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Model Recommendation Guide

Find the perfect Ollama AI model for your needs

Overview

Choosing the right AI model is crucial for the best user experience. This guide helps you select the most suitable Ollama model based on your needs, hardware configuration, and use cases.

Ollama supports over 40 mainstream open-source models, ranging from lightweight 1B parameter models to powerful 671B parameter models, covering general conversation, code generation, vision understanding, and more.

Choose by Use Case

Based on your specific needs, we recommend the following models:

General Conversation & Q&A

Suitable for chat assistants, knowledge Q&A, text generation, and other general scenarios.

Llama 3.3 70B

Rating: ⭐⭐⭐⭐⭐

Strengths:

  • Meta's latest generation, excellent overall capabilities
  • Superior bilingual performance (English & Chinese)
  • Outstanding reasoning and context understanding

System Requirements: At least 48GB RAM

Best for: Users seeking top quality

Mistral 7B

Rating: ⭐⭐⭐⭐

Strengths:

  • Lightweight and efficient, fast execution
  • Strong instruction-following ability
  • Low resource consumption

System Requirements: At least 8GB RAM

Best for: Limited hardware but need efficiency

Gemma 3 12B

Rating: ⭐⭐⭐⭐

Strengths:

  • Google's reliable quality
  • Great performance-resource balance
  • Excellent multilingual support

System Requirements: At least 16GB RAM

Best for: General use on mid-range hardware

Code Programming Assistance

Specifically optimized for code generation, debugging, explanation, and refactoring.

DeepSeek-Coder 33B

Rating: ⭐⭐⭐⭐⭐

Strengths:

  • Extremely high code generation accuracy
  • Supports 80+ programming languages
  • Strong algorithm implementation and optimization

System Requirements: At least 22GB RAM

Best for: Professional developers' top choice

CodeLlama 34B

Rating: ⭐⭐⭐⭐⭐

Strengths:

  • Meta's official code model
  • Excellent multi-file context understanding
  • Strong code completion and debugging

System Requirements: At least 24GB RAM

Best for: Complex project development

Qwen2.5-Coder 32B

Rating: ⭐⭐⭐⭐

Strengths:

  • Strong multi-language code translation
  • Excellent Chinese comments and documentation
  • Great cross-language development support

System Requirements: At least 22GB RAM

Best for: Multi-language projects

Code Llama 7B

Rating: ⭐⭐⭐

Strengths:

  • Low resource consumption
  • Fast code completion
  • Suitable for real-time assistance

System Requirements: At least 8GB RAM

Best for: Beginner or lightweight assistance

Vision & Multimodal Tasks

Support image understanding, description, analysis, and other vision-related tasks.

Llama 3.2 Vision 90B

Rating: ⭐⭐⭐⭐⭐

Strengths:

  • Powerful visual understanding
  • Handles mixed text-image conversations
  • Accurate image analysis and description

System Requirements: At least 56GB RAM

Best for: High-end vision tasks

Llama 3.2 Vision 11B

Rating: ⭐⭐⭐⭐

Strengths:

  • Lightweight multimodal model
  • Good basic vision understanding
  • Moderate resource requirements

System Requirements: At least 16GB RAM

Best for: Daily vision assistance

LLaVA 7B

Rating: ⭐⭐⭐

Strengths:

  • Open-source vision-language model
  • Low resource consumption
  • Suitable for experiments and learning

System Requirements: At least 8GB RAM

Best for: Entry-level vision tasks

Reasoning & Chain-of-Thought

Suitable for complex reasoning, mathematical problems, and logical analysis.

DeepSeek-R1 671B (Cloud)

Rating: ⭐⭐⭐⭐⭐

Strengths:

  • Powerful reasoning capabilities
  • Excellent at math and logic problems
  • Cloud-based, no local hardware needed

System Requirements: Ollama account + internet connection

Best for: Tasks requiring top-tier reasoning

QwQ 32B

Rating: ⭐⭐⭐⭐

Strengths:

  • Strong chain-of-thought reasoning
  • Step-by-step complex problem solving
  • Local execution, privacy-friendly

System Requirements: At least 22GB RAM

Best for: Complex reasoning and analysis

Choose by Hardware Configuration

Select models that run smoothly on your computer configuration.

Low-End (8GB RAM)

Suitable for entry-level users or laptops.

