Hammerspace and AWS
Unify Distributed, Siloed Data With a Global Namespace
that spans sites, cloud providers, and cloud regions
Migrate Data from On-Prem and Other Clouds to AWS
with Data-in-Place Assimilation and Data Orchestration
Run Demanding AI/HPC Workloads in the Cloud
with a standards-based, high-performance parallel file system
Simplify Hybrid-Cloud Infrastructure
By automating data movement
between on-prem storage to
cloud GPUs, without data copy
Streamline Cloud Data Pipelines and Workflows
with an end-to-end data platform that spans ingest through archive
Drive AWS Compute and Storage Consumption
with an agentless, cloud-native architecture that deploys on standard AWS infrastructure
LLM and Gen AI Training
“What Hammerspace does is pure magic.”
– Principal Engineer, Meta
The Hammerspace Data Platform
Hammerspace helps Amazon Web Services (AWS) customers unlock the full value of their data by breaking down silos across on-premises, cloud providers, and cloud regions. Together with AWS, we make it easy to move data where it’s needed, speed up AI and HPC projects, and simplify hybrid-cloud operations. The result is faster time to value, lower infrastructure complexity, and greater consumption of AWS compute and storage.
Powering the
Most Demanding Data Workflows
AI Inferencing
Unifies unstructured data, prewarms datasets, and feeds GPUs at wire speed for low-latency inference.
AI Training
Combines Tier 0 and parallel file system to keep GPUs saturated, accelerating training throughput, efficiency.
Cloud Computing
Unifies data across sites and clouds, enforcing policies and minimizing egress while accelerating cloud workloads.
Data Analytics
Creates one global namespace and orchestrates datasets to compute, speeding queries, pipelines, and interactive analytics.
Machine Learning
Unifies training and inference data, automates placement, and maximizes GPU utilization for faster model iteration.
GPU Acceleration
Feeds GPUs from Tier 0 shared NVMe, eliminating bottlenecks and boosting token throughput and TTFT.
High-performance Computing (HPC)
Delivers parallel file performance across sites, keeping compute saturated while simplifying data movement and access.
Retrieval Technologies (RAG)
Unifies files and objects, accelerating retrieval augmented generation while ensuring governance.
Performance Benchmarking Results on AWS
Hammerspace conducted MLPerf Storage Benchmark testing on the AWS cloud and demonstrated superior performance and scale for machine learning workloads.
Hammerspace delivered approximately 3x the performance* of the closest competitor – on standard AWS infrastructure, without any specialized networking, and without the use of a proprietary file system client.
*As measured by the number of ‘accelerators’ or simulated A100 GPUs supplied with data at >90% utilization, using extrapolated results based on the MLCommons MLPerf RESNET-50 benchmark
Achieve faster performance at Half the cost
In today’s hybrid, multi-cloud world, AI performance must scale without complexity, unnecessary overprovisioning, or runaway costs. Legacy file systems designed for HPC in the past are optimized to run in the datacenter, not in the cloud.
Hammerspace can scale performance and capacity independently to avoid over-provisioning, runs on standard cloud infrastructure, and operates at less than half the cost of Managed Lustre.
Read the AnalysisHammerspace can scale performance and capacity independently to avoid over-provisioning, runs on standard cloud infrastructure, and operates at less than half the cost of Managed Lustre.
Get Started with Hammerspace on AWS Cloud
Experience faster performance, lower latency, and reduced costs with Hammerspace on AWS Cloud.
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