Category: Blog

  • hosted·ai v2.0.1 – now it’s easy to optimize GPU pools

    hosted·ai v2.0.1 – now it’s easy to optimize GPU pools

    We’re excited to announce the availability of hosted·ai version 2.0.1, with some great new features that make GPUaaS easier, more flexible, AND more profitable. Let’s get into it!

    GPU security + performance optimization


    hosted·ai handles GPU very differently to other platforms. Our GPU control plane enables up to 100% GPU utilization by combining individual GPUs into pools, and allowing the resources of the entire pool to be shared with multiple tenants at once (that is the basis for hosted·ai’s dramatic improvement in GPU unit economics).

    The hosted·ai platform has an extremely efficient task scheduler that context-switches tasks in and out of physical GPUs in the pool – but, how is this scheduling controlled?

    Introducing… the new GPU optimization slider.

    When a GPU cloud provider creates a pool, they assign GPUs to that pool and choose the sharing ratio (i.e., how many tenants you can sell the pool’s resources to). For any setting above 1, the new optimization slider becomes available.

    Behind this simple slider is a world of GPU cloud flexibility. The slider enables providers to configure the shared GPU pool to suit different customer use cases:

    • Optimized for Security: temporal scheduling is used. The hosted·ai scheduler switches user tasks completely in and out of physical GPUs in the pool, zeroing the memory each time. At no point do any user tasks co-exist on the GPU. This is the most secure option, but comes with more performance overhead.
    • Optimized for Performance: spatial scheduling is used. The hosted·ai scheduler assigns user tasks simultaneously to make optimal use of the GPU resources available. There is no memory zeroing. This is the highest-performance option, but it doesn’t isolate user tasks – they are allocated to GPUs in parallel.
    • Balanced: temporal scheduling is used, but without fully enforced memory zeroing. This provides a blend of performance and security.

    Self-service/end user enhancements


    Also in this release, some handy improvements for end users running their applications in your hosted·ai environments:

    Service exposure

    We’re made it easier to expose ports for end user applications and services through the hosted·ai admin panel (and coming soon, through the user panel).

    Now your customers can choose how they present their application services to the outside world, through configurable ports.

    More self-service GPU resource management

    We’ve added new management tools for your customers too. Each GPU resource pool they subscribe to can be managed through their user panel, with visibility of the status of each pod; the ability to start, stop and restart pods; and logs with information about the applications using their GPU.

    Furiosa integration


    In July 2025 we announced our partnership with Furiosa, a semiconductor company specializing in next-generation AI accelerator architectures and hardware. We’ve been working bring Furiosa device support to hosted·ai and this is now available in v2.0.1.

    Now service providers can create regions with clusters based on Furiosa, as well as NVIDIA. Once a region has been set up for Furiosa cluster, it can be managed, priced and sold using the same tools hosted·ai makes available for NVIDIA – and in future, other accelerator devices.

    More information:


    Coming next:


    In final testing now – subscribe for updates:

    • Full stack KVM – complete implementation, replacing Nexvisor
    • Scheduler credit system – expanding GPU optimization with a credit system to delivers consistent performance for inference in mixed-load environments
    • Billing enhancements – more additions to the hosted·ai billing and metering engine – more ways to monetize your service
    • Infiniband support