Open-Source Models
Compare open-weight LLMs: Llama 4, Qwen 3, DeepSeek V3/R1, Mistral. MoE architecture, licensing, GPU requirements
The Problem: You want to use an LLM but cannot send data to external APIs due to privacy regulations, need custom fine-tuning, or want to avoid per-token costs at scale. Which open-source models exist and how do they compare?
The Solution: Choose the Right Open Model
Open-source (or open-weight) LLMs are models whose weights are publicly available for download, self-hosting, and often fine-tuning. Unlike closed models (GPT-5, Claude) where you only get API access, open models give you full control: run on your hardware, modify for your domain, no per-token costs. The trade-off: you manage infrastructure and typically get slightly lower performance on the hardest tasks.
Think of it like buying a car vs building your own — closed models are ready to drive, open models let you customize everything under the hood:
- 1. Define your constraints: GPU budget (7B runs on a laptop, 70B needs multi-GPU, 400B+ needs a cluster), latency requirements, and licensing restrictions
- 2. Match model to task: Coding → Qwen 3 / DeepSeek V3. Multilingual → Qwen 3 (119 languages). Reasoning → DeepSeek R1. General → Llama 4. EU compliance → Mistral
- 3. Consider quantization: GPTQ/AWQ/GGUF quantization can reduce 70B models to fit on consumer GPUs with minimal quality loss (Q4 = ~4x memory reduction)
- 4. Evaluate on YOUR data: Benchmarks show general trends but your domain may differ. Test 50-100 real examples from your use case before committing to infrastructure
When to Use Open Models
- Data Privacy: Self-hosted models keep data on your servers — critical for healthcare, finance, legal, and government. No data leaves your infrastructure
- Cost at Scale: At 1M+ requests/day, self-hosting becomes cheaper than API. DeepSeek V3 MoE uses only 37B active params out of 671B total — inference cost of a small model, knowledge of a huge one
- Fine-tuning & Customization: Open models can be fine-tuned on your domain data (medical, legal, code). Closed models offer limited fine-tuning or none at all
- Licensing Matters: MIT (DeepSeek R1) = no restrictions. Apache 2.0 (Mistral) = permissive. Llama = custom license with usage limits. Always check before production use
Fun Fact: DeepSeek V3 has 671 billion parameters total, but thanks to Mixture of Experts (MoE), only 37 billion activate per token. This means inference costs comparable to a 37B model, but knowledge breadth of a 671B model — a 18x efficiency gain.
Try It Yourself!
Compare open-source models interactively below to find the right one for your use case.
Open-weight: model weights available for download (Llama, DeepSeek). Open-source: training code and data also available. Most "open-source" models are actually open-weight — training code is usually proprietary.
- •7B models: RTX 4090 (24GB) — Q4 quantization
- •70B models: A100 80GB or 2x RTX 4090 (Q4)
- •400B+ MoE: cluster of 4-8x A100/H100
- •Tools: vLLM, TGI, llama.cpp, Ollama
MoE architecture activates only a fraction of parameters per token. DeepSeek V3: 671B total but 37B active. Qwen 3: 1T+ total but ~80B active. This gives big-model knowledge at small-model cost.
- •Meta (Llama 4) — 10M context, MoE, custom license
- •Alibaba (Qwen 3) — 119 languages, best at math, Apache 2.0
- •DeepSeek (V3, R1) — MIT license, MoE, reasoning
- •Mistral (Large 3) — EU-based, Apache 2.0, enterprise
Lesson Quiz
1.What is the difference between "open-weight" and "open-source" models?
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