For most of the generative AI boom, someone in North Sumatra typing a question in Batak Toba has been talking to a machine that, at best, half understands them. GoTo Group says it wants to change that. But the hard part is only beginning.
Indonesia’s largest digital ecosystem on Monday released a 27-billion-parameter language model trained for Bahasa Indonesia and a clutch of regional languages, unveiling it at Bali Tech Week and publishing the weights on its official Hugging Face repository.
The instruction-tuned model sits on Google’s Gemma 3 architecture and handles six languages: English, Indonesian, Javanese, Sundanese, Batak Toba and Balinese. It also comes with a 128,000-token context window, which, in plain terms, means it can read a lengthy contract or a long customer-service conversation in one go rather than in chopped-up pieces.
Also Read: Google bets on schools, telcos and local languages to scale Gemini in Southeast Asia
The repository names GoTo as the initiator, and GoTo and AI Singapore as co-developers. That cross-border credit matters. It places the release inside a broader regional push, in which AI Singapore has been a recurring anchor, to fix a problem global models have never taken seriously: Southeast Asia’s languages are spoken by hundreds of millions of people, yet treated as an afterthought in most training data.
“Open source”, with an asterisk
GoTo calls the model open source. The model card tells a slightly different story: the governing licence is Google’s Gemma licence, not a standard open-source licence such as Apache 2.0 or MIT. Gemma’s terms permit broad use and modification, but they come with Google’s own usage restrictions.
For a hobbyist, the distinction is academic. For a bank, a telco or a government agency planning to embed the model into a product, it is the first thing the legal team will flag.
This is not a GoTo-specific sin. Much of what the industry markets as “open” AI is really “open weights”: you can download and tinker, but someone else still writes the rules.
Still, for a release explicitly framed around reducing dependence on foreign platforms, building on a licence controlled by a US tech giant is an irony worth noting. It sharpens a question regional commentators have been raising for months: whether Southeast Asia can build sovereign AI on borrowed choices.
From Sahabat-AI to a leaner bet
The release is not GoTo’s first foray into homegrown language models. In 2024, the company and Indosat Ooredoo Hutchison launched the Sahabat-AI initiative, which later produced a 70-billion-parameter model and a multilingual chat service. GoTo positions the new model as part of that broader effort rather than a replacement for everything that came before.
The shift to a smaller 27-billion-parameter model looks pragmatic. Bigger is not always better when your target users are Indonesian universities and startups, not hyperscalers. The timing is also telling. GoTo only recently posted its first quarterly net profit after years of restructuring and subsidy cuts, and its leadership under CEO Hans Patuwo has every incentive to show investors that AI is a strategic asset, not just a cost centre.
On training data, GoTo says it drew on Indonesian-language text from media partners and public-sector websites, and worked with Universitas Sumatera Utara on Batak-language material. None of this has been independently audited. That gap matters beyond accuracy. As newsrooms globally fight over whether, and on what terms, their journalism should feed AI systems, the precise nature of GoTo’s arrangements with its media partners deserves more transparency than a single line in an announcement.
The 60GB question
Here is where the “AI for everyone” pitch meets the server room. According to the model card, running the full-precision model requires roughly 54GB to 60GB of video memory. The suggested hardware is one Nvidia A100 80GB card, two Nvidia L40S cards or three RTX 4090s.
Translation: this is not something a two-person startup in Yogyakarta runs on a gaming laptop. Most smaller organisations will need cloud infrastructure, specialised servers, or a compressed, lower-precision version that trades some quality for affordability. GoTo’s repository offers deployment guidance for popular frameworks such as vLLM and Hugging Face Transformers, which helps, but it does not lower the bill.
Also Read: Why Singapore, Indonesia, and Vietnam are losing the AI race they think they are winning
That makes Indonesia’s compute strategy part of the story. Jakarta has been courting Nvidia and AWS to build out domestic chip and cloud capacity. A local-language model is only as sovereign as the infrastructure it runs on, and if most deployments end up on foreign-owned clouds anyway, the independence argument weakens considerably.
Benchmarks without a referee
The model card lists Indonesian and Southeast Asian evaluation suites, including IndoMMLU and SEA-HELM. But GoTo has not published independently verified comparisons against rival models, so claims of superior local-language performance remain claims. The repository itself is refreshingly candid: it warns that the model can generate inaccurate or misleading information, may stumble on reasoning-heavy tasks, and should not be treated as authoritative without verification. Safety testing and safeguards, it adds, are the developer’s responsibility.
A crowded field of local champions
GoTo is entering a busy arena. AI Singapore’s own SEA-LION family has long targeted regional languages, Thailand’s SCB 10X has pushed its Typhoon models for Thai, and Malaysia has made local context a sovereign AI priority.
Above them all sit global giants (Google, Meta, Alibaba) whose general-purpose models keep improving in Southeast Asian languages, partly because regional users increasingly prompt them in Vietnamese, Thai and Indonesian. GoTo’s edge, if it has one, lies in lower-resource languages such as Batak Toba and Balinese, where global players have little commercial reason to invest.
What it adds up to
GoTo frames the release as infrastructure for Indonesia’s long-term digital agenda, a foundation universities, startups and public institutions can build on without shipping every workload abroad. The ambition is worth applauding, particularly for languages that the AI industry has historically ignored.
Also Read: Featherless AI secures backing to make open-source models viable at enterprise scale
But access to weights is not adoption. Teams will still need a licensing review, suitable hardware, clean application data and rigorous testing in the dialects and domains they actually serve. GoTo has opened the door. Whether Indonesia’s developers can afford to walk through it is the real test.
The post GoTo’s 27B model speaks Batak, but the real test is who can afford to run it appeared first on e27.