Ollama Optimizer

Ollama Optimizer

Optimize Ollama configuration based on system hardware analysis.

When to Use

Use this skill when the user asks to optimize Ollama, configure Ollama, speed up Ollama, fix Ollama running slow, set up a local LLM, tune inference speed, reduce memory usage, or select models that fit their GPU/RAM. The skill analyzes hardware (GPU, VRAM, RAM, CPU) and produces tailored recommendations.

Do not use for LM Studio, llama.cpp, vLLM, or hosted-API LLM providers (OpenAI, Anthropic) — those use different runtimes and tuning surfaces.

Workflow

Fast path (opt-in only): only skip full hardware analysis if the user explicitly asks to. Otherwise always run Phases 1-4 and follow the tier-based recommendation — do not apply shortcuts by default, and do not let them override a tier decision already made. For the per-platform shortcut commands and env vars, see Platform-Specific Setup and Environment Variables.

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
• hardware_tier — the script's computed category, max_model_size, and recommended_quant

Phase 2: Analyze and Recommend

Use hardware_tier from Phase 1 as the tier decision. Do not re-derive it; the table below explains what each tier means and which optimizations it implies. Override the script only with an explicit reason (e.g. VRAM shared with a display), and state that reason in the report.

Hardware Tier Classification:

Apple Silicon Special Case:

• Unified memory = shared CPU/GPU RAM; the script tiers it directly from total unified memory
• 8GB Mac → entry
• 16GB Mac → prosumer
• 32GB Mac → workstation; 64GB+ Mac → high_end

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 8B 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:

1. Back up the shell init file the user actually uses ($SHELL decides: ~/.zshrc, ~/.bashrc, or ~/.bash_profile) and append the env vars:

   RC=~/.zshrc  # or ~/.bashrc / ~/.bash_profile, matching $SHELL
   cp "$RC" "$RC.ollama-bak"
   printf '\n# ollama-optimizer start\nexport OLLAMA_FLASH_ATTENTION=1\n<KV cache + other export lines from section 3, per tier>\n# ollama-optimizer end\n' >> "$RC"
   

2. Restart Ollama service
3. Pull recommended models
4. Test with ollama run <model> --verbose
5. Rollback (one command, same file as step 1): cp ~/.zshrc.ollama-bak ~/.zshrc — then restart Ollama.

Phase 4: Verification

# Benchmark current performance
python3 scripts/benchmark_ollama.py --model <model>
# Expected output: tokens/s and generation latency — record as the post-tuning baseline.

# Check GPU memory usage (NVIDIA)
nvidia-smi

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

Acceptance Criteria

A run passes when all of the following are true:

• Hardware tier (CPU-only / Low-VRAM / Entry / Prosumer / Workstation / High-end) is identified explicitly in the report.
• Recommended model size fits within detected VRAM/unified-memory budget (no recommending a 14B model on an 8GB Mac).
• Required Ollama environment variables (e.g., OLLAMA_FLASH_ATTENTION, KV-cache quantisation) are written to a shell init file the user actually uses, with a backup of the prior file.
• Apple Silicon special case is applied when detected — unified memory is not double-counted as separate VRAM + RAM.
• Verification step runs ollama run <model> with --verbose and captures the actual offload/cache numbers.
• Rollback instructions are included so the user can revert all env changes with one command.

Step Completion Reports

After completing each major step, output a status report in this format:

◆ [Step Name] ([step N of M] — [context])
··································································
  [Check 1]:          √ pass
  [Check 2]:          √ pass (note if relevant)
  [Check 3]:          × fail — [reason]
  [Check 4]:          √ pass
  [Criteria]:         √ N/M met
  ____________________________
  Result:             PASS | FAIL | PARTIAL

Adapt the check names to match what the step actually validates. Use √ for pass, × for fail, and — to add brief context. The "Criteria" line summarizes how many acceptance criteria were met. The "Result" line gives the overall verdict.

Detection (step 1 of 4)

◆ Detection (step 1 of 4 — hardware profiling)
··································································
  Hardware detected:      √ pass — macOS 14, Apple M2
  GPU identified:         √ pass — Apple Metal (unified memory)
  RAM measured:           √ pass — 16GB unified memory
  [Criteria]:             √ 3/3 met
  ____________________________
  Result:                 PASS

Analysis (step 2 of 4)

◆ Analysis (step 2 of 4 — profile selection)
··································································
  Tier classified:        √ pass — Prosumer (16GB unified)
  Profile selected:       √ pass — Flash attention, full offload
  Bottlenecks identified: √ pass — memory bandwidth primary constraint
  [Criteria]:             √ 3/3 met
  ____________________________
  Result:                 PASS

Plan (step 3 of 4)

◆ Plan (step 3 of 4 — optimization guide)
··································································
  Guide generated:        √ pass — ollama-optimization-guide.md written
  Parameters tuned:       √ pass — OLLAMA_FLASH_ATTENTION=1, KV_CACHE_TYPE=q8_0
  Model recommendations ready: √ pass — llama3.1:14b-instruct-q4_K_M suggested
  [Criteria]:             √ 3/3 met
  ____________________________
  Result:                 PASS

Verification (step 4 of 4)

◆ Verification (step 4 of 4 — config validation)
··································································
  Benchmark commands listed: √ pass — python3 scripts/benchmark_ollama.py
  Config verified:        √ pass — ollama run --verbose output checked
  [Criteria]:             √ 2/2 met
  ____________________________
  Result:                 PASS

Reference Files

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

Expected Output

Generate an ollama-optimization-guide.md file. Ask the user where to save it (suggest ~/.config/ollama/optimization-guide.md or current directory). Contents:

# 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>