AIMES ‘normalises’ healthcare AI with Pivot 3- controlled GPUs

Specialist GPU servers could be overkill when crunching relatively small amounts of data for analytics purposes. So why not use ordinary servers boosted with GPU accelerator cards and deployed via HCI instead? This is what AIMES, a specialist tech provider for the UK’s National Health Service, does with its Health Cloud service.

AIMES is using GPU-enhanced hyperconverged infrastructure appliances (HCIAs) from Pivot3 to provide AI-based applications to NHS clinicians and researchers.

NHS trusts

The NHS is organised into regional NHS Trusts. These are interconnected by the closed, secure and private broadband Health and Social Care Network (HSCN) which safeguards the exchange of sensitive NHS data between trusts and their managed service providers.

An MSP can provide shared IT facilities which regional trusts can use, instead of operating their own duplicated IT department and facilities. One such MSP is AIMES (Advanced Internet Methodologies and Emerging Systems), which is based in Liverpool and originated as a spinout from Liverpool university.

Glenn Roberts, business development director at AIMES, tells us NHS trusts typically do not want to use the big US public cloud providers because of data sovereignty and security issues. They also want to expand the clinical side of their hospitals and not the admin side, which includes local IT services.

My AIMES is true

AIMES provides managed tier 3 data centre services to commercial customers over the general internet, and also to NHS trusts using the NHS HSCN networking facility. One focus area is to provide Trusted Research Environments (TREs) to its NHS customers inside a Health Cloud.

These customers such as clinicians, could provision and decommission research environments with the same speed and flexibility as the public cloud while providing researchers with secure and high-availability virtual access complying with NHS data privacy regulations.

Pivot3 precursors

AIMES is Pivot3’s customer for its GPU-enhanced systems and makes Pivot3 capabilities available through its Health Cloud infrastructure, which it set up in 2015.

Glenn Roberts

Roberts told us AIMES initially provided Health Cloud TREs using a set of servers accessing a Dell EMC EqualLogic (PS) SAN, and then a Compellent (SC) SAN but experienced problems with reliability and upgrades. (The SC SAN is still in use with general virtual servers.) AIMES also experimented with a SAN in the cloud, an Azure pod and Hyper-V, but that did not fulfil its needs either.


After checking out Dell EMC’s VxRail and HPE’s HCI offerings, AIMES switched to Pivot3 HCI systems, In 2018, as a way of fixing these issues. Roberts said: “Pivot3 ticked all our boxes. It’s rare that a technology does exactly what it says on the tin.”

AIMES has five clustered Pivot3 Acuity nodes, two with NVMe SSDs to provide better general performance, and two Lenovo-based systems with GPU cards to deliver faster analytics capability. The non-GPU nodes are based on Dell EMC PowerEdge servers.

Roberts is particularly pleased with Pivot3’s scalability and the quick and easy way its Acuity software recognises and adds nodes such as the two GPU systems to the cluster. That reduces AIMES’s own admin effort.


Roberts told us the GPU nodes help with running predictive AI applications looking at the likelihood of depressive episodes in mental health patients. The AI model code monitors clinical data and sends alerts to clinicians if certain early marker patterns are detected in a patient’s data.

He also told us: “We’re doing AI in a project with Barts [Health NHS Trust] for cardiac imaging.” A patient has a heart MRI scan and the AI code looks at a series of right ventricle contraction images from the scan. It discerns a measure of heart health from them that is up to 40 per cent more accurate than a human assessment.

This use of AI running on Pivot3 HCI systems represents a “normalisation of AI” where ordinary servers are fitted with GPU cards to run accelerated AI code. Roberts said this is big data-stye AI analytics with relatively small amounts of data and it’s not necessary to use separate GPU systems such as Nvidia’s DGX-type GPU servers.