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MosaicML

MosaicML needed cloud infrastructure that could support growing demand for AI services and let it train large, complex generative AI models faster and at lower cost for enterprise customers.

cost savings of up to 80 percent
saved us thousands of hours and tens of thousands of dollars in efficiencies
scaling to thousands of GPUs
up to 50 percent faster performance

Reported impact

Business outcome

MosaicML reports faster, lower-cost model training and scalable AI infrastructure, enabling it and customers such as Twelve Labs to grow AI capacity and deploy models more efficiently.

  • Cost Reduction
  • Productivity Improvement
  • Scalability

Solution

MosaicML added Oracle Cloud Infrastructure (OCI) as the preferred infrastructure underneath its managed machine-learning platform so it could meet rising demand for enterprise generative AI services. In the described setup, customers bring large training datasets and model-building workloads to MosaicML, while the platform offloads cluster and hardware complexity to OCI. MosaicML uses OCI AI infrastructure and OCI Compute bare metal GPU instances to run training and inference jobs, with NVIDIA A100 Tensor Core GPUs supplying accelerator capacity. OCI Supercluster contributes ultra-low-latency networking and HPC storage so jobs can scale across very large GPU pools. The platform then helps enterprises operationalize custom models, and customers such as Twelve Labs deploy those trained multimodal models to search, classify, and otherwise use video data more effectively.

Data flow

MosaicML customers bring large training datasets and model workloads into MosaicML workflows running on Oracle Cloud Infrastructure. Training and inference jobs execute on OCI AI infrastructure using OCI Compute bare metal GPU instances, while OCI Supercluster supplies low-latency networking and HPC storage and NVIDIA A100 GPUs provide acceleration. The resulting models are then deployed by customers such as Twelve Labs for video search, classification, and related applications.

Solution architecture

5 components · 2 layers
  1. Compute
    • OCI AI infrastructure Supplies the high-performance AI environment MosaicML uses to speed training and lower the cost of generative AI workloads.
    • OCI Compute bare metal GPU instances Runs large model training jobs with dedicated GPU-backed compute capacity.
    • NVIDIA A100 Tensor Core GPUs Accelerate MosaicML's training workloads for large generative and multimodal AI models.
  2. Uncategorized
    • Oracle Cloud Infrastructure (OCI) Provides the preferred cloud foundation underneath MosaicML's managed platform for large-scale AI model training and deployment.
    • OCI Supercluster Provides the cluster networking and HPC storage needed to train large, complex models at scale.

Architecture clues

  • Access to the latest NVIDIA GPUs
  • Large compute block sizes for scaling to thousands of GPUs
  • OCI Compute instances are connected by a high-performance ethernet network using RoCE v2
  • OCI Supercluster provides ultra-low latency cluster networking and HPC storage
  • Very high bandwidth interconnect between nodes

Evidence from the source

"By taking advantage of MosaicML's platform running on OCI and OCI's AI infrastructure, Twelve Labs has been able to efficiently scale and deploy its AI models to help users effortlessly search, classify, and more effectively utilize their video data for various applications."
"MosaicML adds Oracle Cloud Infrastructure to its managed platform, accelerating training of AI models for businesses"
"MosaicML enables us to efficiently manage our large AI clusters, while OCI's AI infrastructure ensures we don't have to compromise on speed, which has saved us thousands of hours and tens of thousands of dollars in efficiencies."
"When training models with massive troves of data in the cloud, every minute counts-and with OCI, we pay less than with other cloud providers and can scale almost linearly because of the way Oracle configured its interconnects."
"With OCI's high-performance AI infrastructure, MosaicML states that it has seen up to 50 percent faster performance and cost savings of up to 80 percent compared to other cloud providers."
"With OCI, MosaicML has been able to gain access to the latest NVIDIA GPUs, a very high bandwidth interconnect between nodes, and large compute block sizes for scaling to thousands of GPUs."
Transparency Evidence & methodology 100/100 reference value · 91% confidence · 2 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence91%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency10/10
    2 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Oracle

    MosaicML Trains Generative AI Models Faster with Oracle

    First-party
    https://www.oracle.com/news/announcement/mosaicml-trains-generative-ai-models-faster-with-oracle-2023-08-24/
    Source type
    Vendor-published customer story
    Published
    Aug 24, 2023
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Business outcome
    • Metrics
    • Architecture
    • Publication date
  2. Built In

    MosaicML company profile

    Independent secondary
    https://builtin.com/company/mosaicml

    The company profile identifies San Francisco, California, USA as MosaicML headquarters; it does not provide a defensible employee count.

    Source type
    Structured company profile
    Supports
    • Region

Date evidence

  • Published on source page Aug 24, 2023 · MosaicML · First-party
    Open date source
Not separately sourced Industry, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.