SambaNova Adds GPT Banking to its Growing Dataflow-as-a-Service Lineup

By John Russell

February 23, 2022

AI chip and systems specialist SambaNova today announced a new offering – SambaNova GPT Banking – intended to simplify and speed deployment of GPT models in the financial services industry. The new service is available now and becomes part of SambaNova’s expanding dataflow-as-a-service portfolio although pricing was not disclosed.

Marshall Choy, SambaNova

“Think of dataflow-as-a-service as the umbrella product family for the as-a-service offerings. We have three of them. There’s one for language, one for computer vision and one for recommendation. Within the subcategory of language, we have GPT, and now we’re extending that to an industry-specific GPT banking solution,” said Marshall Choy, SVP, product, in a briefing with HPCwire and EnterpriseAI.

GPT (generative pre-trained transformer) models have mushroomed in size and effectiveness in recent years. OpenAI’s GPT-3, the reigning king of GPT, has a capacity of 175 billion machine learning parameters. GPT models have proven extremely effective in natural language processing and new applications are constantly emerging.

Rodrigo Liang, CEO and co-founder of SambaNova, said in the official release, “GPT Banking has the potential to transform nearly every aspect of banking, from improving operations to managing risk and compliance. Customers tell me they’re most excited about GPT Banking’s ability to truly personalize the customer experience, with richer insights that enable them to understand customers’ evolving needs.”

Within the banking industry SambaNova cites four prominent GPT applications:

  • Sentiment analysis: scan social media, press and blogs to understand market, investor and stakeholder sentiment.
  • Entity recognition: reduce human error, classify documents and reduce manual/repetitive work.
  • Language generation: process, transcribe and prioritize claims, extract necessary information and create documents to improve customer satisfaction.
  • Language translation: language translation to expand customer base.

“We built GPT Banking to improve bank’s competitiveness and efficiency while accelerating their digital transformation,” stated Liang. “AI is the quickest and most cost-efficient tool to do that today and our service can be deployed and delivering value in weeks.” GPT Banking, says the company, is built for banks’ large language models and is offered as a subscription service to simplify the process of deploying the most advanced language models in a fraction of the time.

The big challenge with GPT, as with AI models broadly, is that training such large models is time-consuming and computationally intensive. Specialized hardware and software requirements along with scarce or absent in-house AI expertise are all significant obstacles. Like other AI chip/system newcomers, SambaNova has formidable hardware to sell (DataScale systems based on its proprietary AI chip) and is happy to sell it to you. But such large system sales are few and far between. One example is installation of a large SambaNova system at Lawrence Livermore National Laboratory, which has an abundance of expertise and (relatively speaking) budget.

Mainstreaming the use of large-model AI techniques, such as GPT, at least for now, seems better suited to the X-as-a-service approach. SambaNova prefers dataflow-as-a-service as the descriptor to emphasize the function rather than the infrastructure. It’s still not inexpensive but it is significantly easier and faster to deploy.

Choy said, “Let’s face it, going to Lawrence Livermore, which we’ve done and sold them one, is one type of adoption. But the way this dataflow-as-a-service came up was I had customers who said what you’ve got with your DataScale product sounds great if you’re Lawrence Livermore or Google, [but] I don’t have 300 or 3000 data scientists. I don’t have all the ML engineers. What I’ve got is a team of half a dozen people with a budget to expand that team from six to nine people in the next 18 months. What can you do for me?”

The larger market (revenue) for SambaNova is likely to be dataflow-as-a-service offerings rather than system sales, noted Choy. In practice, the company’s dataflow-as-a-service infrastructure is either co-located on your premises or hidden in a cloud. The models are largely pre-built by SambaNova then refined to reflect a client’s needs. Clients interact with the system via an API.

The GPT Banking offering is a good example of challenge and upside. Data control issues (confidentiality and security) loom large in the financial industry as they do in many commercial settings.

“What dataflow-as-a-service provides is a very clean break between the customer and us [with regard to] data privacy and data view-ability. Banking customers say, ‘We cannot be handing over our data to you.’ We agree. Our view is we’re providing you with the service and the APIs to train the model. Then you’re providing the data set, we never have possession or visibility in that data set, because that’s your IP. It creates a very clean fence between us and customer data. We never see it. It just gets run through the model,” said Choy.

