Llama 3.3 70B is Meta’s efficient dense 70B \u2014 close to 405B-class quality at a fraction of
\nthe size, with a 128K context. It needs about 40 GB of VRAM at 4-bit, so two RTX 4090s or a
\nsingle 48 GB card, and it is among the most-deployed open models for self-hosting.<\/p>\n
Dense is the operative word. Nearly every large open model released since is a
\nmixture-of-experts, which lowers inference compute but keeps the full parameter count
\nresident in memory \u2014 DeepSeek V4-Pro activates 49B parameters but still needs roughly 800 GB
\nof VRAM to hold. A dense 70B has no such gap: what you load is what you use, so 40 GB is the
\nwhole story. For teams sizing hardware, that predictability is worth a lot, and it is why
\n3.3 70B remains the default for on-premises deployments even as newer architectures post
\nbetter benchmark numbers. At $0.10 in \/ $0.32 out per million tokens the API is cheap enough
\nthat self-hosting only makes sense for data-residency reasons or at sustained high
\nvolume.<\/p>","protected":false},"excerpt":{"rendered":"
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