Category: AI

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169: GreyBeards talk AgenticAI with Luke Norris, CEO&Co-founder, Kamiwaza AI

Luke Norris (@COentrepreneur), CEO and Co-Founder, Kamiwaza AI, is a serial entreprenaur in Silverthorne CO, where the company is headquartered.. They presented at AIFD6 a couple of weeks back and the GreyBeards thought it would be interesting to learn more about what they were doing, especially since we are broadening the scope of the podcast, to now be GreyBeards on Systems.

Describing Kamiwaza AI is a bit of a challenge. They settled on “AI orchestration” for the enterprise but it’s much more than that. One of their key capabilities is an inference mesh which supports accessing data in locations throughout an enterprise across various data centers to do inferencing, and then gathering replies/responses together, aggregating them into one combined response. All this without violating HIPPA, GDPR or other data compliance regulations.

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Kamiwaza AI offer an opinionated AI stack, which consists of 155 components today and growing that supplies a single API to access any of their AI services. They support multi-node clusters and multiple clusters, located in different data centers, as well as the cloud. For instance, they are in the Azure marketplace and plans are to be in AWS and GCP soon.

Most software vendors provide a proof of concept, Kamiwaza offers a pathway from PoC to production. Companies pre-pay to install their solution and then can use those funds when they purchase a license.

And then there’s their (meta-)data catalogue. It resides in local databases (and replicated maybe) throughout the clusters and is used to identify meta data and location information about any data in the enterprise that’s been ingested into their system.

Data can be ingested for enterprise RAG databases and other services. As this is done, location affinity and metadata about that data is registered to the data catalogue. That way Kamiwaza knows where all of an organization’s data is located, which RAG or other database it’s been ingested into and enough about the data to understand where it might be pertinent to answer a customer or service query.

Maybe the easiest way to understand what Kamiwaza is, is to walk through a prompt. 

  • A customer issues a prompt to a Kamiwaza endpoint which triggers,
  • A search through their data catalog to identify what data can be used to answer that prompt.
  • If all the data resides in one data center, the prompt can be handed off to the GenAI model and RAG services at that data center. 
  • But if the prompt requires information from multiple data centers,
  • Separate prompts are then distributed to each data center where RAG information germane to that prompt is located
  • As each of these generate replies, their responses are sent back to an initiating/coordinating cluster
  • Then all these responses are combined into a single reply to the customer’s prompt or service query.

But the key point is that data located in each data center used to answer the prompt are NOT moved to other data centers. All prompting is done locally, at the data center where the data resides.  Only prompt replies/responses are sent to other data centers and then combined into one comprehensive answer. 

Luke mentioned a BioPharma company that had genonome sequences located in various data regimes, some under GDPR, some under APAC equivalents, others under USA HIPPA requirements. They wanted to know information about how frequent a particular gene sequence occurred. They were able to issue this as a prompt at a single location which spun up separate, distributed prompts for each data center that held appropriate information. All those replies were then transmitted back to the originating prompt location and combined/summarized.

Kamiwaza AI also has an AIaaS offering. Any paying customer is offered one (AI agentic) outcome per month per cluster license. Outcomes could effectively be any AI application they would like to perform.

One outcome he mentioned included:

  • A weather-risk researcher had tons of old weather data in a multitude of formats, over many locations, that had been recorded over time.
  • They wanted to have access to all this data so they can tell when extreme weather events had occurred in the past.
  • Kamiwaza AI assigned one of their partner AI experts to work with the researcher to have an AI agent comb through these archives, transform and clean all the old weather data into HTML data more amenable to analysis . 
  • But that was just the start.. They really wanted to understand the risk of damage due to the extreme weather events. So the AI application/system was then directed to go and gather from news and insurance archives, any information that identified the extent of the damage from those weather events. 

He said that today’s AgenticAI can implement a screen mouse click and perform any function that an application or a human could do on a screen. Agentic AI can also import an API and infer where an API call might be better to use than a screen GUI interaction.

He mentioned that Kamiwaza can be used to generate and replace a lot of what enterprises do today with Robotics Process Automation (RPAs). Luke feels that anything an enterprise was doing with RPA’s can be done better with Kamiwaza AI agents.

