As we step into 2025, local Large Language Models (LLMs) have seen remarkable advancements. The landscape is now populated with robust options that cater to various needs, from casual use to serious applications in business and research. This article delves into the top ten local LLMs available today, focusing on their context windows, VRAM targets, and licensing, to help you make informed decisions.
1. Meta Llama 3.1-8B: The Daily Driver
Meta’s Llama 3.1-8B stands out as a reliable choice for everyday applications. With a context length of 128K tokens, it offers multilingual support and is well-optimized for local toolchains.
- Specs: Dense 8B decoder; instruction-tuned variants available.
- VRAM Requirements: Typically runs on Q4_K_M/Q5_K_M for ≤12-16 GB VRAM; Q6_K for ≥24 GB.
2. Meta Llama 3.2-1B/3B: The Compact Option
For those needing a lighter model, the Llama 3.2 series offers 1B and 3B options that still support a 128K context. These models are designed to run efficiently on CPUs and mini-PCs.
- Specs: Instruction-tuned; works well with llama.cpp and LM Studio.
3. Qwen3-14B / 32B: The Versatile Performer
Qwen3 is notable for its open-source license under Apache-2.0 and strong multilingual capabilities. Its community-driven development ensures regular updates and improvements.
- Specs: 14B/32B dense checkpoints; modern tokenizer.
- VRAM Requirements: Starts at Q4_K_M for 14B on 12 GB; Q5/Q6 for 24 GB+
4. DeepSeek-R1-Distill-Qwen-7B: Reasoning on a Budget
This model offers compact reasoning capabilities without demanding high VRAM. It’s distilled from R1-style reasoning traces, making it effective for math and coding tasks.
- Specs: 7B dense; long-context variants available.
- VRAM Requirements: Q4_K_M for 8–12 GB; Q5/Q6 for 16–24 GB.
5. Google Gemma 2-9B / 27B: Quality Meets Efficiency
Gemma 2 is designed for efficiency, offering a strong quality-to-size ratio with 8K context. It’s a solid mid-range choice for local deployments.
- Specs: Dense 9B/27B models; open weights available.
- VRAM Requirements: 9B@Q4_K_M runs on many 12 GB cards.
6. Mixtral 8×7B: The Cost-Performance Champion
Mixtral employs a mixture-of-experts approach, optimizing throughput during inference. This model is best suited for users with higher VRAM needs.
- Specs: 8 experts of 7B each; Apache-2.0 licensed.
- VRAM Requirements: Best for ≥24–48 GB VRAM or multi-GPU setups.
7. Microsoft Phi-4-mini-3.8B: Small but Mighty
The Phi-4-mini model combines a small footprint with impressive reasoning capabilities, making it ideal for latency-sensitive applications.
- Specs: 3.8B dense; supports 128K context.
- VRAM Requirements: Use Q4_K_M on ≤8–12 GB VRAM.
8. Microsoft Phi-4-Reasoning-14B: Enhanced Reasoning
This model is specifically tuned for reasoning tasks, outperforming many generic models in chain-of-thought scenarios.
- Specs: Dense 14B; context varies by distribution.
- VRAM Requirements: Comfortable on 24 GB VRAM.
9. Yi-1.5-9B / 34B: Bilingual Capabilities
Yi offers competitive performance in both English and Chinese, making it a versatile option under a permissive license.
- Specs: Context variants of 4K/16K/32K; open weights available.
- VRAM Requirements: Q4/Q5 for 12–16 GB.
10. InternLM 2 / 2.5-7B / 20B: Research-Friendly
This series is geared towards research and offers a range of chat, base, and math variants, making it a practical target for local deployment.
- Specs: Dense 7B/20B; active presence in the community.
Summary
When selecting a local LLM, consider the trade-offs carefully. Dense models like Llama 3.1-8B and Gemma 2-9B/27B provide reliable performance with predictable latency. If you have the VRAM, exploring sparse models like Mixtral 8×7B can yield better performance per cost. Additionally, understanding licensing and ecosystem support is crucial for long-term viability. Choose models based on context length, licensing, and hardware compatibility to ensure you meet your specific needs.
FAQs
- What are local LLMs? Local LLMs are large language models that can be deployed and run on local hardware, offering greater control and privacy.
- How do I choose the right local LLM for my needs? Consider factors like context length, VRAM requirements, and licensing options based on your specific applications.
- What is the significance of context length? A longer context length allows the model to understand and generate more complex responses by considering more input data.
- Are open-source models better than proprietary ones? Open-source models often provide more flexibility and community support, while proprietary models may offer optimized performance.
- What role does VRAM play in LLM performance? VRAM is crucial for running larger models efficiently; insufficient VRAM can lead to slower performance or inability to run the model.
