Ollama Optimizer

Optimize Ollama configuration based on system hardware analysis.

Workflow

Phase 1: System Detection

Run the detection script to gather hardware information:

python3 scripts/detect_system.py

Parse the JSON output to identify:

OS and version
CPU model and core count
Total RAM / unified memory
GPU type, VRAM, and driver version
Current Ollama installation and environment variables

Phase 2: Analyze and Recommend

Based on detected hardware, determine the optimization profile:

Hardware Tier Classification:

Tier	Criteria	Max Model	Key Optimizations
CPU-only	No GPU detected	3B	num_thread tuning, Q4_K_M quant
Low VRAM	<6GB VRAM	3B	Flash attention, KV cache q4_0
Entry	6-8GB VRAM	8B	Flash attention, KV cache q8_0
Prosumer	10-12GB VRAM	14B	Flash attention, full offload
Workstation	16-24GB VRAM	32B	Standard config, Q5_K_M option
High-end	48GB+ VRAM	70B+	Multiple models, Q5/Q6 quants

Apple Silicon Special Case:

Unified memory = shared CPU/GPU RAM
8GB Mac → treat as 6GB VRAM tier
16GB Mac → treat as 12GB VRAM tier
32GB+ Mac → treat as workstation tier

Phase 3: Generate Optimization Plan

Create a structured optimization guide with these sections:

1. System Overview

Present detected hardware specs and highlight constraints (e.g., "8GB unified memory limits to 7B models").

2. Dependency Assessment

List what's needed based on the platform:

macOS: Ollama only (Metal automatic)
Linux NVIDIA: Ollama + NVIDIA driver 450+
Linux AMD: Ollama + ROCm 5.0+
Windows: Ollama + NVIDIA driver 452+

3. Configuration Recommendations

Essential environment variables:

# Always recommended
export OLLAMA_FLASH_ATTENTION=1

# Memory-constrained systems (<12GB)
export OLLAMA_KV_CACHE_TYPE=q8_0  # or q4_0 for severe constraints

Model selection guidance:

Recommend specific models from ollama list output
Suggest appropriate quantization (Q4_K_M default, Q5_K_M if headroom exists)
Warn if current models exceed hardware capacity

Modelfile tuning (when needed):

PARAMETER num_gpu <layers>    # Partial offload for limited VRAM
PARAMETER num_thread <cores>  # CPU threads (physical cores, not hyperthreads)
PARAMETER num_ctx <size>      # Reduce context for memory savings

4. Execution Checklist

Provide copy-paste commands in order:

Set environment variables
Restart Ollama service
Pull recommended models
Test with ollama run <model> --verbose

5. Verification Commands

# Benchmark current performance
python3 scripts/benchmark_ollama.py --model <model>

# Check GPU memory usage (NVIDIA)
nvidia-smi

# Verify config is applied
ollama run <model> "test" --verbose 2>&1 | head -20

Reference Files

VRAM Requirements - Model sizing and quantization guide
Environment Variables - Complete env var reference
Platform-Specific Setup - OS-specific installation and configuration

Output Format

Generate an ollama-optimization-guide.md file in the current directory with:

# Ollama Optimization Guide

**Generated:** <timestamp>
**System:** <OS> | <CPU> | <RAM>GB RAM | <GPU>

## System Overview
<hardware summary and constraints>

## Current Configuration
<existing Ollama setup and env vars>

## Recommendations

### Environment Variables
<shell commands to set vars>

### Model Selection
<recommended models with rationale>

### Performance Tuning
<Modelfile adjustments if needed>

## Execution Checklist
- [ ] <step 1>
- [ ] <step 2>
...

## Verification
<benchmark commands and expected results>

## Rollback
<commands to revert changes if needed>

Quick Optimization Commands

For users who want immediate results without full analysis:

macOS (Apple Silicon):

export OLLAMA_FLASH_ATTENTION=1
export OLLAMA_KV_CACHE_TYPE=q8_0
ollama pull llama3.2:3b  # Safe for 8GB, fast

Linux/Windows with 8GB NVIDIA GPU:

export OLLAMA_FLASH_ATTENTION=1
export OLLAMA_KV_CACHE_TYPE=q8_0
ollama pull llama3.1:8b-instruct-q4_K_M

CPU-only systems:

export CUDA_VISIBLE_DEVICES=-1
ollama pull llama3.2:3b
# Create Modelfile with: PARAMETER num_thread 4

ollama-optimizer