新思科技的动态

Really interesting webinar earlier today from NVIDIA covering considerations for deploying #generativeAI in production. Nice job Bethann Noble and Neal Vaidya! Some takeaways: 1. #genai is moving from experimentation to production quickly, and they provided some good tips on building RAG frameworks and pipelines, and good best practices in general. 2. Interesting insight that the workloads are going to shift from 80% training today to 80% inference in a few years (I rounded and simplified). 3. Arguably, keeping GPUs 100% utilized for inference is going to be more important, because your customers (either internal or external) are waiting. Hammerspace delivers fast performance to keep GPUs fully utilized, and brings your data to your GPU resources even if they are in the cloud. Learn more about how we are reducing latencies and increasing throughput in NVIDIA environments.

dstack is a lightweight, open-source alternative to #Kubernetes & Slurm, simplifying #AI container orchestration with multi-cloud & on-prem support. It natively supports NVIDIA, AMD, & TPU. #gpu

On-device AI is just better. It is advancing and will continue to close the gap until it is good enough to do much of what we may require. It is already better for: * Privacy:?Your data stays on your device. * Speed:?No waiting on the cloud for results. * Reliability:?Doesn't need the internet. * Customization:?Tailored experiences just for you. * Cost-effective:?No need to line the pockets of cloud providers. * Better for the environment:?Reduced energy consumption. Cloud-based AI will always have a role to play now and in the future, but I believe on-device + open source AI is better for people and society to use whenever possible. This option will become increasingly viable also as research continues to advance.

Big deal today. Brev.dev is becoming the software interface to unlock the complexity of decentralized compute. Over the next 7-8 years, GPUs will be one of the largest consumers of power as we begin using AI to be more productive and efficient in all parts of our lives. Today, Brev (??4) just launched a major partnership with Akash— the premier decentralized marketplace for GPUs, and have created the easiest way to develop with the leading open-source AI models and deploy on GPUs in the cloud. Now, developers can 1-click deploy on NVIDIA H100s & A100s at industry-leading pricing. This is a big, big deal.

There are a lot of things "old school" machine learning models (which, by the way, are still pretty powerful tools) and LLM models have in common: They can be a NIGHTMARE to deploy and maintain in a production environment! So, as you can imagine, I'm always trying to find approaches that make my life and my clients' lives easier. Recently, NVIDIA and Google Cloud released the NVIDIA NeMo framework on Google Cloud Marketplace which utilizes everyone's favorite "powerlifter" GKE for model training and distributed deployment. Three things that make this worth checking out: ? Creates stronger unity between GCP infra and the foundational hardware needed for scaled gen-AI systems (i.e., NVIDIA GPUs) ? The NeMo framework is delivered as a set of microservices allowing developers to "mix-and-match" what they need for specific use cases ? Greater abstraction! Has anyone had a chance to try this out yet? I'd love to hear your experiences! #gke #gcp #nvidia #gpu #llm

Joining the #AIConference next week and need fast, dependable GPU infrastructure at your fingertips? Look no further! Whether you're building cutting-edge AI models or scaling your ML workflows, GMI Cloud has you covered. ?? Let’s connect! Reach out here on LinkedIn or email [email protected] to schedule a time to meet and get a personal demo of our full-stack GPU platform. Don’t miss out on optimizing your AI projects with high-performance GPUs! #AIConference #SF #GPUCloud #AIInfrastructure #CloudComputing #GPUPower

We are excited to announce the general availability of Amazon EC2 P5e instances, designed to meet the demands of state-of-the-art generative AI models and high performance computing applications. These instances are powered by NVIDIA H200 Tensor Core GPUs and equipped with enhanced memory capacity, bandwidth, and network optimization, setting a new standard for high performance and scalability in deep learning, generative AI, and HPC workloads. Learn more about the core capabilities of these instances and their use cases, and see an example of getting started with these powerful instances by reading the blog post here: https://ift.tt/rgstOYE

If you want to go fast, go alone. If you want to go far, go together. At NexGen Cloud we understand and celebrate the power of collaboration. More than just having a client/supplier relation, we foster synergies that elevate our partners and help them succeed. If you win, we win. Choose to go far. Choose to win. Choose Hyperstack.

The ability to deliver large scales clusters on-demand by exploiting NVIDIA Spectrum and the Hewlett Packard Enterprise platform is a truly compelling offering. No lengthy set-up phase. No long-term commits. Perfect for the sporadic or temporary needs of Enterprise AI fine-tuning projects and smaller model training. Add to that EU data-residency and I am not aware of another provider this progressive in their AI platform offerings. Well done to Yann-Guirec Manac'h and the Scaleway team for thinking ahead of the game.

Don't sell your AI Data to a Cloud. GIGABYTE proposes a much better solution to safeguard your data. Gigabyte provides you with a full software suite and hardware (PC) to train a HUGE data set (brought to you by Gigabyte Ai Top). And it is COMPLETELY OFFLINE! GIGABYTE USA NVIDIA NVIDIA Taiwan NVIDIA AI AMD Intel Corporation #aitop #ai #llm #datatraining #aitraining #largelanguagemodels