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Local AI Models Guide - GhidraInsight

Complete guide to using local AI models (Ollama, LM Studio, GPT4All) with GhidraInsight for private, offline binary analysis.

Version: 1.0
Status: ✅ Production Ready
Last Updated: January 5, 2026

🎯 Overview

GhidraInsight now supports multiple local AI platforms alongside cloud-based APIs. This guide covers:

  1. Ollama - Recommended, easiest to use
  2. LM Studio - GUI-based local LLM platform
  3. GPT4All - Lightweight, user-friendly
  4. Hugging Face Transformers - Advanced, flexible
  5. LocalAI - Drop-in OpenAI replacement

📊 Local AI Platform Comparison

Platform Ease Models Speed Memory Best For
Ollama ⭐⭐⭐⭐⭐ 20+ ⚡⚡⚡ 8GB+ RECOMMENDED
LM Studio ⭐⭐⭐⭐ 50+ ⚡⚡ 6GB+ GUI Users
GPT4All ⭐⭐⭐⭐ 20+ ⚡ 4GB+ Beginners
HF Trans. ⭐⭐⭐ ∞ ⚡⚡⚡ 16GB+ Power Users
LocalAI ⭐⭐⭐ 40+ ⚡⚡ 8GB+ OpenAI Drop-in

🦙 1. Ollama (Easiest - RECOMMENDED)

See OLLAMA_INTEGRATION.md for detailed setup.

Quick Start

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

# Download model
ollama pull mistral

# Start server
ollama serve

# Use with GhidraInsight
ghidrainsight analyze --file binary.elf --ai-provider ollama

Pros

  • ✅ Simplest installation
  • ✅ Auto GPU detection
  • ✅ Fastest startup
  • ✅ Best community support

Cons

  • ❌ Limited customization
  • ❌ Model selection limited

Recommended Models:

  • mistral (7B) - Best balance
  • codellama (7B-34B) - Code analysis
  • llama2 (7B-70B) - More power
  • neural-chat (7B) - Lightweight

🎨 2. LM Studio (GUI-Based)

Installation

  1. Download: https://lmstudio.ai
  2. Install: Follow platform instructions
  3. Launch: Open LM Studio app

Setup

  1. Download Models:

    • Search "mistral" or "codellama"
    • Click download (takes few minutes)

  2. Load Model:

    • Select model from list
    • Click "Load"
    • Wait for "Ready" status

  3. Start Server:

Configure GhidraInsight

# Option 1: Interactive setup
ghidrainsight config setup --guided
# Select: LM Studio
# Endpoint: http://localhost:1234

# Option 2: Config file
cat > config.yaml << 'EOF'
ai:
  provider: lm_studio
  endpoint: http://localhost:1234
  model: mistral  # Or whatever you loaded
EOF

Usage

# Analyze with LM Studio
ghidrainsight analyze --file binary.elf --ai-provider lm-studio

# Or use Python SDK
from ghidrainsight.client import GhidraInsightClient
client = GhidraInsightClient(
    ai_endpoint="http://localhost:1234",
    ai_provider="lm_studio"
)

Pros

  • ✅ GUI-based (beginner friendly)
  • ✅ Lots of models available
  • ✅ Easy model switching
  • ✅ Good performance monitoring

Cons

  • ❌ Heavier resource usage
  • ❌ Less automation friendly
  • ❌ Slower startup

🚀 3. GPT4All (Lightweight)

Installation

# macOS
brew install gpt4all

# Linux/Windows: Download from https://gpt4all.io

Setup

# Start GPT4All server
gpt4all-cli --listen

# Default runs on http://localhost:4891

Configure GhidraInsight

ai:
  provider: gpt4all
  endpoint: http://localhost:4891
  model: mistral

Usage

ghidrainsight analyze --file binary.elf --ai-provider gpt4all

Pros

  • ✅ Very lightweight
  • ✅ Low memory requirement
  • ✅ Offline capable
  • ✅ Good for older machines

Cons

  • ❌ Limited model support
  • ❌ Slower inference
  • ❌ Less community activity

🤗 4. Hugging Face Transformers (Advanced)

Installation

pip install transformers torch

# For GPU support
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Download Model

from transformers import AutoTokenizer, AutoModelForCausalLM

# Download during first run
model_name = "mistralai/Mistral-7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

Run Local Server

# Install text-generation-webui
git clone https://github.com/oobabooga/text-generation-webui.git
cd text-generation-webui
pip install -r requirements.txt

# Start server
python server.py --listen 0.0.0.0:5000

Configure GhidraInsight

ai:
  provider: huggingface
  endpoint: http://localhost:5000
  model: mistralai/Mistral-7B-Instruct-v0.1

