
In JavaScript, both Promises and async/await handle asynchronous operations,
but they differ in syntax and readability...
Type /bye to exit the chat.
One-Shot Prompt
For a single response without entering interactive mode:
ollama run gemini3-pro "Write a Python function to merge two sorted arrays in O(n) time."
API Access
Ollama serves an HTTP API on localhost:11434:
curl http://localhost:11434/api/generate -d '{
"model": "gemini3-pro",
"prompt": "Write a SQL query to find duplicate email addresses in a users table.",
"stream": false
}'
Step 4: Use Gemini 3 Pro in Your Code
Python (Direct API)
import requests
def ask_gemini(prompt: str, system: str = "") -> str:
response = requests.post(
"http://localhost:11434/api/chat",
json={
"model": "gemini3-pro",
"messages": [
{"role": "system", "content": system},
{"role": "user", "content": prompt}
],
"stream": False
}
)
return response.json()["message"]["content"]
# Example usage
result = ask_gemini(
prompt="Write a FastAPI endpoint for user registration with validation.",
system="You are a senior Python developer. Use type hints and Pydantic models."
)
print(result)
Python (OpenAI SDK - Compatible)
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:11434/v1",
api_key="ollama"
)
response = client.chat.completions.create(
model="gemini3-pro",
messages=[
{"role": "system", "content": "You are a helpful coding assistant."},
{"role": "user", "content": "Write a React hook for debounced search input."}
],
temperature=0.3
)
print(response.choices[0].message.content)
JavaScript / TypeScript
const response = await fetch("http://localhost:11434/api/chat", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({
model: "gemini3-pro",
messages: [
{ role: "system", content: "You are a TypeScript expert." },
{ role: "user", content: "Write a type-safe event emitter class." }
],
stream: false
})
});
const data = await response.json();
console.log(data.message.content);
Step 5: Create a Custom Modelfile
Customize Gemini 3 Pro's behavior for your specific use case:
# Save as Modelfile.gemini-dev
FROM gemini3-pro
SYSTEM """
You are a senior full-stack developer. You specialize in:
- TypeScript, React, Next.js for frontend
- Python, FastAPI for backend
- PostgreSQL for databases
- Docker and Kubernetes for deployment
Rules:
1. Always use TypeScript (never plain JavaScript)
2. Include error handling in all code
3. Add JSDoc or docstring comments
4. Follow SOLID principles
5. When suggesting architecture, explain trade-offs
"""
PARAMETER temperature 0.2
PARAMETER top_p 0.9
PARAMETER num_ctx 16384
PARAMETER repeat_penalty 1.1
Build and run:
ollama create gemini-dev -f Modelfile.gemini-dev
ollama run gemini-dev
Step 6: Performance Optimization
Increase Context Window
The default context window is 4096 tokens. For larger codebases:
# Set to 16K context
OLLAMA_NUM_CTX=16384 ollama run gemini3-pro
# Set to 32K context (requires more RAM)
OLLAMA_NUM_CTX=32768 ollama run gemini3-pro
GPU Layer Allocation
Control how many model layers run on GPU vs. CPU:
# Force all layers to GPU (requires sufficient VRAM)
OLLAMA_NUM_GPU=99 ollama run gemini3-pro
# Split: 20 layers on GPU, rest on CPU
OLLAMA_NUM_GPU=20 ollama run gemini3-pro
# CPU only
OLLAMA_NUM_GPU=0 ollama run gemini3-pro
Keep Model in Memory
Prevent Ollama from unloading the model between requests:
# Keep loaded for 1 hour
curl http://localhost:11434/api/generate -d '{
"model": "gemini3-pro",
"keep_alive": "1h"
}'
# Keep loaded indefinitely
curl http://localhost:11434/api/generate -d '{
"model": "gemini3-pro",
"keep_alive": -1
}'
Batch Size Tuning
For higher throughput on capable hardware:
OLLAMA_NUM_BATCH=512 ollama run gemini3-pro
Gemini 3 Pro vs. Other Local Models
How does Gemini 3 Pro compare to other models you can run locally with Ollama?
| Model | Parameters | HumanEval | MMLU | Speed (Q4, RTX 4090) | Best For |
|---|---|---|---|---|---|
| Gemini 3 Pro | 17B | 88.2 | 85.6 | ~50 tok/s | General purpose, coding |
| Llama 3.2 (8B) | 8B | 72.1 | 73.2 | ~80 tok/s | Fast tasks, lower resources |
| Llama 3.1 (70B) | 70B | 86.8 | 86.0 | ~15 tok/s | Maximum quality (needs 48GB+) |
| Mistral Large | 22B | 81.5 | 81.2 | ~40 tok/s | European language tasks |
| DeepSeek Coder V3 | 16B | 90.1 | 78.4 | ~45 tok/s | Pure coding tasks |
| Qwen 2.5 (14B) | 14B | 83.2 | 82.1 | ~50 tok/s | Multilingual, Chinese support |
| Gemma 2 (9B) | 9B | 75.8 | 78.5 | ~70 tok/s | Lightweight, Google ecosystem |
Gemini 3 Pro hits a strong balance: better quality than 7-9B models, faster than 70B models, and competitive benchmarks across both coding and general knowledge.
Troubleshooting
| Issue | Solution |
|---|---|
| "out of memory" error | Use a smaller quantization (Q2_K or Q4_K_M) or reduce context window |
| Slow generation | Ensure GPU is being used (ollama ps). Reduce num_ctx. |
| Model not found | Run ollama pull gemini3-pro to download |
| Garbled output | Try a higher quantization level (Q5_K_M or Q6_K) |
| High CPU usage even with GPU | Set OLLAMA_NUM_GPU=99 to force full GPU offloading |
Conclusion
Running Gemini 3 Pro locally with Ollama gives you access to one of the most capable AI models available, completely free of charge. The combination of Google's model quality with Ollama's ease of use makes local LLM inference genuinely practical in 2026, even on consumer hardware.
For workflows that go beyond text generation -- creating AI avatars, generating marketing videos, or producing voice content -- Hypereal AI offers affordable, pay-as-you-go media generation that pairs naturally with your local LLM setup. Handle text intelligence locally with Gemini 3 Pro and media generation through Hypereal AI's API for a cost-effective, full-stack AI workflow.