Table of Contents

Once you have an environment to run a local LLM, the next question is: "Which model should I actually install?" Llama, Qwen, Gemma, DeepSeek—there are many names, and the companies and countries behind them differ too. This article organizes the major 2026 models by developer, country of origin, use case, size, and license, so you can pick the "first one" that fits your PC and goals.

One key premise first. Open models update very fast (versions keep climbing under the same name). So this article is built around "families (lineages) + how to choose by use case." That way, the thinking holds even when a new version drops. Always confirm the latest version and license at the distributor (Ollama / Hugging Face).

LOCAL LLM · MODELS

Not "the strongest one," but "the right one for you"

— Developer, country, use case, and size narrow it down

🇺🇸

USA

Llama / Gemma / Phi

🇨🇳

China

Qwen / DeepSeek / GLM

🇪🇺

Europe

Mistral / Teuken

🌍

& more

UAE / India / Japan

1. The bottom line: no single winner—choose by use × size (+ origin)

The conclusion up front: there is no all-purpose model that "you just install and you're done." For local, narrow it down on these three points.

💡 Three axes for choosing: ① Size (the ceiling that fits your VRAM) = the cap on candidates. ② Use case (general, coding, your language, reasoning) = which lineage fits. ③ Country of origin / developer (license, procurement policy, language strengths) = not ignorable if you use it at work.

2. The main model families (with developer & country)

The 2026 local-LLM scene comes down to a few major families (lineages). Knowing who builds them, and in which country, makes choosing much easier. First, two terms that appear in the cards below.

📖 Quick glossary

B (parameter count) = the unit for a model's scale. "B" means "billion," so 7B = 7 billion, 70B = 70 billion parameters. Bigger tends to be smarter, but heavier (uses more VRAM).

MoE (Mixture of Experts) = instead of running everything every time, only some "experts" activate per input. So the total size can be huge while the part that actually runs stays light and efficient.

Qwen

🇨🇳 Developer: Alibaba (China) / mostly Apache 2.0

High all-round ability and strong in CJK (Chinese/Japanese/Korean). Sizes span 3B to hundreds of B (MoE), with coding-specialized variants. A first choice for many. Example: Qwen3 series.

Llama

🇺🇸 Developer: Meta (USA) / custom license (check it)

The most widely adopted, information-rich staple. Plenty of examples and know-how, so it's easy to look things up. A stable generalist. Example: Llama 3.x / 4 series.

Gemma

🇺🇸 Developer: Google (USA) / Gemma license

Lightweight and efficient, with high quality even at small-to-mid sizes. Multimodal variants exist. A strong pick for low-spec PCs. Example: Gemma 3 series.

DeepSeek

🇨🇳 Developer: DeepSeek (China) / R1 is MIT etc.

Strong at reasoning and coding. Distilled small versions exist, so you can chase "smarts" on limited VRAM. Example: DeepSeek-R1 / V3 series.

Mistral

🇫🇷 Developer: Mistral AI (France / Europe)

Mid-sized, snappy and well-balanced. A flag-bearer of Europe's "sovereign AI." Smaller ones are often Apache 2.0. Example: Mistral Small, etc.

Phi

🇺🇸 Developer: Microsoft (USA) / MIT

A small-model specialist (SLM) whose selling point is being smart despite being tiny. Easy to run on weak PCs/laptops at the 8 GB class—ideal for getting started. Example: Phi-4 series.

Beyond these, there's GLM (🇨🇳 Zhipu AI, from Tsinghua—highly rated for coding), Falcon (🇦🇪 UAE's TII), and Command (🇨🇦 Cohere—good for RAG). Start from the major lineage closest to your use case.

3. What changes by country of origin?

"Which country's model" creates practical differences you can't see from performance alone. To avoid a common misunderstanding, start with the key premise.

✅ The key premise: as long as you run it locally, your input data is not sent out (to the developer's country). That's the biggest benefit of a local LLM. So "a Chinese model = your input goes to China" is not true (it's different from a cloud API). Origin matters mainly in the three points below.