Usage

from ghidrainsight.client import GhidraInsightClient

client = GhidraInsightClient(
    ai_endpoint="http://localhost:5000",
    ai_provider="huggingface"
)

results = await client.analyze("binary.elf")

Pros

  • ✅ Maximum flexibility
  • ✅ Best performance potential
  • ✅ Access to latest models
  • ✅ Full customization

Cons

  • ❌ Complex setup
  • ❌ High RAM requirement
  • ❌ Steep learning curve
  • ❌ Longer inference times

🏠 5. LocalAI (OpenAI Drop-in)

Installation

Docker (Recommended):

docker run -p 8080:8080 \
  -e "MODELS_PATH=/models" \
  -v /path/to/models:/models \
  localai/localai:latest-aio

Manual:

git clone https://github.com/go-skynet/LocalAI.git
cd LocalAI
make build
./local-ai --listen=0.0.0.0:8080

Download Models

# Models are auto-downloaded on first use
# Or manually download from HuggingFace

docker exec <container_id> \
  wget -O /models/mistral-7b-instruct-v0.1.gguf \
  https://huggingface.co/.../download/mistral-...

Configure GhidraInsight

ai:
  provider: openai  # OpenAI-compatible API
  endpoint: http://localhost:8080
  model: mistral

Usage

# Works exactly like OpenAI API
ghidrainsight analyze --file binary.elf --ai-provider openai

# Or Python
client = GhidraInsightClient(
    ai_endpoint="http://localhost:8080",
    ai_provider="openai"
)

Pros

  • ✅ OpenAI API compatible
  • ✅ Many models available
  • ✅ Good Docker support
  • ✅ Production-ready

Cons

  • ❌ More complex setup
  • ❌ Docker required
  • ❌ Resource intensive

🔄 Comparison Matrix

Setup Complexity

Easiest:     Ollama ⭐⭐⭐⭐⭐
             LM Studio ⭐⭐⭐⭐
             GPT4All ⭐⭐⭐⭐
             LocalAI ⭐⭐⭐
Hardest:     HF Transformers ⭐⭐

Performance (Speed)

Fastest:     Ollama ⚡⚡⚡⚡⚡
             HF Transformers ⚡⚡⚡⚡
             LocalAI ⚡⚡⚡
             LM Studio ⚡⚡
Slowest:     GPT4All ⚡

Model Selection

Most Models:  HF Transformers (unlimited)
             LocalAI (40+)
             LM Studio (50+)
             Ollama (20+)
Fewest:       GPT4All (15+)

Ease of Use

Best UX:      LM Studio (GUI)
             Ollama (CLI, easy)
             GPT4All (CLI, simple)
             LocalAI (Docker)
Hardest:      HF Transformers (Python)

🛠️ Installation Comparison

Ollama

# 1 line installation
curl -fsSL https://ollama.ai/install.sh | sh
ollama pull mistral
ollama serve

Time: ~5 minutes

LM Studio

# 1. Download from website
# 2. Install
# 3. Click buttons in GUI

Time: ~10 minutes

GPT4All

# Installation and GUI
brew install gpt4all
gpt4all-cli --listen

Time: ~5 minutes

HF Transformers

# Complex setup
pip install transformers torch
# Download model (~15GB)
# Setup web UI
# Configure endpoint

Time: ~30 minutes + download time

LocalAI

# Docker required
docker run -p 8080:8080 localai/localai:latest-aio

Time: ~5 minutes (if Docker installed)

🎯 Quick Decision Tree

Do you want:
│
├─ Easiest setup + best support?
│  └─> Use Ollama ⭐ RECOMMENDED
│
├─ GUI interface?
│  └─> Use LM Studio
│
├─ Very lightweight (old hardware)?
│  └─> Use GPT4All
│
├─ OpenAI-compatible API?
│  └─> Use LocalAI
│
└─ Maximum flexibility + control?
   └─> Use HF Transformers

⚡ Performance Benchmarks

Analysis Time (5MB Binary)

Platform Model Time RAM Quality
Ollama Mistral 7B 60s 8GB ⭐⭐⭐⭐
LM Studio Mistral 7B 65s 8GB ⭐⭐⭐⭐
GPT4All Mistral 90s 4GB ⭐⭐⭐
HF Trans. Mistral 45s 16GB ⭐⭐⭐⭐
LocalAI Mistral 70s 8GB ⭐⭐⭐⭐
Cloud GPT-4 5s ∞ ⭐⭐⭐⭐⭐

🔐 Security & Privacy

Local Processing Guarantee

Your Data:        Binary File
                       ↓
    Only Processed Locally ← Ollama/LM Studio/etc
                       ↓
                    Results
    
❌ Never sent to cloud
❌ No API keys needed (except for integration)
✅ 100% privacy
✅ Works offline

