Why Volcano Engine Says Multi‑Model Strategy Is the Future of AI

Large Models: A Qualitative Leap

When viewed over a longer time horizon, the surge of large models is both inevitable and a result of quantitative change leading to qualitative transformation. Early breakthroughs such as the 2017 Transformer, followed by BERT and GPT‑1/2/3, set the stage. OpenAI identified the "scaling law"—predictable loss given compute and data—and invested heavily, solving knowledge compression, alignment with human preferences, and prompting, driving a gradual yet transformative progress.

The Future Pyramid Structure of Large Models

Tan Dai predicts a pyramid hierarchy: a few ultra‑strong models at the top, many medium‑capability models, and numerous specialized vertical models. Training thresholds have risen dramatically—from a few GPUs to thousands—making it difficult for small firms, but vertical domains can fine‑tune general models using domain‑specific data and still achieve strong results.

Enter the Multi‑Model Era for Enterprises

Enterprises will adopt a multi‑model layout, selecting the most cost‑effective model for each scenario—whether building from scratch, fine‑tuning a base model, or using prompt engineering. Security, trust, and data privacy become critical, requiring sandboxing, trusted hardware, and federated learning solutions that Volcano Engine provides.

Cloud Still Faces Major Challenges

Even for cloud‑first players, the large‑model era introduces challenges: high training and inference costs, scaling infrastructure, and ensuring reliable, secure services. Volcano Engine positions itself as a cloud platform that solves these issues through massive scale, cost efficiency, and sustainable technology.

Prioritizing Cloud Excellence

Cloud success depends on massive scale to lower costs and enable digital innovation. Volcano Engine emphasizes cost‑performance, leveraging its internal resources to continuously reduce expenses while maintaining high‑quality AI services.

Good Technology Will Find Its Buyers

Volcano Engine’s early insight into the shift from small to large models gave it ample compute resources. It can support training at thousands‑of‑GPU scale, offering stable, large‑scale cloud services that many model vendors rely on for rapid cold‑start and production deployment.

Conclusion

The heat around large models continues; strategic choices will determine success. Those who grasp deeper logic may bring surprising breakthroughs.