Open-Source LLM Guide: Choose the Best-fit AI Solution

Open-source LLMs give developers flexible, controllable AI solutions. This article comprehensively compares mainstream open-source models to help you make the best choice.

Overview of Popular Open-Source Models

🦙

LLaMA 2

Meta open-source base model

  • • 7B / 13B / 70B parameters
  • • Commercial license
  • • Rich community ecosystem
🌟

Mistral

High-efficiency European model

  • • 7B outperforms many 13B models
  • • Apache 2.0 license
  • • Excellent inference efficiency
🚀

Qwen

Alibaba Tongyi Qianwen

  • • Full series from 1.8B to 72B
  • • Strong Chinese capability
  • • Tool calling support
💬

ChatGLM

Tsinghua Zhipu open-source

  • • 6B / 130B variants
  • • Bilingual (Chinese/English) optimized
  • • Low-resource friendly deployment
🔬

Baichuan

Baichuan Intelligence

  • • 7B / 13B variants
  • • High-quality training data
  • • Business-friendly license
🎯

Yi

01.AI (Yi series)

  • • 6B / 34B variants
  • • Strong long-context ability
  • • Excellent reasoning performance

Benchmark Comparison

Comprehensive Evaluation of Open-Source Models

ModelParametersMMLUHumanEvalChinese AbilityInference Speed
LLaMA 2-70B70B68.9%29.9%⭐⭐⭐⭐⭐
Mistral-7B7B60.1%26.2%⭐⭐⭐⭐⭐⭐⭐
Qwen-72B72B77.4%35.4%⭐⭐⭐⭐⭐⭐⭐⭐
ChatGLM3-6B6B61.4%18.2%⭐⭐⭐⭐⭐⭐⭐⭐⭐
Yi-34B34B76.3%23.2%⭐⭐⭐⭐⭐⭐⭐

Deployment Requirements

Hardware Configuration Suggestions

VRAM needs (FP16)

7B model~14GB
13B model~26GB
34B model~68GB
70B model~140GB

After quantization (INT4)

7B model~4GB
13B model~8GB
34B model~20GB
70B model~40GB

Feature Comparison

Unique Advantages

🦙 LLaMA 2

  • • Most active open-source community
  • • Rich fine-tuned variants (Alpaca, Vicuna, etc.)
  • • Broad framework support
  • • Detailed technical documentation

🌟 Mistral

  • • Extreme inference efficiency
  • • Sliding-window attention
  • • Small-parameter high performance
  • • Easy to deploy and quantize

🚀 Qwen

  • • Native tool calling
  • • Multimodal variants
  • • Excellent Chinese understanding
  • • Complete model family

💬 ChatGLM

  • • Unique GLM architecture
  • • Low-resource friendly
  • • Balanced CN/EN bilingual
  • • Dialogue-optimized design

Deployment Solutions

Inference Framework Choices

vLLM

High-performance inference engine

  • ✅ PagedAttention optimization
  • ✅ Efficient batching
  • ✅ Supports most models

llama.cpp

CPU/GPU general solution

  • ✅ Mature quantization support
  • ✅ Low resource footprint
  • ✅ Cross-platform deployment

TGI

Hugging Face Text Generation Inference

  • ✅ Production-grade deployment
  • ✅ Streaming output
  • ✅ Robust monitoring

Licenses

Commercial Use Terms

ModelLicenseCommercial LimitsRedistribution
LLaMA 2CustomMAU < 700M✅ Attribution required
MistralApache 2.0No restrictions✅ Free
QwenTongyi QianwenNo restrictions✅ Attribution required
ChatGLMCustomApproval required⚠️ Limited

Decision Tree

How to Choose the Right Open-Source Model

Scenario 1: Resource-constrained deployment

Recommended: Mistral-7B (English), ChatGLM3-6B (Chinese), Qwen-1.8B (ultra-lightweight)

Scenario 2: Chinese-first applications

Recommended: Qwen series, ChatGLM series, Baichuan series

Scenario 3: Need strong community support

Recommended: LLaMA 2 and derivatives (Alpaca, Vicuna, etc.)

Scenario 4: Minimal commercial restrictions

Recommended: Mistral (Apache 2.0), Qwen (business-friendly)

Deployment Best Practices

Production Environment Tips

Optimization Strategies

  • ✅ Use quantization to reduce VRAM
  • ✅ Batch requests to improve throughput
  • ✅ Implement model result caching
  • ✅ Configure load balancing

Monitoring Metrics

  • 📊 Token generation speed
  • 📊 GPU utilization
  • 📊 Memory usage
  • 📊 Request latency distribution

Start Your Open-Source LLM Journey

Open-source LLMs unlock endless possibilities. Whether deployed locally or in the cloud, LLM APIs make it easy to integrate diverse open-source models and build applications tailored to your needs.

Try Open-Source Model APIs