Intelligent CLI tool with AI-powered model selection that analyzes your hardware and recommends optimal LLM models for your system
npm install ollama-checkerAI-powered CLI tool that analyzes your hardware and recommends optimal LLM models from 6900+ variants across 200+ Ollama models.
---
- 6900+ Model Variants - Complete Ollama library with all quantizations (Q2-Q8, FP16)
- Smart Scoring Engine - Multi-dimensional scoring: Quality, Speed, Fit, Context
- Hardware Detection - Apple Silicon, NVIDIA CUDA, AMD ROCm, Intel Arc, CPU
- Instant Search - SQLite-powered search across all models
- Zero Native Dependencies - Pure JavaScript, works with any Node.js version
---
``bash`
npm install -g ollama-checker
`bashDetect your hardware
ollama-checker hw-detect
---
Commands
$3
Analyzes your system and shows compatible backends:
bash
ollama-checker hw-detect
`Output:
=== Hardware Detection ===Summary:
Apple M4 Pro (24GB Unified Memory)
Tier: MEDIUM HIGH
Max model size: 15GB
Best backend: metal
CPU:
Apple M4 Pro
Cores: 12 (12 physical)
SIMD: NEON
METAL:
GPU Cores: 16
Unified Memory: 24GB
Memory Bandwidth: 273GB/s
$3
Gets the best models for your hardware:
bash
ollama-checker smart-recommend
ollama-checker smart-recommend --use-case coding
ollama-checker smart-recommend -l 10
`Output:
=== Top Recommendations ===Best Overall:
qwen2.5-coder:7b-base-q8_0
7B params | 7GB | Q8_0
Score: 100/100 (Q:99 S:100 F:100)
~58 tokens/sec
ollama pull qwen2.5-coder:7b-base-q8_0
Highest Quality:
qwen2.5-coder:14b-base-q6_K
14B | 10.5GB | Quality: 100/100
$3
Search with intelligent scoring:
bash
ollama-checker search llama -l 5
ollama-checker search coding --use-case coding
ollama-checker search qwen --quant Q4_K_M
`Options:
-
- Number of results (default: 10)
- -u, --use-case - Optimize for: general, coding, chat, reasoning, creative
- --max-size - Maximum model size
- --quant - Filter by quantization (Q4_K_M, Q8_0, etc.)
- --family - Filter by model family$3
Downloads latest models from Ollama:
`bash
ollama-checker sync
`---
Scoring System
Models are scored on 4 dimensions:
| Component | Description | Weight (General) |
|-----------|-------------|------------------|
| Q Quality | Model family + params + quantization | 40% |
| S Speed | Estimated tokens/sec on your hardware | 35% |
| F Fit | How well it fits in your memory | 15% |
| C Context | Context length capability | 10% |
$3
| Use Case | Quality | Speed | Fit | Context |
|----------|---------|-------|-----|---------|
| general | 40% | 35% | 15% | 10% |
| coding | 55% | 20% | 15% | 10% |
| reasoning | 60% | 15% | 10% | 15% |
| chat | 40% | 40% | 15% | 5% |
| fast | 25% | 55% | 15% | 5% |
---
Supported Hardware
$3
- M1, M1 Pro, M1 Max, M1 Ultra
- M2, M2 Pro, M2 Max, M2 Ultra
- M3, M3 Pro, M3 Max
- M4, M4 Pro, M4 Max$3
- RTX 40 Series (4090, 4080, 4070, etc.)
- RTX 30 Series (3090, 3080, 3070, etc.)
- Data Center (H100, A100, etc.)$3
- RX 7900 XTX, 7900 XT, 7800 XT
- RX 6900 XT, 6800 XT
- MI300, MI250$3
- Arc A770, A750
- Integrated Iris/UHD$3
- AVX-512 + AMX (Intel Sapphire Rapids+)
- AVX-512
- AVX2
- ARM NEON (Apple Silicon, ARM servers)---
Requirements
- Node.js 16+ (any version: 16, 18, 20, 22, 24...)
- Ollama installed for running models (https://ollama.ai)
---
How It Works
1. Hardware Detection - Detects GPU/CPU capabilities and available memory
2. Database Sync - Downloads model info from Ollama (cached locally in SQLite)
3. Scoring - Calculates multi-dimensional scores for each model variant
4. Recommendations - Returns models sorted by compatibility score
---
Examples
$3
bash
ollama-checker smart-recommend --use-case coding -l 3
`$3
bash
ollama-checker search "3b OR 7b" --max-size 5 -l 10
`$3
bash
ollama-checker search qwen -l 20
`---
Development
bash
git clone https://github.com/Pavelevich/ollama-checker.git
cd ollama-checker
npm install
node bin/enhanced_cli.js hw-detect
``---
MIT License - see LICENSE for details.
---
- GitHub: https://github.com/Pavelevich/ollama-checker
- npm: https://www.npmjs.com/package/ollama-checker
- Issues: https://github.com/Pavelevich/ollama-checker/issues