SaaS solution tasks are also something AgenticAI can easily displace . Luke said at one customer they went from using SAP APIs to provide information to SAP, to using APIs to extract information from SAP, to completely replacing the use of SAP for this task at the enterprise. 

How much of this is fiction or real is subject of some debate in the industry. But Kamiwaza AI is pushing the envelope on what can and can’t be done. And with their AI aaS offering, customers are making use of AI like they never thought possible before. .

Kamiwaza AI has a community edition, a free download that’s functionally restricted, and provides a desktop experience of Kamiwaza AI’s stack. Luke sees this as something a developer could use to develop to Kamiwaza APIs and test functionality before loading on the enterprise cluster. 

We asked where they were finding the most success. Luke mentioned anyone that’s heavily regulated, where data movement and access were strictly constrained. And they were focused on large, multi-data center, enterprises.

Luke mentioned that Kamiwaza AI has been doing a number of hackathons with AI Tinkerers around the world. He suggested prospects take a look at what they have done with them and perhaps join them in the next hackathon in their area.

Luke Norris, CEO & Co-Founder, Kamiwaza AI

Luke Norris is the co-founder of Kamiwaza.AI, driving enterprise AI innovation with a focus on secure, scalable GenAI deployments. With extensive experience raising over $100M in venture capital and leading global AI/ML deployments for Fortune 500 companies.

Luke is passionate about enabling enterprises to unlock the full potential of AI with unmatched flexibility and efficiency.

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168: GreyBeards Year End 2024 podcast

It’s time once again for our annual YE GBoS podcast. This year we have Howard back making a guest appearance with our usual cast of Jason and Keith in attendance. And the topic de jour seemed to be AI rolling out to the enterprise and everywhere else in the IT world. 

We led off with our discussion from last year, AI (again) but then it was all about new announcements, new capabilities and new functionality. This year it’s all about starting to take AI tools and functionality and make them available to help optimize organizational functionality.

We talked some about RAGs and Chatbots but these seemed almost old school.

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Agentic AI

Keith mentioned Agentic AI which purports to improve businesses by removing/optimizing intermediate steps in business processes. If one can improve human and business productivity by 10%, the impact on the US and world’s economies would  be staggering.

And we’re not just talking about knowledge summarization, curation, or discussion, agentic AI takes actions that would have been previously done by a human, if done at all.  

Manufacturers could use AI agents to forecast sales, allowing the business to optimize inventory positioning to better address customer needs. 

Most, if not all, businesses have elaborate procedures which require a certain amount of human hand holding. Reducing human hand holding, even a little bit, with AI agents, that never slees, and can occasionally be trained to do better, could seriously help the bottom and top lines for any organization 

We can see evidence of Agentic AI proliferating in SAAS solutions, i.e., SalesForce, SAP, Oracle and all others are spinning out Agentic AI services.

I think it was Jason that mentioned GEICO, a US insurance company, is re-factoring, re-designing and re-implementing all their applications to take advantage of Agentic AI and other AI options. 

AI’s impact on HW & SW infrastructure

The AI rollout is having dramatic impacts on both software and hardware infrastructure. For example, customers are building their own OpenStack clouds to support AI training and inferencing.

Keith mentioned that AWS just introduced S3 Tables, a fully managed services meant to store and analyze massive amounts of tabular data for analytics. Howard mentioned that AWS’s S3 Tables had to make a number of tradeoffs to use immutable S3 object storage. VAST’s Parquet database provides the service without using immutable objects.

Software impacts are immense as AI becomes embedded in more and more applications and system infrastructure. But AI’s hardware impacts may be even more serious.

Howard made mention of the power zero sum game, meaning that most data centers have a limited amount of power they support. Any power saved from other IT activities are immediately put to use to supply more power to AI training and infererencing.

Most IT racks today support equipment that consumes 10-20Kw of power. AI servers will require much more

Jason mentioned one 6u server with 8 GPUS that cost on the order of 1 Ferrari ($250K US), draws 10Kw of power, with each GPU having 2-400 GigE links not to mention the server itself having 2-400 GigE links. So a single 6U (GPU) server has 18-400GbE links or could need 7.2Tb of bandwidth.

Unclear how many of these one could put in a rack but my guess is it’s not going to be fully populated. 6 of these servers would need >42Tb of bandwidth and over 60Kw of power and that’s not counting the networking and other infrastructure required to support all that bandwidth.  