“Whether you’re an end user, such as a bank or a cloud services provider who’s going to be the intermediary, it’s all the same. I’m deploying systems in your data center. I own and manage and maintain those systems, but you’re putting them behind your firewall. What you have to tell me is you have to fill out my site planning guide, which is pretty straightforward – power, cooling, floor weight, dimensions of the door, etc. – so we can ensure the racks can roll in easily.”

This on-premises deployment model, said Chow, is emerging as a preferred approach across regulated industries with stringent data control regulations. “Others folks whose data is already up in the cloud, often want to go to the cloud. We can do that, too, we can put that into any number of cloud services providers,” he said.

Given the open-source availability of GPT models and the fact that SambaNova already has a GPT offering, one question is what real advantage does the GPT Banking offering provide. Choy said, “We found the general-purpose GPT model, whether that’s ours or someone else’s, is okay for many things but not really great at anything. These are big models trained on big public datasets. They’re effectively like Swiss Army knives. If you’re in banking, you really care a lot about some specific downstream tasks like sentiment analysis, like text extraction, like document creation, like entity recognition. What we’ve done is we’ve taken the pre-trained GPT based model and done another step of pre-training with an industry specific needs.”

It will be interesting to monitor how SambaNova’s dataflow-as-a-service fares in the market. Hyperion Research analyst Steve Conway said, “SambaNova’s strategy of offering a domain-specific GPT banking service makes good sense. AI is already highly useful, but isn’t far enough along yet for versatile experiential learning products or services that can handle many domains effectively. Security is always a concern in the banking sector, but I’m sure SambaNova’s services adhere to all the appropriate regulations and practices.”

Here’s a link to the announcement.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

ARM, Fujitsu Targeting Open-source Software for Power Efficiency in 2-nm Chip

July 19, 2024

Fujitsu and ARM are relying on open-source software to bring power efficiency to an air-cooled supercomputing chip that will ship in 2027. Monaka chip, which will be made using the 2-nanometer process, is based on the Read more…

SCALEing the CUDA Castle

July 18, 2024

In a previous article, HPCwire has reported on a way in which AMD can get across the CUDA moat that protects the Nvidia CUDA castle (at least for PyTorch AI projects.). Other tools have joined the CUDA castle siege. AMD Read more…

Quantum Watchers – Terrific Interview with Caltech’s John Preskill by CERN

July 17, 2024

In case you missed it, there's a fascinating interview with John Preskill, the prominent Caltech physicist and pioneering quantum computing researcher that was recently posted by CERN’s department of experimental physi Read more…

Aurora AI-Driven Atmosphere Model is 5,000x Faster Than Traditional Systems

July 16, 2024

While the onset of human-driven climate change brings with it many horrors, the increase in the frequency and strength of storms poses an enormous threat to communities across the globe. As climate change is warming ocea Read more…

Researchers Say Memory Bandwidth and NVLink Speeds in Hopper Not So Simple

July 15, 2024

Researchers measured the real-world bandwidth of Nvidia's Grace Hopper superchip, with the chip-to-chip interconnect results falling well short of theoretical claims. A paper published on July 10 by researchers in the U. Read more…

Belt-Tightening in Store for Most Federal FY25 Science Budets

July 15, 2024

If it’s summer, it’s federal budgeting time, not to mention an election year as well. There’s an excellent summary of the curent state of FY25 efforts reported in AIP’s policy FYI: Science Policy News. Belt-tight Read more…

SCALEing the CUDA Castle

July 18, 2024

In a previous article, HPCwire has reported on a way in which AMD can get across the CUDA moat that protects the Nvidia CUDA castle (at least for PyTorch AI pro Read more…

Aurora AI-Driven Atmosphere Model is 5,000x Faster Than Traditional Systems

July 16, 2024

While the onset of human-driven climate change brings with it many horrors, the increase in the frequency and strength of storms poses an enormous threat to com Read more…

