Dedicated capacity

GPUs you don't share

Reserved by the month, power and bandwidth included. B300, H200, RTX 5090 and RTX 4090 in stock across North America, Southeast Asia and East Asia.

Most teams start on the API and pay per token. Reserve capacity when your load is steady enough to keep a card busy — the figure below is where that line usually falls.

In stock

Indicative rates, monthly commitment. Final price on quote.
GPUMemoryNotableRegionsFrom /GPU·hrStatus
B300 288 GB HBM3e ~8 TB/s · ~15 PFLOPS dense FP4 · up to 1,400 W NA · SEA · EA $6.50 In stock
H200 141 GB HBM3e 4.8 TB/s · proven for 70B–400B serving NA · SEA · EA $2.60 In stock
RTX 5090 32 GB GDDR7 Best cost per token on small and mid models SEA · EA $0.75 In stock
RTX 4090 24 GB GDDR6X Fleet workhorse, large pool available SEA · EA $0.45 In stock

Rates include power and bandwidth. Minimum term one month; 3, 6 and 12-month commitments carry a discount. Interconnect, local NVMe and private networking are quoted per configuration.

Memory per GPU

What fits without splitting a model across cards.

B300
288 GB
H200
141 GB
5090
32 GB
4090
24 GB

HBM3e on B300 and H200; GDDR7 and GDDR6X on the RTX cards. Bars are to scale.

Per token, or by the month

Which way round is cheaper depends on how busy the card would be.

Pay per token Either Reserve the card 0% ~40% ~60% 100% sustained GPU utilization of your own workload Measured utilization on first look is commonly 35–55%, which is why most teams should start per token.

A rule of thumb, not a quote. Published crossover estimates range from 30% to about two-thirds; the right number for you depends on your traffic shape, model size and term. We will size it with you before you commit.

Three ways to take it

Dedicated nodes

Whole machines or whole racks, yours for the term. Root access, your images, your orchestration. For training and long-running inference that needs predictable performance.

Reserved inference

Guaranteed throughput and concurrency on our serving stack, with our optimization and scheduler doing the work. Monthly fee plus metered overage.

Private deployment

A named open-weight model deployed in your environment or in a region you pick, with inference confined to it. Project fee plus monthly operations.

Regions

Nearest-region routing or pinned by contract North America US East · US Central Southeast Asia Singapore East Asia Seoul · Tokyo
North AmericaUS East · US Central
Southeast AsiaSingapore
East AsiaSeoul · Tokyo

Inference can be contracted to stay in one region. Other locations evaluated on request.

Operations

Failure handling is three-part: detect → remediate → verify. A path with detection but no remediation is not considered fixed, and each one is held by an automated gate that injects the fault and asserts the remediation fires.

Releases roll back. One directory per version, so the previous directory is the rollback point. Read-only code is separated from runtime state; every node pulls the same build SHA.

Every model has a profile: weight provenance, version, quantization, context length and measured throughput, recorded before it serves traffic.

Request a quote

Answered within one business day by a person. Or write to sales@cflabs.ai. Contracts are settled by bank transfer; invoices issued by Computation Frontier Labs Pte. Ltd.

From enquiry to delivery

Requirements → quote → entity and export-control screening → contract and first payment → delivery with monitoring and an on-call contact.

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