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20x Faster Time to First Token: The HPE Alletra X10000 Edge for AI

In March 2026, HPE, NVIDIA, Kamiwaza and Signal65 published a paper (check here and here) showing  about a 20x acceleration for both time-to-first-token (TTFT) and token generation rate, using an HPE Alletra X10000 to store the KV cache. The storage system used S3 over RDMA to achieve this (and is in fact the first NVIDIA-certified object storage system).

I will explain why all this is important, why it’s different from competitor numbers, plus provide some insights about what this means regarding overall system efficiency.

Because the goal isn’t just to keep GPUs busy. It’s to keep them busy generating new stuff, not recalculating old stuff.

The benefits with this solution are numerous:

  • Far more workload becomes possible but also…
  • One could approach it as a lot less infrastructure is needed due to far more efficient use of the hardware, which means…
  • Lower power and rackspace requirements, which all leads to…
  • Lower Watt/token and lower $/token.
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Ransomware Detection in the HPE Alletra B10000 – Successfully Recover

With the R5 release of the code for the HPE Alletra MP B10000, among many other nice improvements (for example NAS and a nice new CSI driver), everyone now gets heavy-duty ransomware detection.

We have invented some new and unique ways for detecting modern ransomware that encrypts data in advanced ways (for example intermittent encryption), but most importantly, ways to detect encryption that doesn’t even look like encryption (which defeats other detection methods that rely on fixed entropy detection thresholds). See explainer here for more details on why this is crucial.

The whole point of doing this is early detection.

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Ransomware Detection Failure – Where does Technology Fail

This is a generic, foundational article to support related materials.

Modern ransomware that encrypts your data tries to evade detection – since that’s how the hackers maximize their payout.

The way they evade detection is by doing 2 main things:

  1. Not encrypt everything – that evades detectors that look for massive changes in your data. So they might encrypt just a tiny bit in each file.
  2. Encrypt in a way that doesn’t look encrypted 🙂

#2 is the interesting one and we will focus on that one in this article since that’s where most encryption detectors fail.

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HPE X10000 Deep Dive – Differentiation For Unstructured Data

At HPE Discover Barcelona 2024, HPE released the Alletra Storage MP X10000, the latest in our new line of shared hardware platform storage offerings.

It’s an innovative new platform specially made for unstructured data, and a long time in the making. This is HPE tech, not a partnership.

The initial workloads this solution is aimed at are anything requiring fast S3 performance, including AI workloads, data lakes, cloud native app development and high speed restore and backup.

It has several innovations such as RDMA for object, and is highly differentiated – plus, allows this kind of technology in a smaller possible starting capacity instead of only focusing on the huge side of the scale.

As usual, my aim is not to regurgitate basic information but rather to explain the true technical differentiation and get people excited about the possibilities on offer here. 

The summary of the X10000 benefits are:

  1. Disaggregation flexibility for separately expanding compute and/or capacity
  2. Ability to scale down and not need huge capacities to get good performance
  3. Balanced read/write performance and low latency for all workloads
  4. Flexible, fully container-based architecture that opens up tons of possibilities for running customer code inside the storage solution.

Let’s get to it:

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How to Successfully Navigate Storage Vendor Capacity Guarantees

beancounter

I want to arm you with the knowledge needed to properly navigate storage efficiency guarantee contracts and arrive at a safe system sizing, with reasonable assumptions.

This is another of my generic, vendor-neutral posts aimed at helping the audience be aware of certain important things that I often see overlooked.

How does one navigate the small print around storage data reduction guarantees? What are you entitled to if the vendor misses the mark? And how do you minimize your risk when faced with certain sales teams that are determined to win even if it means huge customer risk?

Do you know your data makeup? And how it may affect a capacity guarantee? But, more importantly, how it will affect your overall efficiency?

Let’s start with a nice reductio ad absurdum example to illustrate what I mean.

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