Shutterstock 1886124835

Researchers Say Memory Bandwidth and NVLink Speeds in Hopper Not So Simple

July 15, 2024

Researchers measured the real-world bandwidth of Nvidia's Grace Hopper superchip, with the chip-to-chip interconnect results falling well short of theoretical c Read more…

Shutterstock 2203611339

NSF Issues Next Solicitation and More Detail on National Quantum Virtual Laboratory

July 10, 2024

After percolating for roughly a year, NSF has issued the next solicitation for the National Quantum Virtual Lab program — this one focused on design and imple Read more…

NCSA’s SEAS Team Keeps APACE of AlphaFold2

July 9, 2024

High-performance computing (HPC) can often be challenging for researchers to use because it requires expertise in working with large datasets, scaling the softw Read more…

Anders Jensen on Europe’s Plan for AI-optimized Supercomputers, Welcoming the UK, and More

July 8, 2024

The recent ISC24 conference in Hamburg showcased LUMI and other leadership-class supercomputers co-funded by the EuroHPC Joint Undertaking (JU), including three Read more…

Generative AI to Account for 1.5% of World’s Power Consumption by 2029

July 8, 2024

Generative AI will take on a larger chunk of the world's power consumption to keep up with the hefty hardware requirements to run applications. "AI chips repres Read more…

US Senators Propose $32 Billion in Annual AI Spending, but Critics Remain Unconvinced

July 5, 2024

Senate leader, Chuck Schumer, and three colleagues want the US government to spend at least $32 billion annually by 2026 for non-defense related AI systems.  T Read more…

Atos Outlines Plans to Get Acquired, and a Path Forward

May 21, 2024

Atos – via its subsidiary Eviden – is the second major supercomputer maker outside of HPE, while others have largely dropped out. The lack of integrators and Atos' financial turmoil have the HPC market worried. If Atos goes under, HPE will be the only major option for building large-scale systems. Read more…

Everyone Except Nvidia Forms Ultra Accelerator Link (UALink) Consortium

May 30, 2024

Consider the GPU. An island of SIMD greatness that makes light work of matrix math. Originally designed to rapidly paint dots on a computer monitor, it was then Read more…

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…


Nvidia Economics: Make $5-$7 for Every $1 Spent on GPUs

June 30, 2024

Nvidia is saying that companies could make $5 to $7 for every $1 invested in GPUs over a four-year period. Customers are investing billions in new Nvidia hardwa Read more…

Nvidia Shipped 3.76 Million Data-center GPUs in 2023, According to Study

June 10, 2024

Nvidia had an explosive 2023 in data-center GPU shipments, which totaled roughly 3.76 million units, according to a study conducted by semiconductor analyst fir Read more…

AMD Clears Up Messy GPU Roadmap, Upgrades Chips Annually

June 3, 2024

In the world of AI, there's a desperate search for an alternative to Nvidia's GPUs, and AMD is stepping up to the plate. AMD detailed its updated GPU roadmap, w Read more…

Some Reasons Why Aurora Didn’t Take First Place in the Top500 List

May 15, 2024

The makers of the Aurora supercomputer, which is housed at the Argonne National Laboratory, gave some reasons why the system didn't make the top spot on the Top Read more…

Intel’s Next-gen Falcon Shores Coming Out in Late 2025 

April 30, 2024

It's a long wait for customers hanging on for Intel's next-generation GPU, Falcon Shores, which will be released in late 2025.  "Then we have a rich, a very Read more…

Leading Solution Providers


Google Announces Sixth-generation AI Chip, a TPU Called Trillium

May 17, 2024

On Tuesday May 14th, Google announced its sixth-generation TPU (tensor processing unit) called Trillium.  The chip, essentially a TPU v6, is the company's l Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

IonQ Plots Path to Commercial (Quantum) Advantage

July 2, 2024

IonQ, the trapped ion quantum computing specialist, delivered a progress report last week firming up 2024/25 product goals and reviewing its technology roadmap. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

The NASA Black Hole Plunge

May 7, 2024

We have all thought about it. No one has done it, but now, thanks to HPC, we see what it looks like. Hold on to your feet because NASA has released videos of wh Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

  • arrow
  • Click Here for More Headlines
  • arrow