The AI continuum lately feels as if it’s being warped by the sudden appearance of a hidden and super-massive black hole. Spiraling development costs have given rise to feverish discussion of tokenomics as IT organizations struggle to limit their DevOps teams from depleting a year’s worth of tokens in just a few weeks. But runaway token spends are just the most visible part of today’s AI challenges.
Again With the Data.
Our final presenter at AI Field Day #8 – Hammerspace – targeted a key dimension that IT shops must focus on when building robust AI solutions that hopefully will yield meaningful results for their applications’ end users: AI still requires humongous volumes of data to produce accurate intelligence.
Running Out Of Everything Everywhere, All At Once
Hammerspace succinctly summarized the multiple uncomfortable realities about AI resource availability being discussed in just about every C-level boardroom discussion these days:
- Datacenter power resources are crimped because utilities are unable to increase capacity quickly enough.
- Even if you could even buy more processors, CPUs continue to be expensive.
- Since storage is mainly SSD- and NVME-focused, the availability of reasonably priced storage is limited, too.
- Finally, if a shop cannot secure additional power, compute, or storage within their own confines, cloud capacity is no longer a guaranteed off-ramp because nesoscalers – and even the largest hyperscalers – are nearing the maximum limits of their commodity hardware, just to support the existing workloads of their other customers.
Unifying, Rather Than Just Consolidating, Data
Hammerspace’s AI Data Platform solution thus implements a data unification strategy, rather than merely applying typical data consolidation approaches.
When a request is made for a specific set of data – for example, several thousand documents needed for additional AI training, or a vector-driven similarity search during an intense inference operation – the Hammerspace AI platform gathers those resources into the Tier 0 layer so operations can complete as quickly as possible. And when operations cease to require speedy access to resources, they can be moved intelligently to other (s)lower storage tiers.
Their solution essentially builds a global namespace that appears as a single storage viewpoint. Their platform stands in front of an organization’s present set of storage clusters and can access data from anywhere, regardless if it’s retained in local SSDs or NVMes – what we typically call Tier 0 – or an on-premises private cloud, or even a public cloud.
The advantage of this strategy? It treats data, wherever it might exist within multiple physical storage layers, as if they were kept within a single massive cluster.
Storage Vendor Partnerships = Even More Interesting Use Cases
Hammerspace’s customer base illustrates the attractiveness of their platform – they’ve implemented within customer HPE solutions as well as neoclouds. And Hammerspace also described several quite divergent use cases, including an AI application development environment supporting data retrieval demands spanning multiple petabytes and a user base supporting thousands of data scientist end-users.
Hammerspace also co-presented on how their global storage model helps Hitachi Vantara expand the capabilities of its VSP One platform for several of its AI-focused customers.
In concert with Vantara, Hammerspace’s solution was deployed across intriguing use cases, including a complex AI-based fraud detection and risk assessment application, as well as and an AI “incubation hub” where the Hammerspace solution sat above VSP-One block storage and communicated with an NVIDIA-powered two-node GPU cluster.
(Full disclosure: I’d actually helped launch the first iteration on the VSP in 2010 with extensive experimental research on how best to use Hitachi storage solutions to handle extreme Oracle database workloads as they consumed SSD storage bandwidth. I remember one workload was so intense that we actually melted a few SSDs in their cases.)
Storage Has Always Just “Been There”
If I were back in my role as a senior guy “in the trenches” just trying to keep a team of data engineers and data scientists productive and frustration-free, I can see exactly how valuable Hammerspace’s unified storage approach would benefit us. At every IT shop I’ve worked at, the expectation is that the storage layer is transparently ubiquitous, works all the time, and never ever runs out of space.
Of course, that’s a fantasy, and with the onset of increasing AI workload demands like training and inference that are often in direct conflict with each other, anything that helps homogenize and simplify managing storage is worth a closer look.
Our team didn’t get to see how Hammerspace’s AI Data Platform worked in concert with Hitachi VSP One because our final session ran out of time. That’s not a bad sign – it just means our team of delegates found lots of great questions to ask; check out the introductory video to see we all found interesting.
