Can I run Llama 3.3 70B?

Llama 3.3 70B is the model you reach for when an 8B just isn't smart enough and you have the hardware to back it up. It's a 70.6-billion-parameter dense chat model, and the footprint reflects that: even at a 4-bit quant it lands around 42.8 GB, and you want at least 64 GB of system memory to load it with any breathing room. Drop to a 2-bit quant and it shrinks to roughly 29.6 GB, but the realistic home for this model is a high-memory Apple Silicon machine or a multi-GPU box, not a single consumer card.

In daily use the honest caveat is speed. On an M-series Max you're looking at around 8 tokens per second at 4-bit, which is readable but slow enough that you feel every long answer, and on a CPU with DDR5 it crawls at roughly 1 token per second. The 128K context window is genuinely large, but filling it is expensive: at full 128K the total memory footprint climbs to about 87.5 GB, well past the 42.8 GB the weights alone need. Keep working context modest unless you have memory to spare.

Note that the listed RTX 3060 and RTX 4090 simply don't fit this model in 4-bit, so a single mainstream GPU is out. Against DeepSeek R1 70B, which shares the same parameter count, R1 is the reasoning specialist and generally pulls ahead on hard multi-step math, while Llama 3.3 70B is the more reliable, broadly compatible general-purpose chat model. Its standout trait is delivering near-flagship conversational quality from open weights. One caveat: the Llama Community license is open-weight, not true open source, so check Meta's terms before any commercial deployment.