Run Llama 3.3 70B on Mac: Faster & Smarter Than Ollama & LM Studio
✨ Just Released: EVA Llama 3.3 70B, Llama 3.3 70B Euryale v2.3, and Llama 3.3 70B Uncensored for Role-Play, Story Writing, and Uncensored built on the Llama 3.3 70B base model.
Private LLM v1.9.4 for Mac now supports the powerful Meta Llama 3.3 70B Instruct model. Run this advanced AI model locally on your Apple Silicon Mac for maximum privacy and performance.
What Is Llama 3.3 70B?
Meta's Llama 3.3 70B matches the capabilities of larger models through advanced alignment and online reinforcement learning. It delivers top-tier performance while running locally on compatible hardware.
System Requirements:
To run Llama 3.3 70B locally, you need:
- Apple Silicon Mac (M-series)
- 48GB RAM minimum
If you meet these requirements, you can leverage the model's strong performance without relying on the cloud—perfect for developers who value data privacy and direct control over their workflow.
Benchmarks: Llama 3.3 70B vs. Other Models
Llama 3.3 70B shows strong results across a variety of benchmarks:
Category | Benchmark | Llama 3.1 70B | Llama 3.3 70B | Llama 3.1 405B | GPT-4o |
|---|---|---|---|---|---|
General Reasoning | MMLU Chat (0-shot, CoT) | 86.0 | 86.0 | 88.6 | 87.5 |
Instruction Following | IFEval | 87.5 | 92.1 | 88.6 | 84.6 |
Code | HumanEval (0-shot) | 80.5 | 88.4 | 89.0 | 86.0 |
Mathematical Tasks | MATH (0-shot, CoT) | 67.8 | 77.0 | 73.9 | 76.9 |
Multilingual Support | Multilingual MGSM (0-shot) | 86.9 | 91.1 | 91.6 | 90.6 |
- General Reasoning (MMLU Chat 0-shot): Matches Llama 3.1 70B at 86.0, approaching the performance of much larger models like Llama 3.1 405B.
- Instruction Following (IFEval): Scores 92.1, outperforming Llama 3.1 405B (88.6), indicating notable gains in understanding and following complex instructions.
- Coding Tasks (HumanEval 0-shot): Achieves 88.4, ranking among top-performing models for code completion and understanding.
- Mathematical Reasoning (MATH 0-shot, CoT): Scores 77.0, surpassing previous Llama 3.1 series models.
- Multilingual Support (Multilingual MGSM 0-shot): Hits 91.1, handling multiple languages with confidence.
While the model excels in general reasoning, instructions, coding, and multilingual capabilities, there's still room to improve in certain reasoning and tool use benchmarks. Overall, Llama 3.3 70B is a strong choice for a wide range of tasks.
Llama 3.3 70B on Ollama / LM Studio vs Private LLM
Private LLM makes it simple to run Llama 3.3 70B offline—no internet connection needed. This ensures that your data stays on your device, enhancing privacy and control. By integrating OmniQuant quantization, Private LLM speeds up model inference without sacrificing accuracy. This means you can enjoy top-tier performance even when working entirely offline.
If you've tried Llama 3.3 70B on Ollama, LM Studio, or other Llama.cpp/MLX wrappers using naive RTN quantization, you'll notice a difference with Private LLM. OmniQuant quantized models do much better compared to Q4_K_M GGUF quantized models used by llama.cpp based apps like Ollama, and 4-bit RTN quantized MLX models used by MLX based apps like LM Studio (only on macOS, LM Studio currently uses llama.cpp for inference on all other platforms).
Private LLM sets a higher standard for offline model performance, surpassing Ollama and LM Studio in both speed and text output. By running Llama 3.3 70B on Private LLM, you're not just keeping your data local—you're also taking advantage of better performance and advanced quantization that helps you get the most out of your hardware.
Don't just take our word for it—compare for yourself.
Ready to experience Llama 3.3 70B locally on your Mac?
Download Private LLM v1.9.4 from the App Store and give the latest model a spin. Enjoy the benefits of a subscription free local AI chatbot running entirely offline, right on your own hardware.
