Vietnam's Sovereign AI Drive at NVIDIA AI Day Ho Chi Minh City: GreenMind LLM Debut and 50,000-Engineer Goal

Vietnam's sovereign AI push: what product teams need to do next

NVIDIA AI Day in Ho Chi Minh City signaled a clear direction: build locally, compete globally. For product leaders, the message was simple-move fast on Vietnamese-language AI, focus on real use cases, and invest in talent and data that you control.

National direction: data, talent, and policy

Vo Xuan Hoai from the National Innovation Center emphasized three priorities: Vietnamese-language datasets, a target of 50,000+ AI engineers, and policies to back enterprises and startups. The goal is to make AI a core growth engine for Vietnam within the next five years.

For product teams, that means planning for local data sources, compliance, and partnerships. Expect better access to datasets and talent; use it to shorten iteration cycles and ship more useful AI features.

Product lens from VNG: native value and speed

Le Hong Minh of VNG defined sovereign AI as building applications that are authentic and uniquely valuable to Vietnamese users. He pointed to two constraints: limited investment and gaps in research expertise and core platforms-so teams must prioritize concrete business outcomes.

Speed matters. VNG commercialized AI Cloud in six months, and after two years of testing, 20% of Zalo users use AI features. The guidance is clear: integrate AI where it feels natural for users, not as a sticker feature.

• Ship thin-slice features tied to one metric (e.g., reply rate, conversion, AHT).
• Inject AI into existing flows (search, support, onboarding) before adding new surfaces.
• Build feedback loops: collect user signals, fine-tune, redeploy weekly.
• Co-build with universities and startups to extend capacity without bloating headcount.

Vietnamese LLM on NVIDIA NIM: GreenMind goes live

GreenNode introduced GreenMind-Medium-14B-R1, an open-source Vietnamese LLM optimized for NVIDIA NIM and able to run on a single NVIDIA H100 Tensor Core GPU. It supports enterprise assistants, chatbots, retrieval, search, and Vietnamese reasoning tasks.

Vo Trong Thu highlighted an open ecosystem approach and the need for Vietnamese reasoning datasets grounded in local knowledge and culture. For teams, this lowers cost of adoption, enables on-prem or VPC deployment, and reduces dependence on foreign stacks.

• Start with a retrieval-augmented setup (RAG) for accuracy, governance, and cost control.
• Use small, frequent fine-tunes with human-in-the-loop review to improve responses.
• Create Vietnamese eval sets (reasoning, intent, tone, domain) and track win rates over time.
• Run pilots on a single H100 via NIM, then scale if unit economics clear.

Investing in core capabilities: VNG and Zalo AI

Alongside open-source work, VNG is training proprietary LLMs from scratch. Dr. Chau Thanh Duc underscored three levers for a sovereign model: high-quality data, large-scale deployment, and training methods adapted to limited resources.

Zalo AI operates dedicated R&D labs and continues to roll out AI products for Vietnamese users. The ambition is bold: within five years, every Vietnamese person uses messaging and intelligent assistant services built by Zalo.

• Decide where to build vs. integrate: core IP (reasoning, domain data) vs. plumbing (vector DB, orchestration).
• Stand up MLOps early: dataset versioning, eval pipelines, rollback plans.
• Plan for cost: mix open models for baseline tasks, switch to proprietary where differentiation pays.

Action checklist for product leaders

• Pick 3 use cases with clear ROI (agent assist, search, onboarding, fraud triage).
• Define success metrics and target thresholds (e.g., +10% CSAT, -20% handling time).
• Assemble a small task force: PM, data/ML, backend, infra, compliance, QA.
• Secure data access: clean, label, and version Vietnamese-language datasets.
• Prototype with GreenMind via NIM; compare against a closed model baseline.
• Set a 90/180/365-day plan: pilot, limited rollout, broad rollout with cost targets.
• Form partnerships with NIC, universities, and startups to speed experiments.
• Build a feedback loop: human review, auto-evals, weekly fine-tune, safe rollback.

Why this matters for your roadmap

Vietnam is investing in datasets, talent, and policies that make local AI viable now. With models like GreenMind on NIM and enterprise-scale efforts from VNG and Zalo, the path is open for teams to deliver useful AI features-fast and at a sustainable cost.

If your team needs structured upskilling to execute, explore role-based programs at Complete AI Training.