AI Infrastructure & GPU Compute in Thailand
GPU clusters, ML pipelines, and scalable compute built for serious AI workloads. We design and operate the infrastructure that trains and serves your models — on-premise or cloud — for organizations across Thailand.
GPU compute and MLOps platforms sized to what you actually run, deployed on-premise where data sovereignty requires it. For Thai government agencies and regulated sectors that constraint usually decides the architecture before any performance question is asked.
Key Features
GPU Clusters
Provision and manage GPU compute for training and high-throughput inference.
ML Pipelines
Reproducible pipelines for data prep, training, and deployment.
Model Serving
Low-latency, scalable serving for production AI workloads.
On-Prem or Cloud
Deploy where your data and compliance needs require — including data sovereignty.
Our Process
Requirements
Size workloads, throughput, and data-residency requirements.
Architecture
Design GPU, storage, and networking topology for your AI needs.
Build
Provision compute, pipelines, and serving infrastructure.
Operate
Monitor utilization, cost, and performance, and scale as needed.
Technology Stack
Key Benefits
On-premise GPU, and when it is the only option
Thai government procurement and regulated sectors frequently require that data never leaves the organisation. Where that applies, cloud GPU is not an option to be optimised — it is ruled out, and the architecture follows from that constraint rather than from cost or performance.
Where cloud is permitted it is usually cheaper to start with and we will say so, particularly for intermittent training workloads where paying for idle hardware makes no sense. Owning GPUs pays off when utilisation is genuinely high and sustained.
The comparison people get wrong is the one that ignores utilisation. A GPU server running at 15% is far more expensive per useful hour than on-demand cloud, and most first estimates of how busy the hardware will be are optimistic.
Sizing for the workload you have
Training and inference have different profiles. Training is bursty and memory-hungry; inference is steady, latency-sensitive and often runs better on smaller, cheaper hardware. Sizing one machine for both usually produces something expensive that suits neither.
We size against measured workloads rather than headline model specifications. The most common and costly mistake is buying for a peak that occurs twice a year, when a smaller permanent setup plus burst capacity handles the same demand for a fraction of the cost.
MLOps: the part that makes it usable
Hardware alone is not a platform. Reproducible training pipelines, model and dataset versioning, experiment tracking, and monitored serving are what turn a GPU server into something a team can actually work on without stepping on each other.
Without versioning of both data and models, results cannot be reproduced and "why did it predict that?" becomes unanswerable — which matters commercially, and matters under the PDPA where a decision affects an individual.
We hand over the platform definitions as code, so the environment can be rebuilt rather than reconstructed from memory, and so your next team inherits something readable.
Related Services
Frequently Asked Questions
Do you provide GPU compute?+
Yes — GPU clusters for model training and high-throughput inference.
Can it run on-premise?+
Yes — on-premise for data sovereignty, in the cloud, or hybrid.
Do you manage ML pipelines?+
Yes — reproducible pipelines for data preparation, training, and deployment.
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