Businesses can buy B300 or GB300 equipment through server manufacturers, authorized resellers or system integrators. For direct access to compute, they can approach providers about bare-metal rental or dedicated clusters. If ownership matters, compare the purchase and operating costs. If the priority is a working environment, compare reserved capacity, activation dates and management responsibilities.
B300 and GB300 Purchasing Channels and Rental Options
Businesses looking to buy or rent B300 or GB300 can contact KONST Group to discuss available options, deployment locations and delivery terms. Equipment ownership, management responsibilities and the expected usage period will help determine whether purchasing hardware, renting dedicated bare metal or using GPU Cloud is the best fit.
| Access model | Ownership and management | Capacity commitment | Location and best fit |
|---|---|---|---|
| Hardware purchase | Customer owns the hardware, manages it or hires an operator, and bears replacement and residual-value risk. | Capacity is fixed by the equipment purchased; expansion requires more hardware. | On premises or in colocation; suits long-term use, asset control and teams able to manage operations. |
| Dedicated bare-metal rental | Provider usually owns the hardware. Customer has exclusive use; software is self-managed or managed by agreement. | Often a fixed term with reserved capacity; idle capacity may still incur charges. | Remote access at an agreed facility; suits sustained workloads needing control over the environment. |
| GPU Cloud | Platform usually owns the hardware. Customer uses VMs, containers or other cloud resources; management varies by plan. | On-demand or reserved capacity; on-demand access may not guarantee capacity at peak times. | Available platform regions; suits short trials, variable demand or teams seeking preconfigured environments. |
A dedicated GPU cluster combines multiple compute nodes and interconnect resources. It can be purchased or rented. For work spanning multiple servers, agree on node exclusivity, networking, storage and cluster-wide performance acceptance criteria as well as the commercial model.
Choosing Between B300 Servers and GB300 NVL72
Buying B300 usually means purchasing complete servers equipped with B300 GPUs. GB300 NVL72 is a rack-scale system. B300 servers are worth evaluating when a workload fits on one server or scales efficiently across a conventional multi-node cluster. Large-model workloads that need a wider high-speed GPU interconnect domain may benefit from evaluating GB300 NVL72.
DGX B300 and GB300 NVL72 both use Blackwell Ultra GPUs, but differ in CPU architecture and the size of the GPU interconnect domain. B300 servers from other vendors should be checked against their specific configurations.
| Comparison | B300 servers: DGX B300 example | GB300 NVL72 |
|---|---|---|
| System layout | Nine servers with 8 GPUs each can form a 72-GPU cluster. | Rack-scale system |
| CPU and GPU configuration | Per server: x86 CPUs and 8 Blackwell Ultra GPUs | Per rack-scale system: 36 Grace CPUs and 72 Blackwell Ultra GPUs |
| High-speed GPU interconnect | Eight-GPU NVLink within each server; a separate cluster network connects servers. | A 72-GPU domain using fifth-generation NVLink |
| Deployment requirements | Check power and cooling by model; published DGX B300 power consumption is approximately 14 kW per server. | Plan rack-level liquid cooling, power distribution and cluster networking together. |
| Workload fit | Work fits on one server or scales efficiently across nodes. | Large models need a wider high-speed interconnect domain and sustained work at the appropriate scale. |
Nine eight-GPU servers also provide 72 GPUs in total, but communication between those servers depends on the cluster network. That differs from the interconnect inside a single NVL72 system. Compare how the model is distributed, the volume of cross-node traffic and actual execution efficiency, as well as the GPU count.
Comparing B300 and GB300 Prices
Compare B300 and GB300 prices using the same configuration, usage period and service scope. A GPU, an eight-GPU server, a rack-scale system and GPU/hour are different pricing units. Unit prices alone do not show which option has the lower total cost.
Hardware purchase: establish what the quote includes. Networking, storage, liquid cooling equipment, installation and warranty coverage all affect the upfront cost. Add facility, power, cooling, connectivity and operating expenses over time. If suitable infrastructure already exists, assess the additional costs of this deployment.
Bare-metal and cluster rental: confirm reserved capacity and minimum commitments. Check the term, minimum rental unit and expansion conditions alongside the GPU count. GPU/hour can be the pricing basis for a long-term contract; it does not necessarily mean pay-as-you-go billing. Reserved capacity may remain billable while idle.
GPU Cloud: check additional fees and capacity assurance. Storage, data transfer and idle resources may incur separate charges. On-demand access to a particular model depends on region and current capacity. A fixed launch date may require a reservation.
Delivery and Deployment Determine When Compute Is Usable
Plan around the date workloads can run reliably. Hardware purchases require site readiness, installation and software validation. Rental services require confirmed capacity, working account access and acceptance of the delivered environment.
Before ordering equipment, confirm that power, cooling, space and networking support the selected model. Delivered hardware cannot go live while power distribution or liquid cooling work remains unfinished. Clarify warranty commencement, installation and acceptance dates together.
For bare-metal or cluster rental, distinguish a quotation from reserved capacity and a production-ready service. Align activation, billing commencement and acceptance conditions in the contract, including how delivery delays will be handled. Future expansion also requires confirmation of capacity lead times and interconnect compatibility.
A service contact in Taiwan does not establish that equipment and data are also in Taiwan. Confirm the locations of compute, primary storage and backups, as well as cross-border latency, transfer fees and support hours, against the project's requirements.
Validating Performance Before Buying or Signing a Long-Term Rental
Test candidate configurations with your own models and data before committing. Under the same quality requirements, establish how many GPUs are needed, how long work takes and whether the total cost meets expectations. Peak compute and network specifications help with initial screening; real workload results should guide the final decision.
- Large-scale training: Fix the model, data and training settings, then compare time to the target quality, GPU count and execution stability.
- Multi-node workloads: Measure the speedup from adding nodes and identify bottlenecks in communication, data access or storage, beyond nominal port speeds.
- Online inference: Fix the model, input and output lengths, concurrency and cache conditions. Compare completed requests that meet latency and quality targets. Turn the results into measurable acceptance criteria in the delivery scope. If a small rental trial is followed by a multi-node deployment, retest networking and storage at the larger scale. Single-server results do not necessarily scale in direct proportion to the number of servers.
KONST B300 Bare-Metal and Dedicated Cluster Services
KONST offers B300 through its GPU bare-metal rental service, alongside dedicated GPU cluster delivery. These options suit businesses needing reserved capacity, control over their software environment or multi-node computing. Model size, sustained usage and deployment requirements help determine whether a single bare-metal server or a dedicated cluster is appropriate.
Delivery includes server installation, initial operating system setup, drivers and the base environment, plus an isolated network segment and dedicated VPN. KONST owns the hardware; customers connect remotely and manage their frameworks, scheduling and data. Multi-node projects can include agreed networking, storage, performance acceptance and operating responsibilities in the cluster plan.
The service reserves capacity under annual contracts, uses GPU/hour as its pricing basis and is billed monthly. Payment and capacity terms are defined in the contract. It suits teams with sustained compute needs that want to retain software control while the provider delivers the hardware and initial environment.
B300 deployment locations, reservable capacity and activation dates are confirmed per project. GB300 offerings and schedules require a separate inquiry. Discuss B300 bare-metal or dedicated cluster requirements with KONST to identify delivery options that match your timeline and scale.
