Private & on-premise AI
AI servers and GPU workstations, sized to the job.
We specify, source, assemble and configure the machines that run AI locally, and hand them over with the models and software already working.
At a glance
AI servers & GPU workstations
- Sized by workload, not by budget ceiling
- Delivered with models installed and tested
- Power and cooling planned for Kathmandu conditions
- Upgrade path documented
Delivered from New Baneshwor, Kathmandu, on-site across the valley and remotely across Nepal.
In short
What is an AI server?
An AI server is a computer built to run or train AI models, which mainly means one or more powerful graphics cards (GPUs) with a lot of video memory, backed by enough system memory, fast storage and reliable power and cooling. A GPU workstation is the desk-side version for one person or a small team. Bit Microsystems specifies, builds and configures both in Kathmandu, sized to the models you actually need to run.
What is included
Everything needed to make it work in practice
One team covers the full job, so there are no gaps between suppliers.
Workload sizing
We start from the models, the number of simultaneous users and whether you are running or training, and work back to the GPU memory you need.
Component selection and sourcing
GPU, CPU, memory, storage, power supply and case chosen to work together, with availability and warranty in Nepal taken into account.
Assembly and burn-in
The machine is built and run under sustained load before delivery, so faults show up on our bench and not in your office.
Software stack
Operating system, GPU drivers, container runtime and model servers installed, with at least one model running and benchmarked.
Power, cooling and placement
UPS sizing, ventilation and noise advice, and a recommendation on where the machine should physically live.
Remote management and handover
Secure remote access, monitoring of temperature and usage, and a document describing how to upgrade later.
Where it is used
Who this helps, and how
Typical situations we design for in Kathmandu and across Nepal.
A shared office AI server
One machine serving a private assistant to a whole team over the office network.
Developer and research workstations
Desk-side machines for software teams, data scientists and university labs who fine-tune and experiment with models.
Media and design studios
Image, video and audio generation and transcription run locally, without per-render cloud charges.
Organizations that cannot use foreign cloud
In-country compute for teams whose data must not be processed outside Nepal.
How we work
From first conversation to a system your team uses
- 01
Define the workload
Which models, how many users, running or training, and what growth you expect.
- 02
Specify and quote
Two or three itemized options at different levels, with honest notes on what each can and cannot run.
- 03
Build and test
Assembly, burn-in, software installation and benchmarks on your intended models.
- 04
Install and hand over
On-site installation, network connection, remote access and documentation.
Is it right for you?
An honest fit check
We would rather tell you now than after you have paid for the wrong thing.
A good fit if
- You have decided to run AI locally and need the right machine for it.
- You want one supplier responsible for both the hardware and the AI software on it.
- You are a development or research team that needs local GPU capacity.
Probably not the right choice if
- You only need AI occasionally. Renting cloud GPU time by the hour will cost less.
- You want to train very large models from scratch. That needs data-centre scale hardware, and we will point you to cloud options instead.
Tools and technology we work with
Questions
Frequently asked
Straight answers to what people ask us most about this service.
What matters most when choosing hardware for AI?
GPU video memory (VRAM). It decides how large a model you can load and how many people can use it at once. After that come system memory, fast storage and a power supply and cooling setup that can sustain the load. Raw processor speed matters much less than people expect.
Can we run AI on a normal office computer?
Small models will run on a modern laptop or desktop, especially Apple Silicon Macs, and can be useful for one person. For a shared assistant used by a team, or for larger and more capable models, you need a dedicated machine with a proper GPU.
Is a Mac or an NVIDIA GPU machine better for local AI?
Apple Silicon Macs are quiet, power-efficient and can load large models thanks to unified memory, which makes them a good choice for a small team. NVIDIA GPU machines are faster, handle more simultaneous users, and are the standard for fine-tuning and most AI software. We recommend based on your workload rather than preference.
How do you handle power cuts and heat?
Every build is specified with a UPS sized to the machine, so it rides through short outages and shuts down cleanly in long ones. We also plan airflow and placement, because a GPU under load produces significant heat and noise.
Can the machine be upgraded later?
Yes, and we plan for it. Where the budget allows we choose a case, motherboard and power supply with room for a second GPU, and we document the upgrade path at handover.
Related
Often combined with
Start a conversation
Thinking about AI servers & GPU workstations?
Tell us what you want to achieve and what you have today. We will reply with a clear plan, a realistic timeline and an estimate.