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Hostnot GPU

Paid
★ 74 • Updated Sep 8, 2026, 09:13 PM

Hostnot GPU is a paid, on-demand GPU cloud service focused on AI compute, model inference, and development workloads.

Use Cases ▼
01

LLM inference serving and evaluation

02

Machine learning model fine-tuning

03

AI development with notebooks, APIs, and prototypes

04

Computer vision processing on GPU instances

05

Rendering and batch visual workloads

06

Running custom containerized environments

07

Automating GPU capacity and inference through the developer API

What Problem It Solves ▼

Hostnot GPU solves the challenge of finding and provisioning reliable GPU capacity for AI workloads by making availability, pricing, and authorization transparent before deployment. It offers both interactive GPU instances and serverless inference endpoints, enabling teams to run fine-tuning, model serving, and development without managing physical hardware or facing hidden capacity constraints.

Key Features ▼
✓

Live, customer-visible GPU availability and pricing

✓

Supports SSH access for private, direct instance control

✓

Serverless inference API with idempotency and scoped credentials

✓

Broad selection of GPU types and multiple regions

✓

Deploy templates and developer API for repeatable automation

Overview

Hostnot GPU is a paid, on-demand GPU cloud service focused on AI compute, model inference, and development workloads. It presents a customer-visible catalog of live GPU capacity, currently showing roughly 195 offerings across 30 GPU types and 8 regions. The platform emphasizes operational transparency: users can browse live capacity before deploying, see rates from a server-side catalog, and use wallet-first authorization to review the approved cost before compute is requested.

Full Description ▼

The service supports SSH-ready GPU instances for direct access, deploy templates for repeatable environments, and a serverless AI API for chat, image, and video inference requests using scoped credentials and idempotency keys. Hostnot GPU also provides a developer API for automating capacity, usage, and inference tasks. Common workloads include LLM inference, fine-tuning, AI development, computer vision, rendering, and custom containerized environments. Provider credentials and internal routing in the serverless offering are handled server-side, keeping customers' secrets and operational boundaries clear.

Pros & Cons

Pros

+

Live, customer-visible GPU availability and pricing

+

Supports SSH access for private, direct instance control

+

Serverless inference API with idempotency and scoped credentials

+

Broad selection of GPU types and multiple regions

+

Deploy templates and developer API for repeatable automation

Cons

−

No free tier is mentioned on the homepage

−

Limited to eight reported regions

−

Security and compliance details are not detailed on the homepage

−

No open-source repository or community edition is available

−

Pricing competitiveness versus other GPU providers is not publicly benchmarked

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