Most Affordable GPU-as-a-Service (GPUaaS) in Malaysia

GPU as a Service (Malaysia) — Training & Inference

Run AI workloads on enterprise GPUs without buying hardware. Deploy in Malaysia when you need data residency, lower regional latency, and tighter control over where your datasets and model weights live.

Why GPU as a Service (GPUaaS)

Low CapEx, predictable OpEx: skip large upfront GPU purchases and depreciation.
Always up-to-date: you don’t have to worry about your GPUs becoming outdated when new generations arrive—upgrade by switching tiers.
Scale when needed: burst for training runs, then scale down to smaller instances for inference and dev/test.
Faster time-to-value: get GPUs immediately without procurement, rack/stack, power, cooling, or maintenance overhead.

Supports both inference and training

Inference (serving)

LLM chatbots, RAG (retrieval-augmented generation), embeddings
Document understanding, summarization, classification
Vision inference: e-KYC flows, OCR, face verification, fraud signals

Training / Fine-tuning

LoRA / QLoRA fine-tuning for LLMs
Training & tuning image models (e-KYC, detection, segmentation)
Multi-GPU training for larger models and faster experimentation

Common use cases

University AI CoE (students + labs)
Shared GPU pool for classes, research projects, hackathons, and student experimentation with quotas and cost control.
In-house R&D
Rapid prototyping, benchmarking, model evaluation, internal tools, and innovation pilots without hardware lock-in.
Train / fine-tune models
Fine-tune LLMs for domain knowledge; train/optimize vision models (e-KYC, OCR, liveness, document fraud checks).
Production AI
Scalable inference endpoints for customer support, search, personalization, analytics, compliance workflows, and more.

GPU options by region

Malaysia — keep your data in Malaysia

Best for organizations prioritizing Malaysia data residency and low-latency access for Malaysia-based systems and users.

H100

8× GPU — ecs.hpcpni3h.42xlarge — 168 vCPU • 1960GB RAM • 80GB VRAM per GPU • Local 3.84TB8 • Network 400G8 IB — Contact us for best price

H20 (96GB)

8× GPU — ecs.ebmhpcpni3l.48xlarge — 192 vCPU • 2048GB RAM • 96GB VRAM per GPU • Local 3.84TB4 • Network 400G8 — Contact us for best price

L20 (48GB)

1× GPU — ecs.gni3cl.5xlarge — 22 vCPU • 120GB RAM • 48GB VRAM per GPU — Contact us for best price
2× GPU — ecs.gni3cl.11xlarge — 44 vCPU • 240GB RAM • 48GB VRAM per GPU — Contact us for best price
4× GPU — ecs.gni3cl.22xlarge — 90 vCPU • 480GB RAM • 48GB VRAM per GPU — Contact us for best price
8× GPU — ecs.gni3cl.45xlarge — 180 vCPU • 960GB RAM • 48GB VRAM per GPU • Contact us for best price

GPU model guide (what each model is best for)

L20 (48GB) — best value for inference

Best for: LLM serving, RAG, embeddings, vision inference, moderate fine-tuning
Why: strong cost/performance for production inference and steady workloads

H20 (96GB) — larger models + heavier fine-tuning

Best for: bigger context windows, larger batch sizes, multi-GPU workloads, fine-tuning where VRAM is the limiter
Why: 96GB VRAM per GPU reduces OOM issues and improves throughput for bigger models

H100 (80GB) — flagship training performance

Best for: full training runs, large-scale fine-tuning, performance-critical workloads
Pricing: Contact us for best price

B200 — next-gen premium tier

Best for: frontier-scale training and ultra-high throughput inference
Pricing: Contact us for best price

Notes

Monthly prices can include discounted monthly subscription rates where available.
Final pricing and availability may vary by term length, capacity, and configuration.
For H100 and B200, contact us to confirm availability and get the best commercial terms.