AI Weekly Malaysia

Aleph Alpha released Kolibri on 3 October 2026, an open-weight German/English mixture-of-experts LLM with 78.1B total parameters but only 3.46B active per token, under Apache 2.0 for the weights and config files (training code and methods stay proprietary). It was trained from scratch on ~24 trillion tokens — over a fifth German — on 768 NVIDIA B200 GPUs using infrastructure in Germany and Finland, with a 262,144-token native context (tested to 1,048,576), four reasoning levels, tool calling, a 18 June 2026 knowledge cutoff, and about 78 GB of FP8 weights. Aleph Alpha frames it as 'sovereign': built under European/German law with no foreign control, so customers get full deployment freedom and 'compliance as an inherited property', and it has signed the EU's GPAI Code of Practice. The 409-point Hacker News thread drew only 11 comments.

Why: The ~78 GB FP8 footprint means Kolibri can plausibly run on a single 80 GB accelerator rather than a cluster, which is the concrete difference between self-hosting and paying per-token to a US API. If you sell into the EU, handle data that cannot leave a client's building, or need tool-calling agents with a 262k context window, this is a deployable alternative — but the 'scores above every compared model of its size in both languages' claim comes from Aleph Alpha's own evaluation, so benchmark it yourself before committing. For Malaysian and SEA builders, the relevant lesson is the packaging: weights + license + no-foreign-control deployment story as a compliance argument, which is a template local sovereign-model efforts can copy.

Aleph Alpha released Kolibri, an English-German Mixture-of-Experts Transformer with 78B total parameters, 3B active, up to 1M tokens of context, published as full weights on Hugging Face under Apache 2.0. It was trained through the same pipeline as the earlier Kolibri Origin (30B total, 3B active, 65k context), and is specialized for German, reasoning, math, and agentic behavior, aimed at regulated sectors such as public administration, industrials, and aerospace. The announcement post contains no benchmark numbers, only a pointer to a separate tech report.

Why: A 3B-active MoE with a 1M-token window under Apache 2.0 is something you can realistically self-host and fine-tune without a licensing review, which makes it a candidate for on-prem or data-residency-constrained agentic workloads where you currently pay per-token API costs. The catch is that the post ships zero eval numbers and the specialization is German/English, so treat 'sovereignty' here as a marketing claim about training supply-chain provenance and deployment freedom until the tech report gives you something measurable against your own workload.