ModelParametersUse CaseSpeed
Mistral 7B7BGeneral conversation⚡⚡⚡⚡
Gemma 3 1B1BQuick Q&A⚡⚡⚡⚡⚡
Phi 4 Mini3.8BLightweight assistance⚡⚡⚡⚡⚡
Llama 3.2 3B3BBasic conversation⚡⚡⚡⚡
Code Llama 7B7BCode assistance⚡⚡⚡⚡
LLaVA 7B7BBasic vision⚡⚡⚡

Optimization Tips

  • Use quantized versions (Q4_K_M) to reduce memory usage
  • Run only one model at a time
  • Close unnecessary background programs
  • Consider cloud models for complex tasks

Mid-Range (16-32GB RAM)

Suitable for most users' daily use.

ModelParametersUse CaseSpeed
Llama 3.2 Vision 11B11BVision understanding⚡⚡⚡
Gemma 3 12B12BGeneral conversation⚡⚡⚡
Phi 414BEfficient reasoning⚡⚡⚡
QwQ 32B32BComplex reasoning⚡⚡
Qwen2.5-Coder 32B32BMulti-language code⚡⚡

Performance Boost Tips

  • Enable GPU acceleration (if you have a dedicated GPU)
  • Store model files on SSD
  • Adjust num_ctx parameter to optimize context length
  • Choose appropriate quantization level based on tasks

High-End (48GB+ RAM)

Suitable for professional users and complex tasks.

ModelParametersUse CaseSpeed
Llama 3.3 70B70BTop-tier conversation⚡⚡
Llama 3.1 70B70BSystem architecture⚡⚡
Llama 3.2 Vision 90B90BAdvanced vision⚡
DeepSeek-Coder 33B33BProfessional programming⚡⚡⚡
CodeLlama 34B34BCode generation⚡⚡⚡

Ultimate Performance Setup

  • Use high-end GPU (RTX 4090/A100) for significant speed boost
  • Enable multi-GPU support to distribute load
  • Optimize VRAM allocation for better throughput
  • Consider using professional inference servers

Cloud Models (No Hardware Required)

Suitable for users needing massive models but limited hardware.

ModelParametersUse CaseRequirements
DeepSeek-R1 671B671BTop-tier reasoningOllama account
Llama 4 Maverick 400B400BSuperior conversationOllama account
Kimi K2 1T1TUltra-long contextOllama account

About Cloud Models

Cloud models run via Ollama Cloud. You need to:

  1. Register an account at ollama.com
  2. Sign in through Ollama settings
  3. Models automatically connect to cloud service
  4. Requires stable internet connection for data transfer

Model Family Overview

Understanding different model families helps you make better choices.

Llama Series

Developer: Meta (Facebook)

Characteristics:

  • Industry's most popular open-source model series
  • Strong overall capabilities, broad adaptability
  • Continuous updates, fast version iterations

Version Comparison:

VersionParametersFeaturesUse Cases
Llama 4109B, 400BLatest flagship, strongest capabilityProfessional applications
Llama 3.370BExcellent performance, moderate costGeneral use - top choice
Llama 3.21B, 3BLightweight and efficientMobile devices, edge computing
Llama 3.2 Vision11B, 90BMultimodal capabilityVision understanding tasks
Llama 3.18B, 70B, 405BMature and stableProduction environments

Recommended Configuration

  • Entry: Llama 3.2 3B
  • Daily: Llama 3.3 70B (if hardware allows)
  • Professional: Llama 4 Maverick 400B (cloud)

Gemma Series

Developer: Google

Characteristics:

  • Advanced technology, well-optimized
  • Excellent multilingual support
  • Open-source friendly license

Version Comparison:

VersionParametersFeaturesUse Cases
Gemma 31B, 4B, 12B, 27BLatest version, comprehensive improvementsVarious scenarios
Gemma 22B, 9B, 27BMature and stableProduction environments

Recommended Configuration

  • Low-end: Gemma 3 1B
  • Mid-range: Gemma 3 12B
  • High-end: Gemma 3 27B

Mistral Series

Developer: Mistral AI

Characteristics:

  • Excellent performance-efficiency balance
  • Strong instruction-following ability
  • Fast inference speed

Main Versions:

  • Mistral 7B: Lightweight and efficient general model
  • Mixtral: Mixture-of-Experts architecture, more powerful

Best Use

Mistral 7B is the best choice for resource-constrained environments, running smoothly on 8GB RAM machines.

Phi Series

Developer: Microsoft

Characteristics:

  • Small size, big capability
  • High-quality training data
  • Research-oriented, innovative

Version Comparison:

VersionParametersFeaturesUse Cases
Phi 414BLatest version, strong reasoningMid-range configuration
Phi 4 Mini3.8BUltra-lightweight, excellent performanceLow-end devices

Special Note

Phi series is known for "small but powerful", providing excellent performance despite smaller parameter sizes, especially suitable for education and research.

