Sector-specific AI playbooks across 10 industries we know cold.
View all industries →Three GPU cloud models — reserved dedicated compute, distributed marketplace, and serverless inference — each optimise for different APAC AI workload patterns. This guide maps Lambda Labs, Vast.ai, and Inferless to training, research, and inference use cases with APAC cost scenarios and a decision matrix.
Beyond this insight
If this article matches your stage of thinking, the underlying capabilities ship across all six pillars, ten verticals, and nine Asian markets.
vLLM is the default starting point for APAC self-hosted LLM serving, but three specialized frameworks outperform it in specific scenarios: SGLang for structured output APIs (3-5× throughput), TensorRT-LLM for maximum NVIDIA H100 utilization (up to 2.5× faster), and LMDeploy for APAC-language models like Qwen and InternLM. This guide maps each framework to APAC workload patterns with cost scenarios.
BlogBase LLMs require three post-training stages before APAC production deployment: alignment fine-tuning, reproducible experiment management, and objective benchmarking. TRL implements SFT and DPO alignment; Axolotl abstracts multi-GPU training into YAML configs; LM Evaluation Harness provides standardized benchmarks including APAC multilingual tasks. This guide covers the complete APAC post-training workflow.
BlogAPAC regulators (MAS FEAT, JFSA, APRA) increasingly require ongoing model monitoring, fairness documentation, and regulatory explainability — not just pre-deployment validation. This guide explains how Arthur AI, Alibi Detect, and TruEra address production monitoring, open-source drift detection, and regulatory compliance documentation for APAC enterprise AI programs.
We use these frameworks daily in client engagements. Let's see what they look like for your stage and market.