Speaking of other infrastructure, cooling is the other side of this power problem. It’s just thermodynamics, power use generates heat, that heat needs to be disposed of. And with 10Kw servers we are talking a lot of heat. Jason mentioned that at this year’s SC24 conference, the whole floor was showing off liquid cooling.  Liquid cooling was also prominent at OCP.

At the OCP summit this year Microsoft was talking about deploying near term 150Kw racks and down the line 1Mw racks. AI’s power needs are why organizations around the world are building out new data centers in out of the way places that just so happen to have power and cooling nearby. 

Organizations have an insatiable appetite for AI training data. And good (training) data is getting harder to find. Solidigm latest 122TB SSD may be coming along just when the data needs for AI are starting to take off.

SCI is pivoting

We could have gone on for hours on AI’s impact on IT infrastructure, but I had an announcement to make.

Silverton Consulting will be pivoting away from storage to a new opportunity that is based in space. I discuss this on SCI’s website but the opportunities for LEO and beyond services are just exploding these days and we want to be a part of that. 

What that means for GBoS is TBD. But we may be transitioning to something more broader than just storage. But heck we have been doing that for years.

Stay tuned, it’s going to be one hell of a ride

Jason Collier, Principal Member Of Technical Staff at AMD, Data Center and Embedded Solutions Business Group

Jason Collier (@bocanuts) is a long time friend, technical guru and innovator who has over 25 years of experience as a serial entrepreneur in technology.

He was founder and CTO of Scale Computing and has been an innovator in the field of hyperconvergence and an expert in virtualization, data storage, networking, cloud computing, data centers, and edge computing for years.

He’s on LinkedIN. He’s currently working with AMD on new technology and he has been a GreyBeards on Storage co-host since the beginning of 2022

Howard Marks, Technologist Extraordinary and Plenipotentiary at VAST Data

Howard Marks is Technologist Extraordinary and Plenipotentiary at VAST Data, where he explains engineering to customers and customer requirements to engineers.

Before joining VAST, Howard was an independent consultant, analyst, and journalist, writing three books and over 200 articles on network and storage topics since 1987 and, most significantly, a founding co-host of the Greybeards on Storage podcast.

Keith Townsend, President of The CTO Advisor, a Futurum Group Company

Keith Townsend (@CTOAdvisor) is a IT thought leader who has written articles for many industry publications, interviewed many industry heavyweights, worked with Silicon Valley startups, and engineered cloud infrastructure for large government organizations. Keith is the co-founder of The CTO Advisor, blogs at Virtualized Geek, and can be found on LinkedIN.

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163: GreyBeards talk Ultra Ethernet with Dr J Metz, Chair of UEC steering committee, Chair of SNIA BoD, & Tech. Dir. AMD

Dr J Metz, (@drjmetz, blog) has been on our podcast before mostly in his role as SNIA spokesperson and BoD Chair, but this time he’s here discussing some of his latest work on the Ultra Ethernet Consortium (UEC) (LinkedIN: @ultraethernet, X: @ultraethernet)

The UEC is a full stack re-think of what Ethernet could do for large single application environments. UEC was originally focused on HPC, with 400-800 Gbps networks and single applications like simulating a hypersonic missile or airplane. But with the emergence of GenAI and LLMs, UEC could also be very effective for large AI model training with massive clusters doing a single LLM training job over months. Listen to the podcast to learn more.

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The UEC is outside the realm of normal enterprise environments. But as AI training becomes more ubiquitous, who knows whether UEC may not find a place in the enterprise. However, it’s not intended for mixed network environments with multiple applications. It’s a single application network.

One wouldn’t think, HPC was a big user of Ethernet for their main network. But Dr J pointed out that the top 3 of the HPC 500, all use Ethernet and more are looking to use it in the future.

UEC is essentially an optimized software stack and hardware for networking used by single application environments. These types of workloads are constantly pushing the networking envelope. And by taking advantage of the “special networking personalities” of these workloads, UEC can significantly reduce networking overheads, boosting bandwidth and workload execution.

The scale of networks is extreme. The UEC is targeting up to a million endpoints, over >100K servers, with each network link >100Gbps and more likely 400-800Gpbs. With the new (AMD and others) networking cards coming out that support 4 400/800Gbps network ports, having a pair of these on each server, with 100K server cluster gives one 800K endpoints. A million is not that far away when you think of it at that scale.