Qwen Series

Developer: Alibaba Cloud

Characteristics:

  • Industry-leading Chinese capability
  • Comprehensive multilingual support
  • Rich specialized versions

Main Versions:

  • Qwen3: Latest general model
  • Qwen2.5-Coder: Professional code model, strong multi-language support
  • Qwen3-Coder: Next-generation code model

Chinese User Recommendation

Qwen series has extremely strong Chinese understanding and generation capabilities, an excellent choice for Chinese users.

DeepSeek Series

Developer: DeepSeek

Characteristics:

  • Outstanding reasoning ability
  • Code generation specialization
  • High cost-effectiveness

Main Versions:

  • DeepSeek-R1: Chain-of-thought reasoning model, supports ultra-large 671B cloud version
  • DeepSeek-Coder: Professional code model, developers' top choice

Developer Recommendation

DeepSeek-Coder 33B excels in code generation accuracy, the top choice for professional developers.

Performance Comparison Tables

Comprehensive comparison of mainstream models to help quick decision-making.

General Conversation Models

ModelParametersRAM NeededSpeedQualityReasoningOverall Score
Llama 3.3 70B70B48GB⚡⚡⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐95/100
Gemma 3 27B27B18GB⚡⚡⚡⭐⭐⭐⭐⭐⭐⭐⭐88/100
Mistral 7B7B8GB⚡⚡⚡⚡⭐⭐⭐⭐⭐⭐⭐⭐85/100
Gemma 3 12B12B16GB⚡⚡⚡⭐⭐⭐⭐⭐⭐⭐⭐82/100
Phi 414B16GB⚡⚡⚡⭐⭐⭐⭐⭐⭐⭐78/100
Llama 3.2 3B3B8GB⚡⚡⚡⚡⚡⭐⭐⭐⭐⭐⭐70/100

Code Generation Models

ModelParametersRAM NeededSpeedCode QualityLanguage SupportOverall Score
DeepSeek-Coder 33B33B22GB⚡⚡⭐⭐⭐⭐⭐80+95/100
CodeLlama 34B34B24GB⚡⚡⭐⭐⭐⭐⭐Mainstream92/100
Qwen2.5-Coder 32B32B22GB⚡⚡⭐⭐⭐⭐Multi-language90/100
Code Llama 7B7B8GB⚡⚡⚡⚡⭐⭐⭐Mainstream75/100

Vision Models

ModelParametersRAM NeededSpeedVision UnderstandingText GenerationOverall Score
Llama 3.2 Vision 90B90B56GB⚡⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐95/100
Llama 3.2 Vision 11B11B16GB⚡⚡⚡⭐⭐⭐⭐⭐⭐⭐⭐82/100
LLaVA 7B7B8GB⚡⚡⚡⭐⭐⭐⭐⭐⭐70/100

Model Selection Decision Tree

Not sure which to choose? Follow this decision flow:

Determine Primary Use

What do you mainly want to do?

  • 📝 Daily conversation and Q&A → Go to Step 2
  • 💻 Programming and code generation → Go to Step 3
  • 🖼️ Image understanding and analysis → Go to Step 4
  • 🧠 Complex reasoning and math → Go to Step 5

Daily Conversation Scenario

Check your hardware configuration:

  • 8GB RAM: Choose Mistral 7B or Gemma 3 1B
  • 16GB RAM: Choose Gemma 3 12B or Phi 4
  • 32GB+ RAM: Choose Gemma 3 27B
  • 48GB+ RAM: Choose Llama 3.3 70B

Code Programming Scenario

Based on your needs:

  • Quick code completion (8GB RAM): Code Llama 7B
  • Professional development (22GB+ RAM): DeepSeek-Coder 33B or CodeLlama 34B
  • Multi-language projects (22GB+ RAM): Qwen2.5-Coder 32B

Vision Understanding Scenario

Based on task complexity:

  • Basic image description (8GB RAM): LLaVA 7B
  • Daily vision assistance (16GB RAM): Llama 3.2 Vision 11B
  • Professional vision analysis (56GB+ RAM): Llama 3.2 Vision 90B

Complex Reasoning Scenario

Based on reasoning complexity:

  • Medium reasoning tasks (22GB RAM): QwQ 32B
  • Top reasoning capability (internet required): DeepSeek-R1 671B Cloud

Frequently Asked Questions

Model Usage Best Practices

Pre-Download Checklist

Confirm Sufficient Hardware

Use the RAM requirement tables above to ensure your system has enough memory.

Choose Appropriate Quantization

Unless hardware is ample, recommend downloading Q4_K_M quantized versions.