⚖️

License & commercial terms

Terms differ by developer. Apache 2.0 / MIT are permissive; custom licenses may restrict scale, use, or require attribution. Check before product use.

🏛️

Organizational / government policy

Government bodies and large firms may have rules on "whether AI from a given country is allowed." Treat it as a procurement / compliance point to confirm.

🗣️

Language & cultural strengths

Training-data tendencies shape which languages a model is good at. Chinese models are strong in CJK; locally built models often win on their own language's nuance.

A rough "national character": 🇺🇸 USA = the largest ecosystem, info-rich, generally easy to work with. 🇨🇳 China = ahead on performance and efficiency, many permissive licenses, but some organizations need to check adoption policy. 🇪🇺 Europe = a regulation-minded "sovereign AI" stance, balanced. Other regions = models tuned to their own language (next section).

4. Sovereign & local-language models around the world

If you mainly work in a language other than English, models built or tuned for your language/region are worth a look. They tend to win on the naturalness of that language, and they're easier to adopt for organizations with a "sovereign AI" preference. Here's a regional tour of notable open efforts.

🇪🇺 Europe

Mistral & Lucie (France), Teuken-7B (OpenGPT-X, trained on all 24 EU languages), Salamandra / ALIA (Spain, by the Barcelona Supercomputing Center), Aleph Alpha (Germany).

🇦🇪 Middle East

Falcon (UAE's TII) and Jais (UAE, G42/MBZUAI) for Arabic, plus ALLaM (Saudi Arabia's SDAIA). Strong Arabic-first models.

🇮🇳 India

Sarvam / OpenHathi, Krutrim (by Ola), and BharatGPT (CoRover) cover many Indian languages—a fast-growing "sovereign AI" scene.

🇯🇵 Japan & East Asia

Japan: ELYZA (Llama with Japanese tuning), PLaMo, Sarashina. China's Qwen/DeepSeek/GLM (above) double as that region's domestic models.

💡 Rule of thumb: for pure all-round power, a global family like Qwen; if you prioritize your language's naturalness, sovereignty requirements, or explainability for public/business use, a local/regional model. Try both on the same prompt to compare (verify versions and commercial terms at each distributor).

5. Recommendations by size (concrete models)

Your VRAM decides the range you can run. Here are the "sweet spots" per size band, with concrete examples (all assuming Q4 quantization).

~4B (tiny)

VRAM ~6 GB / entry & laptops

Phi-4 mini, Gemma 3 4B, Qwen3 4B, Llama 3.2 3B, etc. For chat, summarizing, light work. Start here.

7B–14B (standard)

VRAM 8–12 GB / daily driver

Qwen3 7B/14B, Llama 8B, Gemma 12B, etc. Best balance of quality and lightness. A great first everyday model.

32B class (upper)

VRAM 24 GB / solid real use

Qwen Coder 32B, mid-sized Mistral, DeepSeek distills, etc. Dependable quality for coding and involved work.

70B+ (serious)

VRAM 40 GB+ / big-memory Mac · multi-GPU

Llama 70B, large DeepSeek, ELYZA-JP 70B, etc. Quality approaching mid-tier cloud.

6. Recommendations by use case

Choose the lineage by "what you want it for." Here are the lineages that fit typical use cases.

🧩 General / anything

Qwen (🇨🇳) or Llama (🇺🇸). When unsure, start from a size variant of these two. Lots of info, hard to go wrong.

💻 Coding

Qwen Coder, DeepSeek, GLM (all 🇨🇳 strengths). Quality jumps if a 32B class fits.

🌐 Your language / multilingual

Qwen (strong CJK) or a local/regional model tuned to your language (see section 4). For naturalness, the regional pick often wins.

🧠 Reasoning / thinking

DeepSeek reasoning models, or "thinking"-enabled variants of each lineage. Strong on hard problems and planning.

