Phase transitions in large language model compression - Nature
<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE5GeEplb3VMeHJwUGJuM1pKMlJWUWg3eGpRenRlR3ViU3pZZ2ZCVDBsbXdOVm1VUDZOTTYwS1NMRXhCMHQ1UVlKTjh1Sll0NW1ydlhKZlpla3JvZnFMVWVj?oc=5" target="_blank">Phase transitions in large language model compression</a> <font color="#6f6f6f">Nature</font>
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modellanguage model![[P] Dante-2B: I'm training a 2.1B bilingual fully open Italian/English LLM from scratch on 2×H200. Phase 1 done — here's what I've built.](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-satellite-bUaXYHZsoMZjyA4XgfFqkD.webp)
[P] Dante-2B: I'm training a 2.1B bilingual fully open Italian/English LLM from scratch on 2×H200. Phase 1 done — here's what I've built.
The problem If you work with Italian text and local models, you know the pain. Every open-source LLM out there treats Italian as an afterthought — English-first tokenizer, English-first data, maybe some Italian sprinkled in during fine-tuning. The result: bloated token counts, poor morphology handling, and models that "speak Italian" the way a tourist orders coffee in Rome. I decided to fix this from the ground up. What is Dante-2B A 2.1B parameter, decoder-only, dense transformer. Trained from scratch — no fine-tune of Llama, no adapter on Mistral. Random init to coherent Italian in 16 days on 2× H200 GPUs. Architecture: LLaMA-style with GQA (20 query heads, 4 KV heads — 5:1 ratio) SwiGLU FFN, RMSNorm, RoPE d_model=2560, 28 layers, d_head=128 (optimized for Flash Attention on H200) Weight
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[P] Dante-2B: I'm training a 2.1B bilingual fully open Italian/English LLM from scratch on 2×H200. Phase 1 done — here's what I've built.
The problem If you work with Italian text and local models, you know the pain. Every open-source LLM out there treats Italian as an afterthought — English-first tokenizer, English-first data, maybe some Italian sprinkled in during fine-tuning. The result: bloated token counts, poor morphology handling, and models that "speak Italian" the way a tourist orders coffee in Rome. I decided to fix this from the ground up. What is Dante-2B A 2.1B parameter, decoder-only, dense transformer. Trained from scratch — no fine-tune of Llama, no adapter on Mistral. Random init to coherent Italian in 16 days on 2× H200 GPUs. Architecture: LLaMA-style with GQA (20 query heads, 4 KV heads — 5:1 ratio) SwiGLU FFN, RMSNorm, RoPE d_model=2560, 28 layers, d_head=128 (optimized for Flash Attention on H200) Weight
If Memory Could Compute, Would We Still Need GPUs?
If Memory Could Compute, Would We Still Need GPUs? The bottleneck for LLM inference isn't GPU compute. It's memory bandwidth. A February 2026 ArXiv paper (arXiv:2601.05047) states it plainly: the primary challenges for LLM inference are memory and interconnect, not computation. GPU arithmetic units spend more than half their time idle, waiting for data to arrive. So flip the paradigm. Compute where the data lives, and data movement disappears. This is the core idea behind Processing-in-Memory (PIM). SK Hynix's AiM is shipping as a commercial product. Samsung announced LPDDR5X-PIM in February 2026. HBM4 integrates logic dies, turning the memory stack itself into a co-processor. Is the GPU era ending? Short answer: no. But PIM will change LLM inference architecture. How far the change goes,
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