Estimate Disk Space

  • 7B model: ~4-8GB
  • 13B model: ~8-16GB
  • 33B model: ~18-25GB
  • 70B model: ~40-50GB

Network Stability

Large models may take hours to download, ensure stable network or resume support.

First-Time Use Recommendations

Start with Small Models

When uncertain, try Mistral 7B or Gemma 3 1B first to familiarize with the process.

Test Performance

Run simple conversations, observe:

  • Load time (should be within 1 minute)
  • Generation speed (at least 10 tokens/s)
  • System response (shouldn't lag)

Adjust Parameters

Based on actual performance, adjust:

  • Lower temperature for stability
  • Reduce num_ctx to lower memory usage
  • Limit num_predict to control output length

Evaluate Quality

Test with your actual tasks to judge if it meets needs.

Long-Term Use Recommendations

  1. Regular updates: Ollama models continuously improve, watch for new versions
  2. Clean unused models: Delete unused models promptly to free space
  3. Backup configs: Save your preferred model parameter settings
  4. Monitor resources: Watch memory and disk usage
  5. Community participation: Share experiences in forums, learn from others

Complete model combination suggestions for different user types.

Students/Beginners

Hardware assumption: 8-16GB RAM

Recommended combination:

  1. Mistral 7B - Daily conversation and learning assistance
  2. Code Llama 7B - Programming homework help
  3. Gemma 3 1B - Quick queries and Q&A

Total usage: ~15-20GB disk

Professional Developers

Hardware assumption: 32GB RAM + GPU

Recommended combination:

  1. DeepSeek-Coder 33B - Primary code generation tool
  2. Llama 3.3 70B (if RAM sufficient) - Technical discussion and architecture design
  3. Qwen2.5-Coder 32B - Multi-language and code translation

Total usage: ~90-100GB disk

Content Creators

Hardware assumption: 16-32GB RAM

Recommended combination:

  1. Gemma 3 12B - Text creation and polish
  2. Llama 3.2 Vision 11B - Image analysis and description
  3. Qwen3 12B - Chinese content generation

Total usage: ~40-50GB disk

Researchers

Hardware assumption: 48GB+ RAM or cloud

Recommended combination:

  1. Llama 3.3 70B - Deep analysis and reasoning
  2. DeepSeek-R1 671B Cloud - Complex reasoning and math
  3. QwQ 32B - Chain-of-thought reasoning
  4. Llama 3.2 Vision 90B - Multimodal research

Total usage: Local ~120GB + cloud models

Enterprise Teams

Hardware assumption: Dedicated server 128GB+ RAM

Recommended combination:

  1. Llama 3.3 70B - General business consultation
  2. DeepSeek-Coder 33B - Code review and generation
  3. Qwen3 27B - Chinese business processing
  4. Llama 3.2 Vision 90B - Document and image analysis

Total usage: ~200GB disk

Enterprise Deployment Recommendations

  • Use dedicated inference servers
  • Configure load balancing
  • Enable multi-GPU acceleration
  • Consider using high-performance frameworks like vLLM
  • Establish model evaluation and update processes

Summary & Recommendations

Quick Selection Guide

If you just want one versatile model:

  • 8GB RAM: Mistral 7B
  • 16GB RAM: Gemma 3 12B
  • 32GB+ RAM: Llama 3.3 70B

If you're a developer:

  • DeepSeek-Coder 33B (requires 22GB RAM)

If you value Chinese support:

  • Qwen3 12B or Qwen3 27B

If you need vision capability:

  • Llama 3.2 Vision 11B (requires 16GB RAM)

If hardware limited but need powerful capability:

  • DeepSeek-R1 671B Cloud or Llama 4 Cloud

Important Reminders

Three Principles of Model Selection

  1. Hardware First: Never choose models beyond hardware capability
  2. Scenario Match: Specialized models (code, vision) outperform general ones
  3. Iterative Optimization: Start with small models, upgrade based on needs

Continuous Learning

The Ollama ecosystem evolves rapidly, with new models constantly emerging:

Next Steps

Explore OllaMan Features

Learn how to maximize model functionality

Quick Start Tutorial

Start using OllaMan from scratch

Ollama Model Library

Browse all available models (official)

Need Help?

If you still have questions about model selection:

  • Ask in GitHub Discussions
  • Join Discord community discussions
  • Consult full documentation for more information
  • Contact technical support for personalized recommendations

This guide was last updated in November 2025. Model information and recommendations are based on current latest versions and may change in the future.

Server Authentication Setup

Configure Basic Auth for Ollama servers