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Intelligent CLI tool with AI-powered model selection that analyzes your hardware and recommends optimal LLM models for your system

Package Exports

  • ollama-checker
  • ollama-checker/src/index.js

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (ollama-checker) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

LLM Checker - Intelligent Ollama Model Selector

AI-powered CLI tool that analyzes your hardware and recommends optimal LLM models from 6900+ variants across 200+ Ollama models.

npm version License: MIT Node.js Version


Features

  • 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

Quick Start

Installation

npm install -g ollama-checker

Basic Usage

# Detect your hardware
ollama-checker hw-detect

# Get smart recommendations
ollama-checker smart-recommend

# Search for specific models
ollama-checker search qwen -l 5

# Sync model database (first time)
ollama-checker sync

Commands

hw-detect - Hardware Detection

Analyzes your system and shows compatible backends:

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

smart-recommend - Intelligent Recommendations

Gets the best models for your hardware:

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

search - Find Models

Search with intelligent scoring:

ollama-checker search llama -l 5
ollama-checker search coding --use-case coding
ollama-checker search qwen --quant Q4_K_M

Options:

  • -l, --limit <n> - Number of results (default: 10)
  • -u, --use-case <case> - Optimize for: general, coding, chat, reasoning, creative
  • --max-size <gb> - Maximum model size
  • --quant <type> - Filter by quantization (Q4_K_M, Q8_0, etc.)
  • --family <name> - Filter by model family

sync - Update Database

Downloads latest models from Ollama:

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%

Use Case Weights

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

Apple Silicon

  • M1, M1 Pro, M1 Max, M1 Ultra
  • M2, M2 Pro, M2 Max, M2 Ultra
  • M3, M3 Pro, M3 Max
  • M4, M4 Pro, M4 Max

NVIDIA (CUDA)

  • RTX 40 Series (4090, 4080, 4070, etc.)
  • RTX 30 Series (3090, 3080, 3070, etc.)
  • Data Center (H100, A100, etc.)

AMD (ROCm)

  • RX 7900 XTX, 7900 XT, 7800 XT
  • RX 6900 XT, 6800 XT
  • MI300, MI250

Intel

  • Arc A770, A750
  • Integrated Iris/UHD

CPU

  • 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

Find the best coding model

ollama-checker smart-recommend --use-case coding -l 3

Search for small, fast models

ollama-checker search "3b OR 7b" --max-size 5 -l 10

Get all Qwen variants

ollama-checker search qwen -l 20

Development

git clone https://github.com/Pavelevich/ollama-checker.git
cd ollama-checker
npm install
node bin/enhanced_cli.js hw-detect

License

MIT License - see LICENSE for details.