Local LLM: which open-source AI model to choose? 2026 comparison
GLM, DeepSeek, Kimi, Mistral, Qwen: open-weight models now rival the best APIs and run on your own hardware. Here is how to choose based on your use case, your hardware and your constraints.
Why run an open-source LLM locally
An open-weight model runs on your own hardware, without sending your data to a third party. Quality has caught up with the best closed interfaces, and the cost per token collapses once the installation is paid off. The permissive licence allows commercial use and sovereign hosting. The general framework is set out in our definition of the sovereign AI platform.
Comparison of 2026 open-source LLMs
| Model | Size | Strengths | For whom |
|---|---|---|---|
| GLM 5.2 | 744B | Maximum quality, reasoning, code | Demanding organisations |
| Kimi K3 | 2800B | Frontier level, 1M context, vision | Large-scale agents |
| Kimi K2.7 Code | 1000B | Agentic, tool calls, MCP | Development agents |
| DeepSeek V4 | 1600B | Price-performance ratio, Pro and Flash | Versatile foundation |
| Nemotron 3 Ultra | 550B | NVFP4, 1M context, NVIDIA ecosystem | NVIDIA-aligned |
| MiniMax M3 | 428B | Multimodal, long context, cost-effective | Documents and image |
| Mistral | Open weights | Sovereign FR and EU, code and reasoning | European sovereignty |
| Qwen 3.6 and Gemma 4 | 4 to 31B | Compact, single card | SMEs and edge |
Sizes indicate the total parameter count. Mixture-of-experts architectures only activate a fraction of them per token, which keeps inference compute cost down.
Choosing by use case
For reasoning and code generation, GLM 5.2 and DeepSeek V4 Pro lead the field. For autonomous agents chaining tool calls, Kimi K2.7 Code brings stability. For long context and large documents, Kimi K3, MiniMax M3 and Nemotron 3 Ultra offer a million tokens. Kimi K3 even measures up against closed APIs: the Artificial Analysis index places it ahead of Claude Opus 4.8, and it takes first place in the Frontend Code Arena for the quality of generated interfaces; its weights remain announced for the end of July. For European sovereignty, Mistral remains the reference option. For an SME or edge use case, Qwen 3.6 and Gemma 4 fit on a single card.
Choosing by hardware and partners
Large mixture-of-experts models require a GPU server, for example an RTX PRO 6000 server or an H200 rack. Compact models run on a DGX Spark workstation or a Mac Studio. Compute relies on NVIDIA GPUs, and servers are certified by HPE, Dell, Supermicro, Lenovo, ASUS and Gigabyte, which guarantees manufacturer support and warranty. QDNA integrates this hardware and operates it for you, from the workstation to the rack. The tier breakdown is in our hardware pages.
Sourcing models: Hugging Face, NVFP4, Unsloth
Weights are downloaded from Hugging Face, the distribution hub for open models. Each vendor publishes under its official organisation: zai-org for GLM, deepseek-ai, moonshotai for Kimi, mistralai, Qwen. The model card specifies the licence, the weight format, generally safetensors, and the serving instructions. The huggingface-cli tool pulls the files directly onto the inference server.
For Blackwell GPUs, NVIDIA publishes versions already quantised to the NVFP4 format, a 4-bit format that reduces memory by a factor of 1.7 to 3 depending on the original precision. The nvidia/DeepSeek-V4-Flash-NVFP4, nvidia/DeepSeek-V4-Pro-NVFP4 or nvidia/Qwen3.6-35B-A3B-NVFP4 repositories serve as-is with vLLM or TensorRT-LLM, with no calibration step on your side. An RTX PRO 6000 server or a B200 rack runs this FP4 compute natively.
Unsloth complements the pipeline on two fronts. Its dynamic-quantisation GGUF conversions tune the compression layer by layer, which preserves the quality of models served by llama.cpp on a workstation. The project also publishes its own NVFP4 versions, such as unsloth/Qwen3.6-27B-NVFP4, which fits on a 24 GB card. The same library handles QLoRA fine-tuning on a single card: the model specialises on your data without it ever leaving your walls. Details are in our Unsloth page.
Licence and sovereignty
Open weights does not always mean open source in the strict sense. Check the licence, often MIT, Apache or permissive, before commercial use. Running locally guarantees that data never leaves the company, which eases GDPR compliance and HDS hosting of health data.
Interchangeable through a gateway
These models are unified behind a single interface with LiteLLM. Switching models takes a single configuration line, with no application code to rewrite. The practical follow-up is in our guide to installing a local LLM in the enterprise.
Frequently asked questions
What is the best open-source LLM in 2026?
GLM 5.2 holds first place among open models available on the intelligence index. Kimi K3 has overtaken it since July 2026 and joins the level of closed APIs, but its weights are only announced for the end of the month. For the best cost-performance ratio, DeepSeek V4 delivers frontier-level quality at reduced cost.
Can an open-source AI model run locally?
Yes. Served by vLLM on a GPU server or by quantised llama.cpp on a workstation, an open-weight model runs entirely on your own infrastructure, with no external dependency.
Which open-source model for local coding?
Kimi K2.7 Code for agentic stability and tool calls, GLM 5.2 or DeepSeek V4 for generation quality. Mistral's Codestral is the European sovereign option.
What hardware should run an open-source LLM?
A DGX Spark or Mac Studio workstation for compact models, an RTX PRO 6000 server or an H200 rack for large models. Servers are certified by HPE, Dell, Supermicro and Lenovo on NVIDIA GPU bases.
Where can you download an open-source LLM?
On Hugging Face, from the vendor's official organisation. NVFP4 releases published by NVIDIA and Unsloth's GGUF builds are ready to serve: the former for a Blackwell server under vLLM, the latter for a workstation under llama.cpp.
Is an open-source LLM worth as much as a closed API like ChatGPT?
On quality, the best open models now match closed APIs in reasoning and code. The difference lies in control: on-premises, your data stays with you and the cost per token collapses once the hardware is paid off, whereas an API bills every call and processes your data externally. The detailed calculation is in our comparison of on-premises versus API.
Choose the right model for your platform
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