Moreover, LLM training and HPC work are starting to look more alike these days. Yes there are differences but the scale of their clusters are similar, and the way work is sometimes fed to them is similar, which leads to similar networking requirements

UEC is attempting to handle a 5% problem. That is 95% of the users will not have 1M endpoints in their LAN, but maybe 5% will and for these 5%, a more mixed networking workload is unnecessary. In fact, a mixed network becomes a burden slowing down packet transmission.

UEC is finding that with a few select networking parameters, almost like workload fingerprints, network stacks can be much more optimized than current Ethernet and thereby support reduced packet overheads, and more bandwidth.

AI and HPC networks share a very limited set of characteristics which can be used as fingerprints. These characteristics are like reliable or unreliable transport, ordered or unordered delivery, multi-path packet spraying or not, etc, With a set of these types of parameters, selected for an environment, UEC can optimize a network stack to better support a million networking endpoints

We asked where CXL fits in with UEC? DrJ said it could potentially be an entity on the network but he sees CXL more as a within server or between a tight (limited) cluster of servers, solution rather than something on a UEC network.

Just 12 months ago the UEC had 10 members or so and this past week they were up to 60. UEC seems to have struck a chord.

The UEC plans to release a 1.0 specification, near the end of this year. UEC 1.0 is intended to operate on current (>100Gbps) networking equipment with firmware/software changes.

Considering the UEC was just founded in 2023, putting out their 1.0 technical spec. within 1.5 years is astonishing. But also speaks volumes to the interest in the technology.

The UEC has a blog post which talks more about UEC 1.0 specification and the technology behind it.

Dr J Metz, Chair of UEC Steering Committee, Chair of SNIA BoD, Technical Director of Systems Design, AMD

J works to coordinate and lead strategy on various industry initiatives related to systems architecture. Recognized as a leading storage networking expert, J is an evangelist for all storage-related technology and has a unique ability to dissect and explain complex concepts and strategies. He is passionate about the innerworkings and application of emerging technologies.

J has previously held roles in both startups and Fortune 100 companies as a Field CTO,  R&D Engineer, Solutions Architect, and Systems Engineer. He has been a leader in several key industry standards groups, sitting on the Board of Directors for the SNIA, Fibre Channel Industry Association (FCIA), and Non-Volatile Memory Express (NVMe). A popular blogger and active on Twitter, his areas of expertise include NVMe, SANs, Fibre Channel, and computational storage.

J is an entertaining presenter and prolific writer. He has won multiple awards as a speaker and author, writing over 300 articles and giving presentations and webinars attended by over 10,000 people. He earned his PhD from the University of Georgia.

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159: GreyBeards Year End 2023 Wrap Up

Jason and Keith joined Ray for our annual year end wrap up and look ahead to 2024. I planned to discuss infrastructure technical topics but was overruled. Once we started talking AI, we couldn’t stop.

It’s hard to realize that Generative AI and ChatGPT in particular, haven’t been around that long. We discussed some practical uses Keith and Jason had done with the technology.

Keith mentioned its primary skill is language expertise. He has used it to help write up proposals. He often struggles to convince CTO Advisor non-sponsors of the value they can bring and found that using GenAI has helped do this better.

Jason mentioned he uses it to create BASH, perl, and PowerShell scripts. He says it’s not perfect but can get ~80% there and with a few tweaks, is able to have something a lot faster than if he had to do it completely by hand. He also mentioned its skill in translating from one scripting language to others and how well the code it generates is documented (- that hurt).

I was the odd GreyBeard out, having not used any GenAI, proprietary or not. I’m still working to get a reinforcement learning task to work well and consistently. I figured once I mastered that, I train an LLM on my body of (text and code) work (assuming of course someone gifts me a gang of GPUs).

I agreed GenAI are good at (English) language and some coding tasks (where lot’s of source code exists, such as java, scripting, python, etc.).

However, I was on a MLops slack channel and someone asked if GenAI could help with IBM RPG II code. I answered, probably not. There’s just not a lot of RPG II code publicly accessible on the web and the structure of RPG was never line of text/commands oriented.

