Open-source LLMs shipped fast in 2026. DeepSeek V4, Qwen 3.6, Llama 4 Scout, Mistral Small 4, and Gemma 4 all landed with open weights, local run files, and licenses that let you self-host without per-token billing. The biggest release, DeepSeek V4, launched on June 9, 2026 with 1.6 trillion total parameters, a 256K context window, and benchmark scores within two points of GPT-5.5 on MMLU-Pro. Alibaba shipped Qwen 3.6 on June 17 with Apache 2.0 licensing. Llama 4 Scout, Mistral Small 4, and Gemma 4 each target a different self-hosting niche: long context, permissive commercial use, and edge hardware. These releases give local AI builders a fixed-cost path away from usage-based pricing.
The open-weight wave matters because it removes the API meter. Closed model prices dropped this year, but the best free AI models guide shows why self-hosted models still win for privacy and cost control. A local DeepSeek V4 or Qwen 3.6 runs inside your own network. There is no prompt logging, no rate limit reset, and no credit pool change. For developers burned by tighter AI API free tier limits, that control is the main reason to self-host. The tradeoff is hardware, quantization work, and model maintenance. But the models are free to download and use.
Hugging Face hosts all five model cards and safetensors weights. You can also review the open-source LLM models benchmark guide to compare coding scores, local agent performance, and license terms before downloading. Each vendor publishes a release page. Meta AI documents Llama 4 Scout on its Meta AI portal. Mistral AI has Mistral Small 4 notes. Alibaba Cloud lists Qwen 3.6. DeepSeek maintains its own release page. You do not need an API key for any of these weights. You do need enough disk, RAM, and GPU memory for the model you pick.
Self-hosting in 2026 is not a toy project. A 1.6T-parameter model like DeepSeek V4 needs a multi-GPU node for full precision, but its 32B active MoE design means a dual 24GB GPU setup can run 4-bit inference. Qwen 3.6, Mistral Small 4, and Gemma 4 fit on a single 16GB to 24GB GPU. Llama 4 Scout has a 10M-token context window that requires aggressive caching but enables document-scale retrieval. Each model has different license terms. Some are MIT, some Apache 2.0, some community licenses. Read the license before you build a product on top.
How Do the Top Options Compare?
| Model | Parameters | Context Window | License | 4-bit Size | Best For |
|---|---|---|---|---|---|
| DeepSeek V4 | 1.6T total, 32B active | 256K | MIT | ~90GB | Frontier local reasoning |
| Qwen 3.6 | 30B | 131K | Apache 2.0 | 16GB | Coding and local agents |
| Llama 4 Scout | 109B total, 17B active | 10M | Llama 4 Community | ~60GB | Massive document context |
| Mistral Small 4 | 24B | 128K | Apache 2.0 | 11GB | Permissive commercial self-host |
| Gemma 4 | 27B | 128K | Gemma Terms | 14GB | Edge and consumer GPUs |
Sizes are approximate 4-bit GGUF downloads. Benchmark performance varies by quantization and serving stack. Check the model card before deployment.
1. DeepSeek V4 , Best for frontier-scale local reasoning
DeepSeek V4 launched on June 9, 2026 with 1.6 trillion total parameters and a 32B active mixture-of-experts design. The full model has a 256K context window and an MIT license. On MMLU-Pro, DeepSeek reports 89.1, within two points of GPT-5.5. On HumanEval, it scores 92.4. The open weights are available from DeepSeek and mirrored on Hugging Face. This is the strongest open model for local reasoning tasks.
The MoE architecture keeps inference cheaper than a dense 1.6T model. Only 32B parameters activate per token, so a 4-bit quantized build needs roughly 90GB of VRAM. That means two 48GB GPUs can run it slowly, while four 24GB GPUs are more comfortable. If you have limited hardware, the DeepSeek V4 open-source guide covers GGUF quants, vLLM, and CPU offload options.
For teams that need GPT-5.5-level quality without sending data to a closed API, DeepSeek V4 is the default choice. The MIT license allows commercial use, fine-tuning, and redistribution. The tradeoff is operational complexity. You must manage sharding, long context KV cache, and multi-GPU serving. Benchmark scores may drop in low-bit quants. A production deployment also needs a serving stack like vLLM or SGLang.
Key strengths:
- ✅ MIT license allows commercial use and modification without royalties
- ✅ 32B active MoE runs faster than dense 1.6T models
- ✅ 256K context window handles long documents and multi-file repos
- ✅ MLU-Pro score within two points of GPT-5.5
- ✅ Weights and GGUF files are free on Hugging Face
- ❌ Needs 90GB or more VRAM for comfortable 4-bit inference
- ❌ Multi-GPU setup adds complexity for small teams
- ❌ Full precision requires data center hardware
Who it’s for: Choose DeepSeek V4 if you need near-frontier local reasoning and can operate a multi-GPU server.
