How to Choose GPU Bare-Metal Rental in Taiwan: H100, H200, B200, and B300

When companies evaluate GPU bare-metal rental in Taiwan, the first decision is often whether to use H100, H200, B200, or B300. The same GPU may also be delivered as a single GPU, an 8-GPU HGX server, or a virtualized resource. A useful comparison therefore asks three questions: Does the model fit in memory? How many GPUs are required to finish the job? What is the total cost per training run or per million tokens?

In brief: H100 remains a practical choice for validation and fine-tuning; H200 is better for memory-bound models and long-context inference; B200 targets high-throughput training and inference after Blackwell software validation; B300 is designed for workloads that need more than 180 GB per GPU, including reasoning and test-time compute.

What is GPU bare-metal rental?

GPU bare-metal rental gives a company exclusive use of a physical server. The customer controls the GPU, CPU, system memory, local storage, drivers, containers, and cluster software without sharing the host with general multi-tenant virtual machines.

• Predictable GPU performance and dedicated capacity

• Root access for CUDA, drivers, Docker, Kubernetes, or Slurm

• Access to NVLink, NVSwitch, or InfiniBand where supported

• Long-running training or steady inference workloads

• Clear requirements for data isolation, software versions, and operational control

H100, H200, B200, and B300 specifications and delivery models

How to choose among H100, H200, B200, B300, GB200, and RTX PRO 6000

• H100: Mature software and deployment experience. A strong baseline for model validation, fine-tuning, RAG, image generation, and enterprise inference.

• H200: Keeps the Hopper software environment while increasing memory to 141 GB HBM3e. It is useful for longer context, larger batch sizes, and larger KV caches.

• B200: Best evaluated with the exact model and framework after Blackwell compatibility testing. A higher headline performance figure does not automatically mean lower cost per completed job.

• B300: Provides 288 GB HBM3e per GPU and targets memory-intensive reasoning and test-time compute. Power density is a deployment constraint: high-power 8-GPU nodes may exceed 14 kW and require data-center power and cooling validation.

• GB200 NVL72: A liquid-cooled rack-scale system with 36 Grace CPUs and 72 Blackwell GPUs. It requires the rack, power, cooling, networking, and delivery model to be assessed as one system.

• RTX PRO 6000: Provides 96 GB GDDR7 ECC memory and fits enterprise inference, fine-tuning, scientific computing, 3D rendering, and virtual workstation scenarios.

KONST advantages for companies deploying AI infrastructure in Asia

Global compute platforms offer large GPU fleets and mature cloud tooling. Companies in Taiwan must also evaluate data location, cross-border connectivity, Chinese-language technical communication, the contracting entity, and the operating model after deployment.

KONST Group provides a Taiwan-based service and contract interface for GPU bare-metal capacity and data-center operations. The service is designed for companies that need dedicated resources, customized environments, and stable long-term capacity. Depending on scale, the deployment can extend from a bare-metal server to a dedicated GPU cluster, colocation, or AIDC planning. Available sites, GPU models, and delivery schedules are confirmed against current capacity.

How to compare GPU rental pricing

Compare providers under the same model and test conditions, then calculate a unit-of-work cost. Cost per million tokens = total GPU, storage, network, and required service cost / completed tokens × 1,000,000. For training, compare the total cost to complete the same epochs, dataset, or target loss.

A four-step process for selecting GPU bare metal

Step 1: Identify training, fine-tuning, or inference

Training must hold model weights, gradients, optimizer states, and activations; inference is dominated by model weights, KV cache, tokens per second, time to first token, and concurrency. Fine-tuning falls between these patterns depending on whether the method is full-parameter or parameter-efficient.

Step 2: Estimate memory and GPU count

Estimate peak GPU memory, then compare quantization, model parallelism, and additional GPUs if the model does not fit on one device. Each option changes performance and operational complexity.

Step 3: Benchmark the real model

Record the GPU model and count, storage and network configuration, model version, precision, peak memory, token lengths, batch size, concurrency, throughput, latency or completion time, and average utilization. Results are not comparable unless test conditions are aligned.

Step 4: Compare unit-of-work cost

GPU/hour is only one billing input. Include idle time, storage, data transfer, environment setup, operations labor, long-term discounts, and interruption risk. The final metric should be cost per training run, completed task, or million tokens.

How KONST supports GPU selection and delivery

KONST starts with the model scale, workload, and data-processing requirements, then maps them to the GPU model, minimum rental unit, network, deployment conditions, schedule, and estimated cost. The scope covers deployment, testing, and post-launch support. Bronze, Silver, and Gold service tiers address different availability targets, hardware replacement windows, and support response times; the guaranteed values and service-credit terms are defined in the contract.

Contact KONST. The KONST team will respond within three business days.

Related service: GPU bare-metal rental.

FAQ

How should a company choose between H100 and H200?

Choose H100 when software maturity and broad workload support are the priority. Consider H200 when the workload is constrained by 80 GB of memory, requires longer context, or needs a larger KV cache. Validate the decision with the same model and inference settings.

Is B200 always more cost-effective than H200?

No. B200 can be more efficient for workloads that use Blackwell capabilities, but H200 may deliver a lower cost per completed job when software compatibility or utilization prevents the workload from using B200 effectively.

What is the difference between B200 and B300?

Both use the Blackwell architecture, but B300 increases GPU memory from 180 GB to 288 GB. An 8-GPU HGX node therefore provides about 1.44 TB on B200 and 2.3 TB on B300. B300 is more suitable when 180 GB per GPU is insufficient, including long-context inference, large KV caches, and intensive reasoning. If 180 GB is sufficient, compare completion time, power, and GPU/hour under the same model, precision, and software stack, and verify facility power and cooling requirements. See the NVIDIA enterprise reference architecture.

How is GPU bare metal different from GPU Cloud?

Bare metal provides exclusive control of a physical server and is well suited to root access, predictable performance, and customized cluster environments. GPU Cloud usually offers faster provisioning and more flexible billing. Always confirm whether resources are dedicated, the minimum rental unit, and who owns each operational responsibility.