AI & Machine Learning Workstations in Malaysia

Training models on cloud GPU instances accumulates cost quickly. A local workstation gives your team direct access to compute: faster iteration on local data, your data stays on your own hardware, and a cost that stops at purchase. We configure AI and ML workstations around your framework, model size and dataset throughput.

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Software we build aroundPyTorch · TensorFlow · JAX · CUDA

Black dual-chamber workstation with a tower air cooler and NVIDIA GeForce RTX graphics card

Workstation build, hand-assembled at Publika.

Where consumer hardware holds AI work back

Consumer cards saturate VRAM headroom quickly on larger models. Single-GPU setups bottleneck teams that need to run parallel experiments. And without ECC memory, a stray bit flip during a long training run can silently corrupt results. These are not theoretical risks; they happen in production.

A workstation that passes a 10-minute benchmark but fails a 12-hour run is not a training machine. We catch that before it reaches you.

How we specify an AI & ML workstation

Component classes, not part numbers. Your quote names the exact parts.

Component	We specify	Why it matters for training and inference
Graphics	NVIDIA RTX · 16–48 GB VRAM · multi-GPU available	Local training and inference; multi-GPU PCIe layouts for distributed training.
Processor	High core count	Data preprocessing and parallel CPU-bound pipelines.
Memory	128–256 GB · ECC available	Large datasets held in memory; ECC for numerical integrity.
Storage	NVMe scratch + high-capacity secondary	Fast dataset staging, with room for model checkpoints and experiment logs.
Cooling	Built for sustained GPU load	Thermal throttling during long training runs silently kills throughput.
OS and environment	Ubuntu or Windows · CUDA and cuDNN	Pre-configured to your version if requested.

How much VRAM do you need to run a local LLM?

Roughly 2 GB per billion parameters at 16-bit: about 14 GB for a 7B model and 140 GB for a 70B model, falling to about 4 to 5 GB and 35 to 40 GB at 4-bit. At 16-bit precision each parameter takes 2 bytes, so a 7-billion-parameter model needs about 14 GB of video memory for its weights alone, and a 70-billion-parameter model about 140 GB. Quantising to 8-bit halves that and 4-bit quarters it: a 7B model then fits in roughly 4 to 5 GB, a 70B model in about 35 to 40 GB.

Then add headroom for the context window. Long prompts and several simultaneous users grow the key-value cache, often by several gigabytes. Fine-tuning needs far more than inference, because gradients and optimiser states sit in memory beside the weights. That is why LoRA-style fine-tuning is the usual choice on a single workstation. If a model does not fit on one card, it can be split across two, which is where the PCIe layout and power supply have to be planned from the start.

Built for long unattended runs

AI training jobs run for hours or days. We stress-test every build under sustained GPU and CPU load to confirm thermal and power stability before delivery.

Close-up of an NVIDIA GeForce RTX graphics card and liquid cooler inside a Techtonic build

Assembled in-house at Publika
No drop-shipping, no outsourced assembly. Cables routed for airflow and easy upgrades.
Stress-tested under load
24 hours at full load on Prime95 and FurMark 2 before sign-off. Test logs available on request.
Your framework installed and checked
PyTorch, TensorFlow or JAX with your required CUDA and cuDNN version, on Ubuntu or Windows.
Planned for more GPUs
If your model sizes grow, we know exactly what PCIe lanes and PSU headroom are available for more VRAM or an additional GPU.

Warranty, payment and delivery

Warranty on parts and labour

Each build carries its components’ manufacturer warranty, generally 3 years, covering parts and labour. If something fails, bring it to us and we handle the claim for you.

Free after-sales care

Dust cleaning, troubleshooting and the labour for upgrades are free for as long as you own the PC. Within the Klang Valley, we also collect and return it for servicing at no charge.

Cash, transfer, card or instalments

A 10–20% deposit, depending on the order value, starts the build. Instalments are available through SPayLater, subject to eligibility, and credit card plans with most banks, except Maybank, CIMB and Public Bank.

Delivered or collected

Free delivery within the Klang Valley. On-site setup is available for a charge. Self-collect at Publika Shopping Gallery.

AI & ML workstation FAQ

Can you build a machine that supports multi-GPU training?

Yes. We design the motherboard, PCIe layout, PSU headroom and case airflow around multi-GPU setups from the start. Retrofitting a single-GPU build for multi-GPU later is almost always constrained, so it is better to plan it upfront.

What frameworks and CUDA versions do you support?

We can pre-install PyTorch, TensorFlow or JAX with your required CUDA and cuDNN version on Ubuntu or Windows. Tell us your stack and we configure it before delivery.

How does a local workstation compare to cloud GPU costs over time?

For teams running regular training jobs, a local workstation typically recovers its cost within 6 to 18 months versus equivalent cloud GPU spend. You also gain speed, privacy and no concurrency limits.

Tell us about your model and dataset

Share your framework, model size and training frequency. We will come back with a configuration recommendation and quote, usually within one business day.

Prefer to talk? WhatsApp +60 11-6938 2989