What is the best Local LLM out there and for what use?
High agreement — the answer is well-supported across models.
Models agree on
- ✓No single 'best' local LLM; choice depends on hardware (VRAM) and use case.
- ✓Llama 3.1/3.2/3.3 (various sizes) and Gemma 2 (9B) are highly recommended for general purpose tasks across different hardware tiers.
- ✓DeepSeek-Coder-V2 and Qwen (especially Qwen 2.5) are leading choices for coding-specific tasks.
- ✓Ollama and LM Studio are consistently recommended as easy-to-use tools for running local LLMs.
Points of disagreement
- ~One model suggested DeepSeek R1 for complex reasoning/math, while the other primarily highlighted it for coding as part of DeepSeek-Coder-V2, thus having a different emphasis on its primary strength, and did not mention QwQ 32B for reasoning at all.
- ~The models disagreed on specific Llama versions and names, with one citing 'Llama 3.1 / 3.3' and 'Llama 3.2 3B', implying future or slightly different versions, while the other focused on 'Llama 3.1' specifically.
- ~There was a divergence in recommendations for creative writing/roleplay, with one specifically suggesting Nous Research's Hermes 3 and Command R+, while the other did not mention any models for this particular niche, focusing more on general writing/instruction following.
Choosing the 'best' local LLM is highly dependent on your specific hardware, especially GPU VRAM, and your intended use case. The landscape is rapidly evolving, with new top models appearing every few weeks. Currently, Meta's Llama 3.1 and Google's Gemma 2 are dominant, alongside specialized models.
1. Best Overall & General Purpose
For well-rounded performance in chatting, summarizing, and general reasoning, consider:
- ·Llama 3.1 (8B) or Gemma 2 (9B): These models are excellent for everyday tasks and fast responses on consumer hardware. Quantized versions (Q4/Q5) can run on modern PCs with 8GB–12GB of VRAM/RAM, making them highly accessible (MacBooks with Apple Silicon perform exceptionally well). The Llama 3.1 8B boasts a 128k context window, while Gemma 2 9B offers high-quality outputs that rival older 30B models.
- ·Llama 3.1 (70B): This is the current gold standard among open-weight models for high-level reasoning, complex analysis, and intelligence comparable to GPT-4. It requires substantial hardware, typically around 40GB of VRAM (e.g., dual RTX 3090/4090s or a Mac with 64GB+ Unified Memory).
2. Best for Coding and Development
If your primary need is code generation, debugging, or terminal assistance:
- ·DeepSeek-Coder-V2: This model is a strong performer across various programming languages. The Lite version (16B) is manageable on consumer GPUs (12GB–16GB VRAM), while the full 236B version requires enterprise or multi-GPU setups. As a Mixture of Experts (MoE) model, it excels on coding benchmarks, with a 128k context window to handle large codebases.
- ·Codestral 22B (by Mistral AI): Purpose-built for coding, it’s fast and fits well on high-end single consumer GPUs like an RTX 4090 or Mac Studio.
- ·Qwen 2.5 72B / Qwen 2.5 Coder 32B: The 72B variant is noted for strong coding and multilingual capabilities, while the 32B version offers excellent code performance at a more manageable size.
3. Best for Low-Resource / Older Hardware
For models running on basic laptops, phones, or older hardware without a dedicated GPU:
- ·Microsoft Phi-3.5 Mini (3.8B) / Phi-4 (14B): These models deliver surprisingly strong reasoning despite their small size. Phi-3.5 Mini runs on almost any modern laptop (4GB–8GB RAM) and offers fast performance on CPU-only machines. Phi-4 is also noted for punching above its weight. These are highly efficient.
- ·Llama 3.2 3B: Runs on almost anything, ideal for ultra-low resource environments.
4. Best for Creative Writing & Roleplay (Uncensored)
For unrestricted creative writing, roleplay, or uncensored brainstorming, look to fine-tuned models:
- ·Nous Research's Hermes 3 (Llama 3.1 8B / 70B): Built on Llama 3.1, Hermes 3 is designed for high steerability and neutral, unfiltered responses, lacking the typical "safety" guardrails of base models.
- ·Command R+ (104B): Excellent for long-form creative writing and extensive document analysis, though it requires high-end Mac or multi-GPU setups.
Quick Hardware-to-Model Guide
| Your VRAM/RAM | Recommended Model | Primary Use |
|---|---|---|
| 4GB–8GB | Phi-3.5 Mini (3.8B) / Llama 3.2 3B | Basic tasks, CPU inference, mobile |
| 8GB–16GB | Llama 3.1 (8B), Gemma 2 (9B), Mistral 7B, Qwen 2.5 14B | General tasks, light coding, writing |
| 24GB+ | DeepSeek-Coder-V2 (Lite), Gemma 2 (27B), Qwen 2.5 32B (Q4), DeepSeek R1 32B | Advanced coding, heavy reasoning |
| 48GB+ | Llama 3.1 (70B), Qwen 2.5 72B | Best overall intelligence, complex logic |
How to Run Local LLMs
Several free, open-source tools make running these models easy:
- ·Ollama: Generally the easiest setup, it's CLI-based and runs in the background across Mac, Linux, and Windows.
- ·LM Studio: Offers a user-friendly GUI, excellent for downloading different GGUF quantizations.
- ·AnythingLLM: Ideal if you want to use your local model for RAG (Retrieval Augmented Generation) or searching personal documents.
- ·llama.cpp: Provides maximal flexibility and is very lightweight.
- ·Jan: Another robust desktop application option.
Bottom Line
If you have the hardware, aim for Qwen 2.5 72B or Llama 3.1 70B for top-tier performance. For most enthusiasts, Qwen 2.5 32B or DeepSeek R1 32B offer an excellent balance. For low-end hardware, Phi-4 (14B) or Gemma 2 9B are remarkable choices.
Keep in mind the field of local LLMs is incredibly dynamic, with new breakthroughs frequently emerging.
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