Question 1

What is Istidlal?

Accepted Answer

Istidlal is an AI inference platform that gives developers and companies access to production-grade GPU infrastructure without the cost and complexity of building it themselves. It combines GPU access, deployment tooling, model runtimes, and orchestration into one platform so teams can deploy and scale inference workloads faster.

Question 2

Can I use fractional GPUs instead of renting a full machine?

Accepted Answer

Yes. Istidlal supports fractional access to high-end GPUs so you can pay for only the capacity your workload needs instead of reserving an entire device. This makes premium hardware more accessible for smaller workloads, experiments, and cost-sensitive production use cases.

Question 3

Is my workload isolated from other tenants on the same GPU?

Accepted Answer

Yes, at the hardware level. On NVIDIA we use MIG (Multi-Instance GPU); on AMD Instinct we use hardware partitioning. Both give each tenant dedicated memory, cache, and compute — so another workload can never touch your bandwidth or slow down your inference.

Question 4

Do you also support full GPUs and dedicated capacity?

Accepted Answer

Yes. You can use fractional slices when efficiency matters, or reserve full GPUs and dedicated capacity when your workloads need predictable performance, isolation, or larger-scale serving. The platform is designed to support both lightweight and production-heavy usage patterns.

Question 5

What kinds of GPUs are available?

Accepted Answer

Istidlal supports a broad range of NVIDIA GPUs, from more accessible options like T4, L4, A10, and L40S up to premium accelerators like H100, H200, B200, and B300. This lets teams match hardware choices to model size, latency targets, and budget constraints.

Question 6

Which inference runtimes and containers can I use?

Accepted Answer

Istidlal provides pre-built containers and deployment paths for popular inference runtimes such as vLLM, SGLang, TGI, and Ollama. The goal is to help teams deploy faster without having to assemble the full serving stack from scratch.

Question 7

Can I deploy on Kubernetes?

Accepted Answer

Yes. Istidlal supports serving Kubernetes virtual clusters with built-in GPU management and scheduling, making it easier to run inference workloads in a more structured production environment. This is useful for teams that need orchestration, repeatability, and cleaner scaling workflows.

Question 8

Does Istidlal support distributed or multi-node inference?

Accepted Answer

Yes. Istidlal includes distributed inference templates and supports deployment patterns for larger-scale LLM serving. Teams can use ready-made templates and supported runtimes to move beyond single-node setups when throughput or model architecture requires it.

Question 9

Who is Istidlal for?

Accepted Answer

Istidlal is built for individual developers, product teams, and enterprises. A solo engineer can use it to access full or sliced GPUs on demand, while larger teams can use it for repeatable production inference, reserved capacity, and managed infrastructure workflows.

Question 10

How does pricing work?

Accepted Answer

Pricing is flexible and depends on the access model you need. Developers can start with on-demand GPU usage, teams can combine that with reserved capacity and commitment discounts, and enterprises can work with custom infrastructure and deployment models based on their operational needs.

Question 11

Do you offer enterprise or private deployments?

Accepted Answer

Yes. For enterprise customers, Istidlal can support dedicated infrastructure, multi-month commitments, private deployment models, and platform-level engagements. This can include on-prem installation or bring-your-own-device / bring-your-own-GPU workflows managed through the Istidlal platform.

Question 12

How fast can I get started?

Accepted Answer

Istidlal is designed to reduce the time between choosing infrastructure and serving a model. With pre-built containers, supported runtimes, and deployment-ready infrastructure, teams can move much faster than they would with a fully self-managed stack.

Question 13

Why use Istidlal instead of managing inference infrastructure directly?

Accepted Answer

Managing inference infrastructure directly often means dealing with hardware access, orchestration, runtime setup, scaling strategy, and cost efficiency as separate problems. Istidlal brings those layers together in one platform so teams can focus more on shipping AI products and less on stitching infrastructure together.

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Frequently Asked Questions

Questions teams ask before they deploy.