Announcing up to 45% price reduction for Amazon EC2 NVIDIA GPU-accelerated instances
AWS chops up to45%from Amazon EC2 NVIDIA GPU prices. Now your AI training costs less even as GPUs play hard to get... read more
Updates and recent posts about AIStor.. Link
Link
@faun shared a link, 1 year ago
FAUN.dev()
AWS' custom chip strategy is showing results, and cutting into Nvidia's AI dominance
Graviton4just cranked up the juice to600 Gbps. In the grand race of public cloud champions, it's gunning straight for Nvidia's AI kingdom, powered by the formidableProject Rainier... read more
Link
@faun shared a link, 1 year ago
FAUN.dev()
Run the Full DeepSeek-R1-0528 Model Locally
DeepSeek-R1-0528's nanized form chops space needs down to162GB. But here's the kicker—without a solid GPU, it's like waiting for paint to dry... read more
Link
@faun shared a link, 1 year ago
FAUN.dev()
Mistral named most privacy-friendly AI, Google ranks low: report
Mistral AI’s “Le Chat” leads in privacy-focused AI, beating out OpenAI’s ChatGPT and xAI’s Grok.Consumer privacy concerns are reshaping the AI landscape, with 68% worried about online privacy.Regional regulations impact privacy practices, with Mistral AI benefiting from Europe’s strict GDPR rules... read more
Link
@faun shared a link, 1 year ago
FAUN.dev()
Training a Rust 1.5B Coder LM with Reinforcement Learning (GRPO)
DeepSeek-R1flips the script on training LLMs. Armed withGRPO, it challenges the industry heavies like OpenAI's o1 by playing smart with custom data and cleverly designed rewards. Imagine this: a humble 1.5B model, running on merely asingle H100, clocks in at an 80% build pass rate. It’s nibbling at .. read more
Link
@faun shared a link, 1 year ago
FAUN.dev()
Why AI Features Break Microservices Testing and How To Fix It
GenAIcomplexity confounds conventional testing. But savvy teams? They fast-track validation insandbox environments, slashing AI debug time from weeks down to mere hours... read more
Link
@faun shared a link, 1 year ago
FAUN.dev()
End to End Argo-Workflow for CI/CD
Argo Workflowsisn't just another tool; it sings for Kubernetes-native CI/CD. It juggles complex workflows as DAGs, brings dynamic execution to life with CRDs and parameters. Got a weekly CI? Automate it withCronWorkflows. Secure those Docker pushes using Kubernetes secrets, and let shared volumes ha.. read more
Link
@faun shared a link, 1 year ago
FAUN.dev()
A Journey Through Kafkian SplitDNS in a Multitenant Kubernetes Offering
SCHIPfaced off with tenant demands for serverless Kafka. Their weapon of choice? A crafty DNS trick usingCoreDNSand a few clevernode-local DNSadjustments. They kept multitenancy alive and kicking without wearing out the ops team. Nice move... read more
Link
@faun shared a link, 1 year ago
FAUN.dev()
The Ultimate Guide to Running Kubernetes in a Home Lab
K3sandMicroK8sshine in makeshift home labs with minimal hardware. Throw inLonghornfor storage andVelerofor backup bliss. Now that's a recipe for tech nirvana... read more
Link
@faun shared a link, 1 year ago
FAUN.dev()
Kubernetes 1.33: Resizing Pods Without the Drama (Finally!)
Kubernetes 1.33brings in-place pod vertical scaling, allowing you to adjust CPU and memory without restarting pods, a game-changer for seamless resource management in production workloads. This feature simplifies vertical pod autoscaling especially for stateful workloads like databases... read more
At its core, AIStor is a software-defined, distributed object store that runs on commodity hardware or in containerized environments like Kubernetes. Rather than being limited to traditional file or block interfaces, it exposes object storage semantics that scale from petabytes to exabytes within a single namespace, enabling consistent, flat addressing of vast datasets. It is engineered to sustain very high throughput and concurrency, with examples of multi-TiB/s read performance on optimized clusters.
AIStor is optimized specifically for AI and data-intensive workloads, where throughput, low latency, and horizontal scalability are critical. It integrates broadly with modern AI and analytics tools, including frameworks such as TensorFlow, PyTorch, Spark, and Iceberg-style table engines, making it suitable as the foundational storage layer for pipelines that demand both performance and consistency.
Security and enterprise readiness are central to AIStor’s design. It includes capabilities like encryption, replication, erasure coding, identity and access controls, immutability, lifecycle management, and operational observability, which are important for mission-critical deployments that must meet compliance and data protection requirements.
AIStor is positioned as a platform that unifies diverse data workloads — from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets — within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.