2. Qwen 3.6 , Best for local coding and agent work
Qwen 3.6 shipped June 17, 2026 from Alibaba’s Qwen team with a 30B parameter dense architecture, a 131K context window, and Apache 2.0 licensing. It scores 90.5 on HumanEval and 86.2 on MMLU-Pro. The Apache license is the most permissive of this group for production code. You can download weights from Alibaba Cloud or Hugging Face. This is the strongest open coding model under 50B for local use.
Qwen 3.6 embeds tool calls, handles 40-language code translation, and works with agentic frameworks. A 4-bit Q4_K_M GGUF needs about 16GB of VRAM, so a single RTX 4090 or 5090 laptop GPU can run it. The Qwen 3.6 Apache release walks through local serving with llama.cpp and vLLM. It also includes examples for function calling and code editing.
Compared with closed coding models, Qwen 3.6 loses some latency and larger context recall, but it avoids usage-based billing. Developers squeezed by AI coding tools pricing changes can lock in a fixed hardware cost. The model is small enough for a local agent stack and permissive enough for private forks. It does not match DeepSeek V4 on frontier math, but it wins on deployability.
Key strengths:
- ✅ Apache 2.0 license has no share-alike or use restrictions
- ✅ 30B model fits in 16GB VRAM at 4-bit
- ✅ Strong 90.5 HumanEval score for local coding
- ✅ 131K context handles large repos
- ✅ Works with llama.cpp, vLLM, and Ollama
- ❌ Dense 30B inference is slower than MoE peers per token
- ❌ Long context memory can exceed 32GB system RAM
- ❌ Benchmarks trail DeepSeek V4 on multi-step reasoning
Who it’s for: Choose Qwen 3.6 if you want a permissively licensed local coding model that runs on a single consumer GPU.
3. Llama 4 Scout , Best for 10M-token document context
Meta AI released Llama 4 Scout in April 2026 with a 109B total parameter MoE design and 17B active parameters. Its headline feature is a 10M-token context window, the largest among open models you can self-host. On MMLU-Pro, it scores 87.5. On HumanEval, it scores 89.7. The weights are on Meta AI and Hugging Face. This model is built for document-scale retrieval and agent memory.
The 10M-token context lets you load entire codebases, legal documents, or multi-book research sets into one prompt. A 4-bit GGUF build needs about 60GB of VRAM, which fits a 64GB Mac or a dual 32GB GPU setup. The Llama 4 Scout and Maverick release details context caching and low-resource serving. It also covers Maverick if you want more reasoning depth.
Scout uses the Llama 4 Community License, which is free for most users but includes restrictions for large platforms. It is not Apache or MIT. This is the main downside for startups that want unrestricted commercial redistribution. It also trades dense reasoning depth for context length, so its MMLU-Pro score sits below DeepSeek V4. Still, no other open model handles this much text locally.
Key strengths:
- ✅ 10M-token context window is unmatched for document-scale work
- ✅ 17B active MoE keeps per-token compute manageable
- ✅ Scales across 64GB Mac or dual 32GB GPU systems
- ✅ Good 89.7 HumanEval for code generation
- ❌ Llama 4 Community License adds usage restrictions for large platforms
- ❌ 4-bit build needs 60GB VRAM, too much for a single 24GB card
- ❌ Reasons less reliably than DeepSeek V4 on complex multi-hop tasks
Who it’s for: Choose Llama 4 Scout if your main need is loading millions of tokens of context without an API.
4. Mistral Small 4 , Best permissive model for a single GPU
Mistral Small 4 shipped May 22, 2026 under Apache 2.0. It is a dense 24B model with a 128K context window. It scores 85.4 on MMLU-Pro and 88.9 on HumanEval. Mistral AI publishes the weights on Mistral AI and Hugging Face. This model is the easiest permissive option for a single 16GB GPU.
A 4-bit Q4_K_M build of Mistral Small 4 is about 11GB. It leaves room on a 16GB card for context and tool calls. The Mistral Small 4 Apache release covers Ollama, llama.cpp, and vLLM. Unlike Llama 4 Scout, there are no special license restrictions for large commercial use. That makes it a safe default for small product teams.
Mistral Small 4 does not win on raw benchmark totals. It trails Qwen 3.6 on coding and DeepSeek V4 on reasoning. But it wins on deployment simplicity and licensing. For small teams that want a fast, private, no-fee assistant, this model is the pragmatic default. It also fine-tunes well on a single 24GB GPU.
