One-line summary
Open-source LLMs are models with publicly downloadable weights that you can run locally; commercial use depends on the license (MIT and Apache-2.0 generally allow it, community licenses add conditions); local model scale is bounded by GPU/unified memory, and the common deployment tools are Ollama, vLLM and llama.cpp.
Basic questions
1. What is an open-source LLM?
An open-source LLM is a large language model whose weights are publicly downloadable and can be run locally, without depending on the provider's API. Unlike closed models (such as OpenAI GPT-6 or Anthropic Claude, which are accessible only through official APIs), open-source model weights are directly available for download. MiniMax M3.1 Flash, open-sourced on October 23, 2026 (428B total parameters), is an example.
2. What is the difference between open-source and closed LLMs?
The core differences are weight availability, deployment model and control. Open-source models have public weights and can be deployed locally, fine-tuned and audited privately; closed models only expose APIs, cannot run locally, and data must be sent to the provider. Open-source deployment requires your own compute; closed models charge per call with no hardware investment. Closed examples: OpenAI GPT-6, Anthropic Claude. Open examples: DeepSeek, Qwen, Llama, MiniMax.
3. Can open-source LLMs be used commercially?
It depends on the license: MIT and Apache-2.0 generally permit commercial use; community licenses (such as Llama and MiniMax Community License) add conditions. DeepSeek uses MIT and Qwen uses Apache-2.0, which permit commercial use; Meta Llama uses a community license that requires a grant for products with over 700 million monthly active users; previous-gen MiniMax M3 uses the MiniMax Community License. Always read the license text in the model repository before use.
4. What are the mainstream open-source LLMs?
As of October 2026, mainstream open-source LLMs include DeepSeek (MIT), Qwen (Apache-2.0), Meta Llama (community license), and MiniMax M3.1 Flash (open-sourced October 23, 2026). Models span from 7B to several hundred billion parameters; MoE models (e.g., M3.1 Flash at 428B total / 23B active) deliver large parameter counts at lower active cost.
Advanced questions
5. What hardware is needed to run an open-source LLM locally?
It depends on parameter scale and quantization: 7B-class models need about 8GB+ of GPU/unified memory, 70B-class about 40GB+, and 100B+ parameter models need 64GB+ unified memory or multi-GPU setups. For example, the NVIDIA DGX Spark runs up to 100B parameters on the 64GB version and up to 200B on the 128GB version (official spec). Mac users can run 7B-70B models on Apple Silicon via MLX.
6. How do I deploy an open-source LLM?
The main tools are Ollama (one-command deployment, good for individuals), vLLM (high throughput, good for serving), llama.cpp (CPU/edge friendly) and MLX (Apple Silicon). The general flow: download the weights, choose an inference framework, start a local inference service (e.g., OpenAI-compatible API), then connect your application. Large MoE models (such as MiniMax M3.1 Flash) are usually served with vLLM or SGLang to exploit sparse activation.
7. What is a MoE (Mixture of Experts) model?
A MoE model splits into multiple expert subnetworks and activates only a subset per token, achieving a large parameter count at lower compute cost. Example: MiniMax M3.1 Flash has 428B total parameters but only 23B active per token. Total parameters bound knowledge capacity; active parameters bound per-token inference cost.
8. What is the difference between open-source and open-weight models?
Strict "open source" follows the Open Source Definition (OSI), requiring weights, code, data and license together; "open weight" only publishes weight files, without guaranteeing training data or code. In practice most LLMs are open-weight: Llama releases weights and inference code but not training data; DeepSeek and Qwen release weights plus technical reports and some data documentation.
9. Do open-source LLMs raise data privacy issues?
Running an open-source model locally keeps data on your machine, so privacy risk is lower than cloud API calls; however, the model itself may have memorized training data, and local logs or third-party runtimes (e.g., telemetry) should be audited. For sensitive data: deploy locally, disable telemetry, avoid inputting unauthorized personal information, and comply with local data protection law.
10. Are open-source LLMs worse than closed ones?
Quality depends on the task and benchmark: on coding, reasoning and Chinese tasks, top open-source models (DeepSeek, Qwen, MiniMax, etc.) have narrowed the gap with closed flagships, while closed flagships still lead on broad multimodal and long-context tasks. Choose based on public benchmarks and your own evaluation for the target task, not on an open/closed label alone.
Data sources and references
- DeepSeek official GitHub (MIT license) link
- Qwen official GitHub (Apache-2.0) link
- Meta Llama official site (community license) link
- MiniMax Open Platform Docs: Self-hosting MiniMax-M3 (MiniMax Community License) link
- National Business Daily: MiniMax open-sources M3.1 Flash (Oct 23, 2026) link
- NVIDIA Blog: DGX Spark 64GB local AI (Oct 2026) link