Meta Releases Llama 3.3 70B: Best Open Model for Its Size

Meta has released Llama 3.3 70B, a new instruction-tuned model that represents a significant quality jump for the 70B parameter class. Using improved training data and refined RLHF, Llama 3.3 70B matches the original Llama 3.1 405B on several key benchmarks while being small enough to run on a single high-end consumer GPU.

Performance analysis

Llama 3.3 70B scores within one to two points of Llama 3.1 405B on MMLU, HumanEval, and GSM8K. It significantly outperforms the original Llama 3.1 70B across the board, with the most noticeable improvements in instruction following, safety alignment, and multilingual capabilities. The model supports 128K token context windows and handles long document analysis effectively.

Hardware requirements

The 70B model in Q4_K_M quantization requires approximately 42GB of memory. This means it fits on an RTX 5090 with 32GB VRAM using some system RAM offload, or runs entirely in memory on a Mac Studio M2 Ultra with 192GB unified memory. For pure GPU inference on NVIDIA hardware, a dual-GPU setup with two RTX 4090s or a single A100 80GB is ideal. The model runs at 8 to 12 tokens per second on suitable hardware.

Ecosystem support

Llama 3.3 70B works with all major local inference tools. Ollama added support on release day, and optimized GGUF quantizations from the community appeared within hours. The model uses the same tokenizer and chat template as Llama 3.1, so existing integrations and prompts work without modification.

Why this matters

Llama 3.3 70B demonstrates that model quality continues to improve at fixed parameter counts. A year ago, getting 405B-level quality required enterprise hardware. Now it fits on hardware that a dedicated enthusiast can afford. This trend of improving quality at smaller sizes is the most important development for local AI inference.