Key strengths:
- ✅ Apache 2.0 license supports full commercial use
- ✅ 11GB 4-bit footprint fits a 16GB GPU
- ✅ 128K context handles long reports and logs
- ✅ Fast token speed for a dense 24B model
- ❌ Lower MMLU-Pro score than DeepSeek V4 and Qwen 3.6
- ❌ Dense architecture uses more memory per token than MoE
- ❌ Less agentic tool-calling depth than Qwen 3.6
Who it’s for: Choose Mistral Small 4 if you need a permissive 24B model that runs on one consumer GPU.
5. Gemma 4 , Best for edge and consumer GPU self-hosting
Gemma 4 released June 2, 2026 as a 27B open-weight model with a 128K context window. It scores 84.6 on MMLU-Pro and 86.3 on HumanEval. Gemma 4 targets edge devices and laptops, not data center boxes. The weights are free to download from Hugging Face under Gemma Terms of Use. This is the best option for a private model on limited hardware.
At 4-bit quantization, Gemma 4 is about 14GB. That fits a 16GB laptop GPU or an Apple M-series Mac with 32GB unified memory. The Gemma 4 open-source guide covers llama.cpp, Ollama, and local API serving. You can run it offline with no API key and no cloud billing.
Gemma 4’s license is not Apache or MIT. The Gemma Terms allow most self-hosting and research but limit some commercial redistribution scenarios. Its reasoning and coding scores trail Qwen 3.6 and DeepSeek V4. But for edge self-hosting, it offers the simplest setup. The model supports 140 languages and long document summarization.
Key strengths:
- ✅ 27B model runs on a 16GB laptop GPU at 4-bit
- ✅ 128K context window for local document work
- ✅ Supports 140 languages
- ✅ Good 86.3 HumanEval for on-device coding
- ✅ Easy llama.cpp and Ollama deployment
- ❌ Gemma Terms of Use are less permissive than Apache 2.0
- ❌ Lower MMLU-Pro score than larger open models
- ❌ Struggles with multi-step agent tasks compared with Qwen 3.6
Who it’s for: Choose Gemma 4 if you want a local model on an edge device or laptop without a dedicated GPU server.
Frequently Asked Questions
Which open-source LLM should I self-host if I have one 16GB GPU?
Mistral Small 4 is the safest choice. A 4-bit build uses about 11GB of VRAM. Qwen 3.6 and Gemma 4 also fit, but Qwen 3.6 may need context trimming and Gemma 4 has a less permissive license for commercial use.
What is the most permissive license for self-hosted LLMs?
Apache 2.0 is the most permissive common license in this group. Qwen 3.6 and Mistral Small 4 use it. MIT is also permissive, and DeepSeek V4 uses MIT. Llama 4 Scout and Gemma 4 add extra restrictions for large platforms or some redistribution.
Do I need internet access to run these models after download?
No. Once you download the weights, you can run them fully offline. This keeps data private and avoids rate limits. You only need internet for the initial model download and updates.
How much VRAM do I need to self-host DeepSeek V4?
A 4-bit build requires about 90GB of VRAM. That means two 48GB cards or four 24GB cards. CPU offload can reduce VRAM use, but token speed will drop sharply.
Which model handles the longest documents?
Llama 4 Scout supports a 10M-token context window. That is far larger than DeepSeek V4, Qwen 3.6, Mistral Small 4, and Gemma 4. You can load entire multi-book research sets or large code repos.
Are these open-source models actually free for commercial use?
Most are free for commercial use, but check the license. Qwen 3.6 and Mistral Small 4 use Apache 2.0 and allow commercial use with few restrictions. DeepSeek V4 uses MIT. Llama 4 Scout and Gemma 4 have specific terms that limit large platforms or some redistribution.
What Should You Remember?
- DeepSeek V4 leads self-hosted frontier performance with 1.6T total parameters and a 256K context window under an MIT license.
- Qwen 3.6 is the strongest Apache 2.0 coding model with 30B parameters and a 131K context window.
- Llama 4 Scout offers a 10M-token context window, ideal for book-length retrieval and agent memory.
- Mistral Small 4 gives you a 24B Apache 2.0 model that fits on a 16GB GPU with 4-bit quantization.
- Gemma 4 is the best edge option, with a 27B model small enough for local laptops and low memory.
- Self-hosting removes API fees but shifts compute, storage, and maintenance costs to your hardware.
- Check the license before production use, because not all open weights allow full commercial redistribution.
Free AI News is an independent editorial publication. Information about AI pricing, free-tier limits, and features changes frequently and may become outdated. Always verify current details through the vendor’s official pages. Affiliate links may earn a commission at no cost to you, and never affect our reporting.
