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Catalogue › Hardware & Equipment Marketplace › Servers, Storage, GPU & AI Infrastructure
Hardware & Equipment Marketplace
Servers, Storage, GPU & AI Infrastructure
Enterprise compute, storage and accelerator platforms for business applications, virtualization, data platforms, AI and high-performance workloads.
- Right-sized compute and storage capacity
- Resilient infrastructure
- Supported AI acceleration
- Documented deployment and growth path
- Rack and tower servers
- SAN, NAS and object storage
- GPU servers and accelerator systems
- Virtualization and hyperconverged platforms
- Backup appliances and media
- Racking, configuration, burn-in and installation
- Approved bill of materials or manufacturer part selection
- Technical datasheets and compatibility record
- Warranty, authenticity and regulatory documentation
- Configuration, staging or asset tagging when purchased
- Shipping, installation and acceptance records
- Support, replacement and end-of-life information
- Single server
- High-availability cluster
- Private cloud stack
- AI training/inference platform
- Storage and backup solution
- Managed infrastructure
- Brand, model and technical specification
- Quantity, stock and supplier price validity
- Configuration, licenses and accessories
- Warranty and support level
- Freight, installation, taxes and duties
- Compatibility and lifecycle availability
Content on this page comes from the governed ARRIX catalogue record HW-02; pricing is confirmed only through a reviewed quotation.
What This Is
Servers, Storage, GPU & AI Infrastructure
Server and storage infrastructure is the compute and data capacity that business applications run on, whether it sits in your building or in a rack you rent.
Applications, databases, file shares and AI workloads all need somewhere to run and somewhere to keep data. That capacity can be a single server in a cupboard, a clustered pair that keeps working when one fails, or a rack of machines with accelerators for model training. The choice is driven less by raw speed than by how much downtime the business can absorb and how fast the data is growing.
Continuity through failure
Redundant power, storage and clustered hosts let a component fail without the service stopping.
Consolidation savings
Virtualisation puts many workloads on fewer physical machines, cutting power, rack space, licensing and maintenance.
Predictable performance
Dedicated capacity removes the variability of shared or oversubscribed environments.
Data protection
Redundant disk layouts plus separate backup copies survive both hardware failure and deletion.
Room to grow
Buying with expansion headroom lets capacity be added without replacing the platform.
Why It Matters
The problems this answers
Server and storage decisions set the availability floor for everything above them. A single server is the cheapest way to run an application and the most expensive way to discover you needed two. The questions that matter are what happens when a component fails, how quickly data is growing, and whether the workload is steady enough to own outright rather than rent.
- Business applications running on hardware with no redundancy and no tested recovery path
- Storage filling up faster than anyone planned for, with no headroom to absorb it
- Ageing servers out of manufacturer support, where a failed part means an unknown wait
- Backups that exist but have never been restored, so recovery time is unknown
- AI or analytics workloads that cannot run on general-purpose hardware
| Organisation | Need | What the equipment does | Outcome |
|---|---|---|---|
| An organisation running its own line-of-business application | A reliable place for the application and its database | A supported server with redundant storage and power, backed up off the machine | A single component failure stops nothing |
| A business consolidating several ageing servers | Fewer machines, lower running cost, easier maintenance | Virtualisation hosts sized for the combined workload plus headroom | Less hardware to maintain and a simpler recovery story |
| A team with growing file and record storage | Capacity that can expand without a migration | Shared storage with expansion room and a separate backup target | Growth is a purchase, not a project |
| A data or AI team | Sustained accelerated compute for training or inference | GPU-equipped hosts with the power, cooling and fast storage those cards require | Jobs finish in hours rather than days |
How To Choose
Types, and when each one is the right answer
Sizing matters more than brand here. ARRIX specifies to the requirement rather than to a fixed model list.
Tower server
One or two workloads in an office without a rack; quiet, simple, inexpensive.
Rack server
The standard building block once there is a rack and more than a couple of workloads.
High-availability cluster
The service must survive a host failure without manual intervention.
Shared / network storage
Several systems need the same data, or capacity should grow independently of compute.
Backup and recovery target
Always - a copy on the same array is redundancy, not backup.
GPU / AI platform
Model training, inference at volume, or accelerated analytics.
Hyperconverged / private cloud stack
Compute and storage are grown together and managed as one pool.
What decides the choice
- State the tolerable downtime first - it decides single server versus cluster more than any other input.
- Size memory and storage from the actual workload, then add headroom for growth over the intended service life.
- Decide the recovery objectives: how much data you can afford to lose, and how long recovery may take.
- Check the physical envelope early - power, cooling, rack depth and weight constrain what can be installed.
- For accelerated workloads, confirm power draw and cooling before choosing cards; this is usually the binding limit.
- Match support level to how quickly a failed part must be replaced at that site.
- Compare owning against renting capacity for workloads that are seasonal or still changing shape.
