Latitude.sh

The primary use cases for Latitude.sh include: AI training, inference, bare metal GPU, fine-tuning, research, dedicated workloads, generative AI

Latitude.sh operates as a Bare Metal provider with pricing starting from $0.35/hr. The platform is well-suited for teams and individuals who need flexible GPU access without long-term commitments.

Available hardware: A30, RTX A5000, RTX A6000, L40S, RTX 6000 Ada, A100 SXM, H100 SXM, GH200, RTX PRO 6000

Explore Latitude.sh's full GPU lineup and decide if it fits your use case at their official website.

As of August 24, 2026, Latitude.sh is rated 2.8 out of 5.0 on Trustpilot with 5 reviews. Founded in 2001, Latitude.sh has built its reputation over multiple years of serving GPU compute to AI developers and researchers.

Visit the Trustpilot page to read individual reviews covering topics like GPU availability, pricing fairness, support quality, and overall platform experience.

Explore what Latitude.sh currently offers at their official website.

Framework support at Latitude.sh includes:

ML-optimized images, PyTorch, TensorFlow (user-installed), CUDA

For teams with specific requirements, Latitude.sh also supports custom Docker images (Yes), allowing you to define your exact software stack including CUDA version, Python packages, and system libraries.

Additional developer tools:
- Jupyter notebooks: No
- Persistent storage: Yes

View supported framework versions and Docker images at Latitude.sh official website.

Deployment and developer tools at Latitude.sh:

Setup time: Seconds
Docker support: Yes
SSH access: Yes
Jupyter notebooks: No
API / CLI: Yes
Custom images: Yes

A fast setup time combined with Docker and SSH support means you can go from sign-up to running your first training job in minutes. Latitude.sh provides the tools needed for both interactive development (via Jupyter) and automated pipelines (via API/CLI).

For step-by-step deployment tutorials and quickstart guides, visit Latitude.sh official website.

Serverless availability at Latitude.sh: No

With serverless GPU, you deploy a model container and the platform handles autoscaling, load balancing, and cold starts automatically. You pay only when your endpoint is processing requests — there are no charges during idle time. This can reduce costs by 80-95% compared to always-on dedicated instances for bursty inference workloads.

Latitude.sh on-demand pricing starts from $0.35/hr (Per-hour billing).

View serverless deployment options and cold-start benchmarks on Latitude.sh official website.

Data center locations for Latitude.sh (headquartered in Brazil):

23 locations: US (8 cities), LATAM (5), Europe (5), APAC (4), Mexico City. GPU in Dallas, Frankfurt, Sydney, Tokyo

Latitude.sh guarantees an uptime SLA of 99.9%, which is an important factor for production inference workloads that require high availability. Private networking support: Yes.

Check GPU availability per region on Latitude.sh official website.

Multi-GPU and distributed training capabilities at Latitude.sh:

- Interconnect: NVLink
- Max GPUs per instance: 8
- Multi-node clusters: No

For training large models like LLMs that require multiple GPUs, the interconnect bandwidth directly impacts training throughput. High-bandwidth interconnects like NVLink and InfiniBand minimize the communication overhead during gradient synchronization, resulting in near-linear scaling across GPUs.

View NVLink and InfiniBand configurations at Latitude.sh official website.

Does Latitude.sh offer spot instances? No

Spot or preemptible instances are one of the most effective ways to reduce cloud GPU costs. They work by offering unused GPU capacity at a discount, with the trade-off that the provider can reclaim the instance with short notice. This is well-suited for training jobs that save checkpoints frequently, as well as batch inference workloads.

Base pricing at Latitude.sh: $0.35/hr (Per-hour billing).

For spot instance terms and savings estimates, visit Latitude.sh official website.

Data transfer pricing at Latitude.sh is as follows: None

For AI/ML workloads, egress costs can add up quickly when exporting large model checkpoints, serving predictions at scale, or syncing data across regions. Understanding the egress pricing structure at Latitude.sh is essential for accurately estimating total cost of ownership.

Storage: Local NVMe included (up to 4x 3.8TB), Block Storage $0.10/GB/mo, Filesystem Storage $0.05/GB/mo

View detailed egress pricing per region at Latitude.sh official website.

New users at Latitude.sh can take advantage of: $200 via referral program

This allows you to provision real GPU instances and test workflows including model training, inference, and environment setup without upfront cost. Since Latitude.sh pricing starts at $0.35/hr, free credits can cover several hours of GPU compute depending on the model selected.

See the latest free trial and credit promotions on Latitude.sh official website.

Latitude.sh offers a range of GPU models for AI, machine learning, and high-performance computing workloads. The full list of available GPUs includes:

A30, RTX A5000, RTX A6000, L40S, RTX 6000 Ada, A100 SXM, H100 SXM, GH200, RTX PRO 6000

The maximum VRAM available on a single GPU at Latitude.sh is 96 GB, and instances can be configured with up to 8 GPUs. The interconnect technology used for multi-GPU setups is NVLink, which determines the bandwidth between GPUs during distributed training.

Browse the full catalog of available GPUs and their specifications at Latitude.sh official website.

Latitude.sh offers cloud GPU instances starting at $0.35/hr. Billing is calculated on a Per-hour basis, which means you only pay for the exact compute time you use rather than rounding up to full hourly increments.

Spot/preemptible instances: No
Reserved instance discounts:

Latitude.sh supports the following payment methods: Credit Cards, Apple Pay, Google Pay, Wire Transfer, Crypto (USDC), ACH, Regional (Boleto/PIX, Alipay, iDEAL).

For live pricing across all GPU models and current availability, see Latitude.sh official website.