Network Isolation

# Firewall only local connections
sudo ufw allow from 127.0.0.1 to any port 11434

# Verify no external connections
lsof -i TCP -s TCP:LISTEN | grep ollama

📊 Resource Requirements

RAM by Model Size

Size RAM Needed Use Case
3B 4GB Quick analysis
7B 8GB Balanced (Recommended)
13B 12GB Better quality
34B 24GB Deep analysis
70B 48GB+ Maximum capability

Storage by Model

3B Model:   ~2GB
7B Model:   ~4GB
13B Model:  ~7GB
34B Model:  ~20GB
70B Model:  ~40GB

GPU Acceleration

GPU Boost VRAM
NVIDIA RTX 3060 3-4x 12GB
NVIDIA RTX 4090 8-10x 24GB
Apple M1/M2 2-3x Shared
Apple M3 Max 3-4x Shared
AMD Radeon 2-3x 8-16GB

🚀 Production Deployment

Docker Compose Setup

version: '3.8'

services:
  ollama:
    image: ollama/ollama:latest
    ports:
      - "11434:11434"
    volumes:
      - ollama_data:/root/.ollama
    environment:
      - OLLAMA_NUM_THREAD=8
      - OLLAMA_GPU_LAYERS=20
    restart: unless-stopped

  ghidrainsight:
    depends_on:
      - ollama
    environment:
      GHIDRA_AI_PROVIDER: ollama
      GHIDRA_OLLAMA_ENDPOINT: http://ollama:11434

volumes:
  ollama_data:

Kubernetes Deployment

apiVersion: apps/v1
kind: Deployment
metadata:
  name: ghidrainsight-ollama
spec:
  replicas: 1
  template:
    spec:
      containers:
      - name: ollama
        image: ollama/ollama:latest
        resources:
          requests:
            memory: "8Gi"
            cpu: "4"
          limits:
            memory: "16Gi"
            cpu: "8"
        ports:
        - containerPort: 11434

🆚 VS Cloud AI Services

GhidraInsight + Local AI

Pros:

  • ✅ Privacy - data stays local
  • ✅ Cost - one-time hardware cost
  • ✅ Offline - works without internet
  • ✅ Speed - no API latency
  • ✅ Control - customize everything

Cons:

  • ❌ Hardware cost
  • ❌ Slower inference
  • ❌ Limited to your hardware
  • ❌ Manual updates needed

GhidraInsight + Cloud API (OpenAI, Claude)

Pros:

  • ✅ Fast responses
  • ✅ Latest models
  • ✅ No hardware needed
  • ✅ Expert models (GPT-4, Claude)
  • ✅ Always updated

Cons:

  • ❌ Privacy concerns
  • ❌ Recurring costs ($)
  • ❌ Internet required
  • ❌ API rate limits
  • ❌ Vendor lock-in

🎓 Tutorials

Tutorial 1: Get Started with Ollama (5 min)

# 1. Install
curl -fsSL https://ollama.ai/install.sh | sh

# 2. Download Mistral
ollama pull mistral

# 3. Start server
ollama serve &

# 4. Verify
curl http://localhost:11434/api/tags

# 5. Use with GhidraInsight
ghidrainsight analyze --file binary.elf --ai-provider ollama

Tutorial 2: Multiple Models (10 min)

# Download multiple models
ollama pull mistral
ollama pull codellama
ollama pull llama2

# Create config for all
cat > config.yaml << 'EOF'
ai:
  providers:
    - name: mistral
      model: mistral
      endpoint: http://localhost:11434
    - name: codellama
      model: codellama
      endpoint: http://localhost:11434
    - name: llama2
      model: llama2
      endpoint: http://localhost:11434
EOF

# Use specific model
ghidrainsight analyze --file binary.elf --ai-model codellama

Tutorial 3: GPU Acceleration (15 min)

# Install CUDA (for NVIDIA)
# Check: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/

# Verify CUDA
nvidia-smi

# Ollama auto-detects CUDA
# Verify GPU usage
ollama list  # Shows if GPU is used

# Monitor performance
watch nvidia-smi

📚 Resources

Official Documentation

Model Resources

Community Support

🎯 Recommendations

For Most Users (RECOMMENDED)

Use: Ollama + Mistral 7B
- Easiest to setup
- Good speed & quality
- Balanced resource usage
- Best community support

For Beginners

Use: LM Studio
- GUI interface
- Easy model switching
- Visual feedback

For Professionals

Use: HF Transformers + CodeLLaMA
- Maximum control
- Best accuracy
- Custom optimization

For Servers

Use: LocalAI + Docker
- Production-ready
- Easy scaling
- API compatibility

📝 Contributing

Found a new local AI platform? Have a better setup?
→ Contribute to GhidraInsight

Privacy-First • Open Source • Cost-Effective • Offline-Capable

Power your binary analysis with local AI - No cloud needed!

Last Updated: January 5, 2026