Why choosing the right local LLM is crucial
In 2026, running language models locally is no longer reserved for experts. With tools like LM Studio, everyone can enjoy generative AI without sending their data to the cloud. But the success of your experience entirely depends on the chosen model.
An LLM poorly adapted to your hardware will result in: unbearable slowdowns, frequent crashes, or disappointing generation quality. This guide helps you avoid these pitfalls and maximize your machine's potential.
The 4 essential selection criteria
1. Your RAM (or VRAM) amount
The most determining factor. Here's the golden rule: a model needs approximately 1.2x its size in available RAM. This includes the GGUF file weight plus the memory needed for processing.
| Available RAM/VRAM | Max Model Size | Recommended Models |
|---|---|---|
| 4-6 GB | 3-4B parameters | Phi-4 Mini, Gemma 3 1B, Qwen 3 4B (Q4) |
| 8 GB | 7-8B parameters | Llama 3.3 8B, Qwen 3 8B, GLM 4.7 Flash, Mistral 7B |
| 16 GB | 13-14B parameters | Qwen 3 14B, GLM 4.5 Air, Step 3.5 Flash, DeepSeek 14B |
| 32 GB+ | 30-70B parameters | Qwen 3 32B, DeepSeek R1 32B, GLM 4.7, Kimi K2.5 |
| 64 GB+ | 70B+ parameters | DeepSeek V3.2, Trinity Large, Llama 3.3 70B (Q8), Qwen 3 32B |
💡 Apple Silicon Tip: On M1-M4 Macs, all RAM is "unified" and accessible for LLMs. A 36GB MacBook Pro M3 can comfortably run 32B models, which would be impossible on a Windows PC with an RTX 4090 (24GB VRAM max).
2. Quantization (Q4, Q5, Q8)
Quantization compresses the model by reducing number precision (from FP16 to INT4/5/8). The lower the number, the smaller the model but the less precise.
- Q4_K_M: Good size/quality balance. Recommended for 8-16GB RAM. Quality loss ~5-10%.
- Q5_K_M: Superior quality, medium size. Ideal for 16-32GB RAM. Quality loss ~3-5%.
- Q8_0: Nearly identical to the original model. Requires 32GB+ RAM. Quality loss ~1-2%.
- FP16: Maximum precision (unquantized). Professional use only with 64GB+.
3. Your use case
Each model excels in specific domains:
| Use Case | Best Models | Why? |
|---|---|---|
| General chat | Qwen 3, Llama 3.3 | Versatile, good in all domains |
| Programming | Qwen 2.5 Coder, CodeLlama | Trained on code, better completion |
| Reasoning | DeepSeek R1, Qwen 3 | Native chain-of-thought, excellent in math/logic |
| Vision (images) | Gemma 3 Vision, LLaVA 1.6 | Native multimodal, performant image analysis |
| French language | Qwen 3, Mistral | Better French language mastery |
4. Your operating system
Some models are optimized for specific platforms:
- macOS (Apple Silicon): All GGUF models work, but prioritize those with Metal support for GPU acceleration.
- Windows + NVIDIA: Ensure you have the latest CUDA drivers. CUDA-optimized models run 5-10x faster.
- Linux: The most flexible platform, all backends available (CUDA, ROCm, Vulkan).
Our picks by configuration
💻 Modest laptop (8GB RAM)
The most common configuration. You need to compromise on size, but quality remains excellent with the right choices.
- Recommended #1: Qwen 3 8B Q4_K_M — Best quality/weight ratio for general chat
- Recommended #2: Llama 3.3 8B Q5_K_M — Excellent in English, very good reasoning
- Alternative: Phi-4 Mini 3.8B Q8 — If you prioritize speed and need an ultra-lightweight model
🖥️ Powerful desktop PC (16GB RAM + RTX 3060/4060)
With 16GB and a recent graphics card, you can aim higher while maintaining good performance.
- Recommended #1: Qwen 3 14B Q5_K_M — Quality close to GPT-4 on many tasks
- Recommended #2: DeepSeek R1 14B Q4_K_M — Exceptional for complex reasoning
- Code specialist: Qwen 2.5 Coder 7B Q8 — High-quality quantized version for developers
🚀 High-end workstation (32GB+ RAM / Mac Pro)
"Pro" configuration allowing you to run the largest local models with optimal quality.
- Recommended #1: Qwen 3 32B Q5_K_M — One of the best open-source LLMs, GPT-4 competitor
- Recommended #2: DeepSeek R1 32B Q4_K_M — Excellence in reasoning, mathematics, code
- Best of the best: Llama 3.3 70B Q4_K_M — If you have 48GB+ RAM, this is the summit
How to test a model?
Theory is good, practice is better. Here's how to quickly evaluate an LLM:
- Download via LM Studio: Use the "Search" button and filter by size matching your RAM.
- Test these typical prompts:
- "Explain string theory like I'm 10 years old" (pedagogy test)
- "Solve this equation: 3x² + 5x - 2 = 0" (reasoning test)
- "Write a Python function that sorts a list of dictionaries by value" (code test)
- "Summarize this text in 3 sentences: [paste a 500-word article]" (compression test)
- Evaluate speed: A good indicator is the "tokens/second" displayed by LM Studio. Above 20 tok/s is fluid, above 50 tok/s is very responsive.
Common mistakes to avoid
❌ Don't do this:
- Download a 70B model with 8GB RAM (it won't work or will be horribly slow)
- Ignore quantization and take FP16 by default (unless you have 64GB+)
- Choose a model based only on hype without checking recent benchmarks
- Forget to configure GPU offload in LM Studio (leaving everything on CPU = very slow)
Conclusion
Choosing the right local LLM is a balance between your hardware, your needs, and size constraints. In 2026, models like Qwen 3 and Llama 3.3 offer performance rivaling paid APIs, provided you select the right version.
Use LocalClaw to get a personalized recommendation based on your exact configuration. In 30 seconds, you'll know which model to download and how to configure it for optimal performance.