Llama 3.1 405B Instruct

Llama 3.1 405B is the first openly-released model trained at GPT-4-class scale. The release was paired with a detailed 92-page technical report covering pretraining recipe, post-training, and infrastructure (including the failure-rate analysis on Meta's 16,384-H100 cluster, which became a reference point for anyone planning frontier training). On benchmarks it lands within reach of contemporaneous closed frontier models on MMLU and reasoning suites, which made it the first time researchers had access to a 400B-class checkpoint's weights for ablation studies and distillation experiments. Its practical deployment story is more constrained: at 810 GB in fp16, single-machine inference requires either multi-GPU sharding or fp8 quantization, and even at fp8 it pushes against the limits of single 8xH100 nodes. That cost-and-complexity ceiling is why the smaller 70B-class checkpoints (3.1 70B and the post-training-refreshed 3.3 70B) capture more production usage. The 405B's lasting impact is the published recipe and the synthetic data the larger checkpoint generated for post-training the 70B and 8B siblings, a pattern subsequent open-weights releases have copied.