How would you design a 70B hybrid model for an agentic...

For a 70B model with 64k context, a hybrid architecture is optimal to balance memory constraints and factual recall. By placing full-attention layers at positions 4, 8, 16, 20, 24, and 28, you concentrate the model’s capacity for cross-document reasoning where it is most needed. With this configuration, the KV cache for the full-attention layers is approximately 16 GB, and the linear state for the remaining layers is roughly 384 MB. Given the 140 GB weight footprint in bf16, this design can be served using tensor parallelism across 2-4 H100 GPUs, keeping the KV cache within tractable limits per node.