Why open-source LLMs are finally good enough for business
A couple of years ago the gap between open and cloud models was a canyon. Not anymore. Today’s workhorse-class open models (27–32 billion parameters) handle most business tasks with confidence: answering questions from a knowledge base, reviewing contracts, drafting text, reading spreadsheets, writing and debugging code. And all of it runs on hardware you own, with no per-token bill showing up at the end of the month.
The main open-source LLM families
| Family | What it is | Strong at |
|---|---|---|
| Qwen | Chinese lineup, wide range of sizes | strong all-rounder, good at code and non-English languages |
| Llama | Meta’s models, huge ecosystem | maturity, the most third-party tooling |
| DeepSeek | built around reasoning and code | logic, math, software development |
| Gemma | Google’s compact models | efficiency on modest hardware |
| Mistral / Mixtral | European, includes MoE variants | fast, a good quality-to-resource ratio |
How much VRAM you actually need
The real bottleneck isn’t which family you pick: it’s video memory. The model has to fit in it whole. Rough guidelines for quantized versions:
| Class | VRAM | What it’s good for |
|---|---|---|
| 7–14B | 6–12 GB | chat, documents, drafts, basic coding |
| 27–32B | 18–24 GB | business workhorse: contracts, vision, long context |
| 70B | 40–48 GB | close to top-tier cloud models |
| 100B+ (MoE) | 80 GB+ | maximum reasoning quality |
For the full memory math, see our guide on how much VRAM a local LLM actually needs.
How to choose the right model for your business
- •Start from the job, not the model: Q&A over a knowledge base, code, document analysis, vision — the task decides both the family and the size,
- •Don’t chase the biggest model available — 27–32B covers most business needs for a fraction of the cost and latency,
- •Test on your own data, not on benchmark leaderboards: results on your documents can look very different,
- •Size the GPU memory for several concurrent users, not just for the model file to barely fit.
For the hardware and stack behind the model — GPU sizing, software, security — see our on-premise AI server page.
Frequently asked questions
Are open-source models free to use commercially?
Most popular families, including Qwen, Llama, DeepSeek, Gemma, and Mistral, allow commercial use, but the licenses aren’t identical: some cap usage above a certain scale or user count, others restrict specific product categories. Check the license of the exact version you plan to deploy before committing; we do that as part of the model-selection stage.
Are they worse than ChatGPT?
On most everyday business tasks, workhorse-class models in the 27–32B range are already on par with cloud models. Top proprietary models still pull ahead on deep multi-step reasoning and hard coding problems, but for the routine 80% of the work the gap barely matters. The remaining hard 5% is usually handled with a hybrid setup — local by default, cloud for the edge cases.
How do we know which model fits our business?
You test it — on your own real tasks and documents, not on someone else’s benchmark scores. We run candidate models and compression levels against your scenarios and hardware as part of the rollout, and pick whichever one actually performs on your data.