Specification Detail
For whoever has to sign it off
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Specifications that actually decide the outcome
| Specification | Weight | What it means | Why it matters | Specify higher when |
|---|---|---|---|---|
| CPU cores and sockets | Critical | How many parallel threads the machine can carry. | Sets virtualisation density and throughput for concurrent work. | Many virtual machines, heavy databases, concurrent users. |
| Memory capacity | Critical | Total RAM and how far it can be expanded later. | Usually the first ceiling a virtualisation host meets. | Consolidation, in-memory databases, analytics. |
| Storage type and layout | Critical | NVMe, SSD or mechanical, and the redundancy arrangement across drives. | Governs both speed and what happens when a drive fails. | Transactional databases, many concurrent users, AI data loading. |
| Redundancy | Critical | Duplicated power supplies, drives, network paths and, at cluster level, hosts. | Decides whether a single failure is an incident or a notification. | The service cannot be offline during working hours. |
| Support level and term | Critical | How fast the manufacturer replaces a failed part, and for how many years. | Recovery time is set by this, not by the hardware. | Remote sites, critical services, long service life. |
| Expansion capacity | Important | Free drive bays, memory slots and expansion slots. | Determines whether growth is an upgrade or a replacement. | Data is growing and the platform must last. |
| Power and cooling draw | Important | What the machine consumes and the heat it produces. | Frequently the real constraint in an office server room. | GPU platforms, dense racks, small rooms. |
| Network throughput | Important | Interface speed and how many paths exist. | A fast server behind a slow, single link is still slow. | Shared storage, backup windows, virtualisation. |
| GPU class and count | Specialized | Accelerator type, memory and how many fit. | Accelerator memory usually decides which models can run at all. | Training, large models, batch inference. |
What it has to work with
- Rack depth, power connectors and cooling capacity must be confirmed against the room before ordering.
- Virtualisation and backup software support specific hardware and firmware generations - check certification.
- Shared storage and hosts should be specified together; a mismatch in network path or protocol shows up as unexplained slowness.
- GPU platforms have power and cooling requirements that ordinary racks and circuits frequently cannot meet.
What it costs to own, not just to buy
- Hardware purchase is a fraction of lifetime cost once support contracts, power, cooling and licensing are included.
- Software licensing on some platforms is charged per core or per socket, so CPU choice can move software cost more than hardware cost.
- Consolidation reduces power, cooling, rack space and maintenance simultaneously - the saving compounds.
- Downtime cost belongs in the comparison; it is usually what justifies redundancy.
- Extending support on ageing hardware eventually costs more than replacement, and the crossover is worth calculating.
Risks and things worth knowing first
- Redundant disks protect against drive failure, not against deletion, corruption or ransomware - a separate backup copy is required.
- A backup that has never been restored is an assumption; recovery time is unknown until it is tested.
- Hardware out of manufacturer support has no guaranteed parts path, and that becomes visible only at failure.
- Undersized power or cooling limits what can be installed later, regardless of free rack space.
- Single-server designs concentrate risk; the saving is real and so is the exposure.
Mistakes that are common and expensive
- Sizing for today's data with no growth headroom
- Treating disk redundancy as a backup
- Choosing a support level that cannot meet the recovery time the business assumes
- Specifying GPUs before checking the power and cooling the room can actually deliver
- Ignoring per-core software licensing when choosing processors
When you may not need this at all
If workloads are variable, seasonal, or still changing shape, rented cloud capacity may fit better than owned hardware - you pay for elasticity instead of buying for a peak that may not arrive. Owning tends to win on steady, predictable, data-heavy workloads with long lives.
Common Questions
Answered plainly
What does this category cover?
The compute and data capacity business applications run on: servers, shared storage, backup targets and accelerated platforms for AI and analytics.
Do we need one server or two?
It depends on tolerable downtime. One server is cheaper; if the service cannot be down during working hours, a clustered pair or equivalent redundancy is the requirement, not an upgrade.
Is RAID a backup?
No. Redundant disks survive a drive failure. They do not survive deletion, corruption or ransomware. A separate backup copy is required, and it should be restored periodically to prove it works.
Should we buy servers or use the cloud?
Steady, predictable, data-heavy workloads with long lives often favour owning. Variable or still-changing workloads often favour renting. Many organisations run both and place workloads by fit.
What usually limits a GPU platform?
Power and cooling, before rack space. Accelerator cards have requirements ordinary office server rooms frequently cannot meet, so that should be checked before the cards are chosen.
Which server brand does ARRIX supply?
ARRIX represents this category at family level and sources to your specification from approved manufacturers. Exact manufacturer, model, configuration and lead time depend on the requirement and current sourcing conditions.
Request A Quotation
Tell ARRIX the situation, not the part number
ARRIX sources this family to requirement. A specified quotation is faster than a catalogue search, and these are the questions it answers.
- What applications and databases will run on this, and how many users do they serve?
- How long can the service be unavailable before it hurts - minutes, hours or a day?
- How much data do you hold now, and how fast is it growing?
- Do you have a rack, and what power and cooling is available?
- Do you virtualise today, and with which platform?
- What is your current backup arrangement, and when was a restore last tested?
- Is any AI, analytics or GPU workload planned?
- Should installation, migration and decommissioning be included?
- What support response time do you need at this site?