Open Source LLMs
Why Open Source? Open-source LLMs enable local deployment, customization, and full control over your AI stack without API dependencies or per-token costs.
Leading Open Source Models
Meta Llama Family
| Model | Parameters | Context | Highlights |
|---|---|---|---|
| Llama 3.1 8B | 8B | 128K | Best small model |
| Llama 3.1 70B | 70B | 128K | Competitive with GPT-4 |
| Llama 3.1 405B | 405B | 128K | Largest open model |
Other Top Models
| Model | Provider | Parameters | Strengths |
|---|---|---|---|
| Mistral 7B | Mistral AI | 7B | Efficient, fast |
| Mixtral 8x7B | Mistral AI | 46B (12B active) | MoE architecture |
| Qwen 2 | Alibaba | 7-72B | Multilingual, code |
| Gemma 2 | 9-27B | Efficient, safety | |
| Phi-3 | Microsoft | 3.8-14B | Small but capable |
Running Models Locally
Hardware Requirements
| Model Size | Minimum GPU | Recommended |
|---|---|---|
| 7B | 8GB VRAM | 16GB (RTX 4080) |
| 13B | 16GB VRAM | 24GB (RTX 4090) |
| 70B (4-bit) | 40GB VRAM | 80GB (A100) |
| 70B (16-bit) | 140GB VRAM | 2x A100 80GB |
Local Inference Tools
| Tool | Platform | Best For |
|---|---|---|
| llama.cpp | CPU/GPU | Maximum compatibility |
| Ollama | Desktop | Easy setup |
| vLLM | GPU | Production serving |
| text-generation-webui | Desktop | GUI interface |
Licensing
| License | Commercial Use | Modifications |
|---|---|---|
| Llama 3 | ✅ (with conditions) | ✅ |
| Apache 2.0 | ✅ | ✅ |
| MIT | ✅ | ✅ |
Advantages vs Disadvantages
Advantages
- ✅ No API costs, private data stays local
- ✅ Full customization, fine-tuning freedom
- ✅ No rate limits, predictable performance
- ✅ Air-gapped deployment possible
Disadvantages
- ❌ Requires GPUs or specialized hardware
- ❌ Self-managed infrastructure and updates
- ❌ May lag frontier models in capabilities
- ❌ More complex deployment and scaling
open sourceosslocal modelllama
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