Low-Latency AI Performance
GPU-accelerated builds deliver steady tokens per second for chat, RAG, vision, and voice.
AI Solutions Partner · Malaysia
Private AI,
built in Malaysia.
We design, build, and run on-prem AI systems for Malaysian firms — on hardware our team assembles and supports locally.
Built on
EMARQUE pairs in-house AI specialists with hardware built in Malaysia. Private chat, RAG, agents, vision, voice, and custom model training — designed, deployed, and operated on-prem.
15+Years experience
10,000+Systems Built
57-PointAssembly & QC Process
In-HouseStrategy · Build · Operate
How we work
An AI partner — not a box vendor.
Our team in Malaysia scopes the use case, picks the right model, sizes the hardware, and runs the production stack. Most projects ship in 8–14 weeks — shorter for pilots, longer for multi-site rollouts. Cost and timeline disclosed at each step.
- 01
Discover
Workshops with your leaders and data teams to map the workflow, set success metrics, and assess data readiness. We flag where on-prem AI fits and where it doesn't.
AI strategy & architecture
- 02
Design
Model selection, hardware sizing, security and compliance design, integration plan. One document your CTO, finance team, and auditors can sign off on.
Private chat · RAG · vision · custom model
- 03
Build & test
Hardware assembled at the EMARQUE Lab with 57-point QC and a 48-hour stress test. Runtime install, data integration, and benchmarks on your real prompts before sign-off.
Tuned models · validated runtime · benchmark report
- 04
Operate
Monitoring, patching, parts SLAs, capacity reviews, and a quarterly benchmark report. Managed-service tiers for teams without in-house MLOps.
Managed AI infrastructure
Industry Leading Clients
Hardware for every scale.
From a desk-side AI supercomputer to a trillion-parameter rackmount server. Our team helps you size the right one.
NVIDIA DGX Spark
From RM 15,888 – RM 24,999
128
GB unified
4
TB NVMe max
20
Arm cores
NVIDIA DGX Station
Contact for pricing
20
PFLOPS FP4
748
GB coherent
800
GbE network
AI PRO 500
From RM 35,000 – RM 110,000
4
GPU max
1 TB
ECC DDR5
30
TB NVMe
AI Work 100
From RM 12,000 – RM 28,000
2
RTX 5090 GPUs
192 GB
DDR5 max
8 TB
Gen5 NVMe
EMARQUE AI Server
Contact for pricing
768
GB GDDR7 max
8
PCIe Gen5 cards
4U
Air-cooled
NVIDIA DGX B200
Contact for pricing
8
B200 GPUs
1,440 GB
HBM3e
144
PFLOPS FP4 inference
NVIDIA DGX B300
Contact for pricing
8
Blackwell Ultra GPUs
2,304 GB
HBM3e
11×
vs Hopper inference
NVIDIA GB300 NVL72
Contact for pricing
72
Blackwell Ultra GPUs
21 TB
HBM3e pool
1.4
ExaFLOPS FP4 inference
NVIDIA Vera Rubin NVL72
Contact for pricing
Rubin
GPU generation
HBM4
Memory
~3.6
ExaFLOPS FP4 inference
Need a custom build? Build your own.
We Got You Covered
Performance, data control, and predictable cost — backed by our build standards and local support.
Predictable Cost & Control
You own the capacity. No per-token bill shock. Data stays on your hardware.
NVIDIA GPU-First Builds
Single-CPU, multi-GPU layouts tested for thermals, power, and airflow.
ECC Memory & NVMe Path
256–2,048 GB ECC paired with NVMe / U.2 pools for long contexts, embeddings, and active jobs.
Build & Burn-In
57-point assembly, BIOS/BMC hardening, and a 48-hour stress test with benchmark report.
Priority Care
Next-business-day pickup and return where possible. Remote diagnostics and parts SLAs to keep you online.
Contact Us
Get in Touch with Us
Tell us about your workload. We reply within one business day with a quote sized to fit.
- 01
Key Account Manager
+6012 627 2280 - 02
Request for Quotation
business@emarque.co
FAQs
Frequently asked questions
What is the best AI workstation in Malaysia for on-prem inference?
For most Malaysian teams, the NVIDIA DGX Spark is a good starting point — a desk-side personal AI supercomputer with NVIDIA GB10 Grace Blackwell, 128 GB unified memory, and the full DGX OS stack pre-installed. It runs 7B–70B models locally with low latency. For departments or production, the AI PRO 500 (multi-GPU pedestal, up to 4 GPUs) or EMARQUE AI Server (4U rackmount, up to 8 NVLink H200 GPUs) are next steps.Should I buy an on-prem AI server or use cloud AI in Malaysia?
Buy on-prem if you have steady inference (chat, RAG, agents, batch jobs), data that must stay inside your network, or workloads that grow faster than cloud per-token pricing fits. Use cloud for short, spiky experiments. Most teams in Malaysia find a single AI PRO 500 pays back in 12 to 18 months versus equivalent cloud GPU rental — no egress fees, no per-token surprises.Which GPUs and CPUs are recommended for an on-prem AI system?
GPUs: NVIDIA RTX 5090 (32 GB) for individual / small-team use, RTX 6000 Ada / 5090 multi-GPU for departments, and H200 / B200 NVLink for production training and large models. CPUs: AMD Ryzen 9 9950X for towers, Threadripper PRO or Intel Xeon W for multi-GPU pedestals, and dual EPYC or Xeon Scalable for rackmount. EMARQUE validates every combination for thermals, power, and airflow before shipping.Can an AI workstation run major LLM models locally?
Yes. EMARQUE workstations are tuned to run OpenAI GPT-OSS (20B and 120B), Meta Llama 3 / 3.1 / 3.2 / 3.3 (1B–90B), and the DeepSeek family (R1, Coder, Math, V3 Chat) locally. We pre-load the runtime (Ollama, vLLM, or your stack of choice) and validate tokens-per-second on your real prompts before delivery, with a benchmark report.What OS and networking are ideal for an on-prem AI build in Malaysia?
Most teams run Ubuntu 24.04 LTS for the Linux toolchain and CUDA support; Windows 11 Pro is also supported when your workflow requires it. For networking, 10 GbE is standard on AI PRO 500 and above; EMARQUE AI Server supports 25 / 100 GbE with optional RDMA for multi-node setups. We can ship default-deny outbound firewall rules and air-gapped configurations on request.