We had some heated discussion on where LLMs get the data to train with. Keith was fine with them using his data. I was not. Jason was neutral.

We then turned to what this means to the white collar workers who are coding and writing text. Keith made the point that this has been a concern throughout history, at least since the industrial revolution.

Machines come along, displace work that was done by hand, increase production immensely, reduce costs. Organizations benefit, but people doing those jobs need to up level their skills, to take advantage of the new capabilities.

Easy for us to say, as we, except for Jason, in his present job, are essentially entrepreneurs and anything that helps us deliver more value, faster, easier or less expensively, is a boon for our businesses.

Jason mentioned, Stephen Wolfram wrote a great blog post discussing LLM technology (see What is ChatGPT doing … and why does it work). Both Jason and Keith thought it did a great job about explaining the science and practice behind LLMs.

We moved on to a topic harder to discuss but of great relevance to our listeners, GenAI’s impact on the enterprise.

It reminds me of when Cloud became most prominent. Then “C” suites tasked their staff to adopt “the cloud” anyway they could. Today, “C” suites are tasking their staff to determine what their “AI strategy” is and when will it be implemented.

Keith mentioned that this is wrong headed. The true path forward (for the enterprise) is to focus on what are the business problems and how can (Gen)AI address (some of) them.

AI is so varied and its capabilities across so many fields, is so good nowadays ,that organizations should really look at AI as a new facility that can recognize patterns, index/analyze/transform images, summarize/understand/transform text/code, etc., in near real-time and see where in the enterprise that could help.

We talked about how enterprises can size AI infrastructure needed to perform these activities. And it’s more than just a gaggle of GPUs.

MLcommons’s MLperf benchmarks can help show the way, for some cases, but they are not exhaustive. But it’s a start.

The consensus was maybe deploy in the cloud first and when the workload is dialed in there, re-home it later. With the proviso that hardware needed is available.

Our final topic was the Broadcom VMware acquisition. Keith mentioned their recent subscription pricing announcements vastly simplified VMware licensing, that had grown way too complex over the decades.

And although everyone hates the expense of VMware solutions, they often forget the real value VMware brings to enterprise IT.

Yes hyperscalars and their clutch of coders, can roll their own hypervisor services stacks, using open source virtualization. But the enterprise has other needs for their developers. And the value of VMware virtualization services, now that 128 Core CPUs are out, is even higher.

We mentioned the need for hybrid cloud and how VCF can get you part of the way there. Keith said that dev teams really want something like “AWS software” services running on GCP or Azure.

Keith mentioned that IBM Cloud is the closest he’s seen so far to doing what Dev wants in a hybrid cloud.

We all thought when DNN’s came out and became trainable, and reinforcement learning started working well, that AI had turned a real corner. Turns out, that was just a start. GenAI has taken DNNs to a whole other level and Deepmind and others are doing the same with reinforcement learning.

This time AI may actually help advance mankind, if it doesn’t kill us first. On the latter topic you may want to checkout my RayOnStorage AGI series of blog posts (latest … AGI part-8)

Jason Collier, Principal Member Of Technical Staff at AMD, Data Center and Embedded Solutions Business Group

Jason Collier (@bocanuts) is a long time friend, technical guru and innovator who has over 25 years of experience as a serial entrepreneur in technology.

He was founder and CTO of Scale Computing and has been an innovator in the field of hyperconvergence and an expert in virtualization, data storage, networking, cloud computing, data centers, and edge computing for years.

He’s on LinkedIN. He’s currently working with AMD on new technology and he has been a GreyBeards on Storage co-host since the beginning of 2022

Keith Townsend, President of The CTO Advisor a Futurum Group Company

Keith Townsend (@CTOAdvisor) is a IT thought leader who has written articles for many industry publications, interviewed many industry heavyweights, worked with Silicon Valley startups, and engineered cloud infrastructure for large government organizations.

Keith is the co-founder of The CTO Advisor, blogs at Virtualized Geek, and can be found on LinkedIN.

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154: GreyBeards annual VMware Explore wrap-up podcast

Thanks, once again to The CTO Advisor|Keith Townsend, (@CTOadvisor) for letting us record the podcast in his studio. VMware Explore this year was better than last year. The show seemed larger. the show floor busier, the Hub better and the Hands-On Lab much larger than I ever remember before. The show seems to be growing, but still not at the pre-pandemic levels, but the trend is good.

