In April 2026, the LLM arena's main characters are undoubtedly large-scale MoE architectures — Llama 4 Scout, DeepSeek V4, Qwen3.6 series, all starting at hundreds of billions of parameters. But at exactly this point, IBM released Granite 4.1, taking the pure Dense route.
Granite 4.1 is a Dense decoder-only model family, available in 3B, 8B, and 30B sizes. The parameters aren't big, but the training scale isn't half-hearted either: 15T tokens, five-stage pretraining pipeline, where the fifth stage extends the context window in stages to 512K and adopts four-stage RLHF with DAPO loss.
More noteworthy is the 8B version's efficiency — it can match the previous-generation 32B MoE model's performance, showing that Dense architecture isn't inherently inefficient as long as training is sufficiently refined. The 30B version is deployable on a single H100, an attractive combination for enterprise users needing private deployment.
The real differentiator is data governance. IBM embeds GRC assessment at the pretraining data stage — users don't see this step, but it's significant for regulated industries like finance and healthcare.
That said, engineering rigor is only the threshold; production stability is the final validation.