🪶 Low-spec / lightweight

Phi (🇺🇸) or Gemma (🇺🇸) small models, or Qwen/Llama 3–4B. Snappy even at the 8 GB class.

📚 Long documents

A lineage with long context length (e.g., long-context Llama variants). Watch the memory cost though.

💡 What works for most: starting from "the largest Qwen that fits your VRAM"—or a regional model in your language—rarely disappoints. If it falls short, move to a specialized variant (coder, etc.) or a larger size.

7. Licensing (commercial use) cautions

If you use it for work or in a product, licensing is not to be missed. Even "open" comes with different terms. Always confirm commercial use and conditions at the distributor.

✅ Permissive (easy for commercial)

Apache 2.0 / MIT family (e.g., Qwen, Gemma※, Phi, much of DeepSeek). Easy commercial use, high freedom to embed in products.

⚠️ Custom terms

Some use custom licenses (scale limits, use restrictions, attribution). The Llama license and Gemma license have clauses to check. Read them before commercial use.

8. A selection flow and getting started

Putting it all together, choosing is three steps.

  1. Decide the size: from your VRAM ceiling, pick the largest size that fits (see the hardware requirements article).
  2. Pick the lineage by use case + origin: general = Qwen/Llama, coding = Qwen Coder/DeepSeek/GLM, your language = Qwen/regional models, lightweight = Phi/Gemma. For commercial use, also cross-check license and procurement policy.
  3. Download one and test: if it falls short, go one size up or to a specialized variant. Comparing several on the same prompt is the fastest way.

💡 Getting started is easy: with Ollama or LM Studio, you just pick a model name and download (e.g., ollama pull qwen3—a few minutes). Install several and compare them on the same question to quickly find your fit.

Summary

Choosing a local-LLM model comes down to three points.

  • No all-rounder; choose on three axes: size (VRAM ceiling) × use case × country of origin (license, procurement, language).
  • Remember by lineage + country: Qwen/DeepSeek/GLM (🇨🇳), Llama/Gemma/Phi (🇺🇸), Mistral (🇫🇷), plus regional models for your language (🇪🇺🇦🇪🇮🇳🇯🇵…). Versions move fast, so track by lineage.
  • Local means input doesn't leave: origin matters mainly for license, procurement policy, and language strengths. For commercial use, checking the license is a must.

When unsure, start from "the largest Qwen that fits your VRAM"—or a regional model in your language. Then run it, feel the difference from the cloud, and converge on the one that fits your use best. For setup steps, see how to run a local LLM.

FAQ

Q. So which should I install first?

A. "The largest Qwen (China, Alibaba) that fits your VRAM," or a model tuned to your own language, is a safe start—good balance of all-round ability, multilingual support, and size range. If lightness is the priority, the small Phi (Microsoft, USA) or Gemma (Google, USA) pair well too.

Q. If I use a Chinese model, does my input get sent to China?

A. No. As long as you run it locally, your input is never sent anywhere (it stays on your PC). That's the decisive difference from a cloud API. Origin relates mainly to license (commercial terms), organizational procurement policy, and language strengths—not where your data goes.

Q. Which local model is good for my language?

A. Qwen (strong CJK) is a safe default. For more natural output in your own language—nuance, honorifics, cultural context—a regional/sovereign model built for it (see section 4) is a strong option. Try both for your use case and compare.

Q. Are small models actually usable?

A. Plenty, depending on the task. For daily work like chat, summarizing, drafting, and classification, a 3–7B class runs comfortably. The more complex the reasoning or the longer the context, the more a larger size helps.

Q. What should I watch for when using it at work?

A. License and procurement policy are the top priorities. Apache 2.0 and MIT are easy for commercial use, while custom licenses (Llama license, Gemma license, etc.) may carry conditions on scale, use, or attribution. Some organizations also restrict AI by country of origin, so confirm both the distributor's terms and your internal rules before embedding it in a product.