The engineers have been busy at VMware this past year. Announced at the show include Private AI Foundation, a way for enterprises to train open source LLMs on corporate data kept private, a significant re-direct to VMware Edge environments moving from the push model updates to push model updates, and vSAN Max, NSX+, Tanzu App Engine, and more. And we heard that Brocade is clearing more hurdles to the acquisition. Listen to the podcast to learn more.

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Private AI plays to VMware’s strengths and its control over on-prem processing. Customers need a safe space and secured data to train corporate ChatBots curated on corporations knowledge base. VMware rolled this out two ways,

  • Reference architecture approach based on Ray cluster management, KubeFlow, PyTorch, VectorDB, GPU Scaling (NVLink/NVswitch), vSAN fast path (RDMA, GPUdirect), and deep learning VMs. There was no discussion of tie ins to the Data Persistence (object) storage.
  • Proprietary NVIDIA approach based on NVIDIA workbench, TensorRT, NeMO, NVIDIA GPU & Network Operator

By having both approaches VMware provides alternatives for those wanting a non-proprietary solution. And with with AI/MLOps moving so fast, the open source may be better able to keep up.

The tie in with NVIDIA is a natural extension of what VMware have been doing with GPUs and DPUs, etc.

Also, VMware announced a technological partnership with Hugging Face. We were somewhat concerned with all the focus on LLM and GenAI but the agreement with Hugging Face goes beyond just LLMs.

VMware Edge solutions are pivoting. Apparently, VMware is moving from the vSphere pull model of code updates in the field which seems to handle 64 server, multi-cluster environments without problem to more of a YAML-GitHub push model of IoT device updates that seems better able to manage fleets of 1K to 100K devices in the field.

With the new model one creates a GitHub repo and a YAML file describing the code update to be done and all your IoT devices just get’s updated to the new level.

Once again the Brocade acquisition is on everyone’s mind. As I got to the show, one analyst asked if this was going to be the last VMware Explore. I highly doubt that, but Brocade will make lots of changes once the transaction closes. One thing mentioned at the show was that Brocade will make an immediate, additional $1B investment in R&D. The deal had provisionally passed the UK regulatory body and was on track to close near the end of October.

Other news from the show:

  • The Tanzu brand is broadening. Tanzu Application Platform (TAP) still exists but they have added a new App Engine is to take the VMware management approach to K8s clusters, other cloud infrastructure and the rest of the IT world. Tanzu Intelligent Services also now supports policy guardrails, cost control, management insight and migration services for other environments.
  • vSAN Max, which supports disaggregation (separation) of storage and compute is available. vSAN Max becomes a full fledged, standalone storage system that just happens to run on top of vSphere. Disaggregated (vSAN Max) storage and (regular vSAN) HCI can co-exist as different mounted datastores and vSAN Max supports PB of storage.
  • Workspace One is updated to provide enhanced digital experience monitoring that adds coverage of what Workspace One users are actually experiencing.
  • NSX+ continues to roll out. VMware mentioned that the number one continuing problem with hybrid cloud/multi-cloud setup is getting the networking right. NSX+ will reduce this complexity by becoming a management/configuration overlay over any and all cloud/on-prem networking for your environment(s).
  • VMware chatbots for Tanzu, Workspace One and NSX+ are now in tech preview and will supply intelligent assistants for these solutions. Based on LLM/GenAI and trained on VMware’s extensive corporate knowledge base, the chatbots will help admins focus on the signal over the noise and will provide recommendations on how to resolve issues. .

Jason Collier, Principal Member of Technical Staff, AMD

Jason Collier (@bocanuts) is a long time friend, technical guru and innovator who has over 25 years of experience as a serial entrepreneur in technology. Jason currently works at AMD focused on emerging technology for IT, IoT and anywhere else in the world and across the universe that needs compute, storage or networking resources.

He was Chief Evangelist, CTO & Co-Founder of Scale Computing and has been an innovator in the field of hyper-convergence and an expert in virtualization, data storage, networking, cloud computing, data centers, and edge computing for years.

He has also been another co-founder, director of research, VP of technical operations and director of operations at other companies over his long career prior to AMD and Scale.

He’s on LinkedIN.