Google Cloud A3 nodes can now run Meta's 405B Llama 3.1, but you'll need 8 H100s

If you want to run Meta’s monster 405-billion-parameter Llama 3.1 model without it melting your hardware, Google Cloud’s A3 instances are now a viable path—provided you use the FP8 quantized version. The math is pretty stark: a full-precision 405B model would need more than the 640GB of VRAM you get from a single node with eight H100 GPUs. The FP8 variant, however, squeezes in comfortably, and Hugging Face has partnered with Google to streamline the whole deployment pipeline on Vertex AI.

The process leans on Text Generation Inference (TGI) and Hugging Face’s purpose-built Deep Learning Containers, which means you’re not fumbling with Dockerfiles or CUDA versions. You can even skip the code entirely and deploy straight from the Hugging Face Hub or the Vertex AI Model Garden. But don’t click “deploy” just yet. The A3 machines, while generally available, aren’t something you just spin up on a whim. You’ll need to request a quota increase specifically for 8 H100 GPUs and 208 vCPUs under Vertex AI’s custom model serving, and availability is limited to certain zones.

Google’s A3 series itself is a flex—208 vCPUs and nearly two terabytes of system memory alongside those eight H100s. It’s the kind of hardware you’d use for serious training runs, but here it’s being positioned for inference, which says a lot about how resource-hungry these frontier models have become. The blog walks through the gcloud setup, API enablement, and the Python SDK initialization you’d expect, but the real value is in clarifying that the FP8 quantized checkpoint is the default recommendation.

This isn’t a plug-and-play SaaS product. You’re still managing cloud resources and cleaning them up afterward to avoid a nasty bill. But the partnership between Hugging Face and Google Cloud is making what was once a multi-node nightmare into a single-node, single-command deployment. For teams already committed to the Google Cloud ecosystem, this lowers the barrier to experimenting with a model that genuinely competes with GPT-4 class performance, without needing to hand your data to a third-party API.