Ron Diamant

Here is a short blog post on why SambaNova's RDU is the Best Inference Solution! https://lnkd.in/dd3hiSN8 (The numbers are taken from https://lnkd.in/duqgAuEj)

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Good listen: Mark Suster, the guest host, is joined by Dan Senor to discuss the remarkable growth of NVIDIA in #Israel, which now boasts over 4,000 employees and a global CTO who is Israeli. The conversation shifts to Israel’s startup ecosystem, highlighting its impressive statistics: 500 venture capital firms, 120 accelerators, 35 incubators, 400 multinationals with major R&D operations, and 7,000 startups. These figures underscore Israel’s position as having the highest density of startups globally, attracting significant global venture capital, leading in R&D spending relative to GDP, and ranking fourth in NASDAQ-listed public companies. Regardless - I'll recommend following This Week in Startups podcasts which brings interesting conversations with leaders from the startup community... https://lnkd.in/eGv3fr-H #iwork4dell

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The world's most advanced processors, including Intel(Intel 7), Apple(M1, M2, M3, M4 etc chips) and Nvidia(Blackwell) were researched and developed in Israel. The list goes on. Apple's 2nd largest R&D, Nvidia's 2nd largest R&D, Intel's 2nd largest R&D all in Israel. If there was no Israel today and the people inside, the advancement of software and hardware as it is today(GPUs, AI, CPUs, humanoid robots etc) would cease to exist. Not sure what it's going to take for the world to recognize Israel as a force for good, an ally, and get behind backing Israel and Israelis. Let these people get back to work, their government defend themselves and there will be a brighter future for all of us. Enough is enough.

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The oh so existential question for compute, which none of us can answer, is whether cryogenic hardware -- the most significant being touted in the press is QC -- survive the grueling scale out and economic battle ahead. ... I am betting yes, and I am betting hardware revenue will lead software revenue as it did historically at first for Boolean/Classical computing in the 60s and 70s.

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Keeping your data close to the GPUs is essential, so we offer VAST Data as part of our #GPUasaService AI Platform. Let us help you #StepontheGaaS and move your #AI strategy forward. #Fast #Secure #Private Dominic Ken Michael Bill Traci

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🇵🇱 8 Multinational Companies in Krakow to Know 🇵🇱 Dropbox adopted a #VirtualFirst work style in 2020, and started expanding hiring into Poland in 2022. Read more on how Dropbox is making a splash in #Krakow!

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🌟 2024: The Year of Generative AI Deployment! 🌟   If 2022 was the year the world discovered the potential of generative AI (GenAI) and 2023 marked breakthroughs in large-scale training, then 2024 is set to revolutionize how organizations deploy these powerful systems. Companies are now actively testing GenAI in production, unlocking unprecedented benefits. However, this shift isn't without its challenges—managing complexity and ensuring rapid response times are crucial for a seamless, human-like experience.   🚀 Introducing NVIDIA NIM™   NVIDIA has launched NIM™ inference microservices, designed to streamline GenAI deployment and significantly reduce time to market. Learn more about NIMs here https://lnkd.in/dyFkBfbi.   But what if the specific model you need isn't available as a NIM yet? You can still be extremely performant, and we’ll show you how.   📚 Unlocking Potential with Optimized Inferencing   In our latest blog , we explore how to leverage NVIDIA's cutting-edge inferencing technologies—Triton Inference Server and TensorRT-LLM—to enhance large language model (LLM) inferencing in production. We showcase the DictaLM-2.0 model with a custom tokenizer (which has recently led the Hugging Face Open Leaderboard for Hebrew LLMs) and demonstrate how to create optimized engines for both FP16 and quantized INT4 versions, all served through Triton Server. This setup allows for efficient interaction with the LLM, delivering streaming outputs with minimal latency.   🔗 Dive Deeper!   Check out our blog and explore the accompanying Jupyter notebook that details each step of the process. We’d love for you to try it out and share your feedback! Blog - https://lnkd.in/d68QYepb Unofficial Jupyter notebook - https://lnkd.in/digcgqQh   Big shoutout to the incredible team behind this initiative and the guys over at Dicta for the amazing collaboration: Shaltiel Shmidman Amit Bleiweiss Zvi Lapp   #GenerativeAI #MachineLearning #NVIDIA #TritonInferenceServer #TensorRT #LLM

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There are multiple factors for any programming language to be successful. Let me share a few points based on my experience. 1: Development environment. What kind of open source tools are available for a particular programming language ? 2: Learning curve . Who will be learning the particular programming language ? I noticed an average non Computer science background struggle for even learning C. You may see almost daily several posts related to C basics learned by engineers in social platforms. Many of them didn't reach the Linked list even after two years. When will they get some working knowledge of C++ ? If these well supported programming languages take such time , think about Rust . On the other hand, any school level student can also learn Python easily. 3: Market demand : Why should one learn any programming language? I never focused on Java because I didn't need it in my domain. How many companies adopted Rust in their products? If not then what will be the job opportunity ? 4: I will be happy if Rust is adopted by multiple companies because then I have to only learn and teach Rust for porting huge C/C++ legacy code base. If you are confused in selecting a particular programming language, you may simply ask me through Google search: EmbedKari which Programming Language I should learn ? You will find me a few hops away . Answer to What is the future outlook of Rust programming language? Is it a better alternative to the monstrous C++? What are its prospects of eventually replacing C++? by Sanjay Kumar https://lnkd.in/gJ-pkNQY #embedded #embedkari #opentowork #learnc #c++

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Architecture Of Intel Corporation Nightmare = In processor design, microcode serves as an intermediary layer situated between the central processing unit (CPU) hardware and the programmer-visible instruction set architecture of a computer, also known as its machine code. It consists of a set of hardware-level instructions that implement the higher-level machine code instructions or control internal finite-state machine sequencing in many digital processing components. While microcode is utilized in Intel Corporation and AMD general-purpose CPUs in contemporary desktops and laptops, it functions only as a fallback path for scenarios that the faster hardwired control unit is unable to manage. Housed in special high-speed memory, microcode translates machine instructions, state machine data, or other input into sequences of detailed circuit-level operations. It separates the machine instructions from the underlying electronics, thereby enabling greater flexibility in designing and altering instructions. Moreover, it facilitates the construction of complex multi-step instructions, while simultaneously reducing the complexity of computer circuits. The act of writing microcode is often referred to as microprogramming, and the microcode in a specific processor implementation is sometimes termed a microprogram. Through extensive microprogramming, microarchitectures of smaller scale and simplicity can emulate more robust architectures with wider word lengths, additional execution units, and so forth. This approach provides a relatively straightforward method of ensuring software compatibility between different products within a processor family. Some hardware vendors, notably IBM and Lenovo, use the term microcode interchangeably with firmware. In this context, all code within a device is termed microcode, whether it is microcode or machine code. For instance, updates to a hard disk drive's microcode often encompass updates to both its microcode and firmware. = We all know of the famous articles: Why Software Is Eating the World (2011) by Marc Andreessen lnkd.in/gE3qZr2k How software is eating the world (2024) lnkd.in/g6hhKre4 Now we can understand: Architecture Of Intel Corporation Nightmare In short the Architecture is also known as: Cutting Corners (with Software) From the referred article below: ... the problem is likely due to a microcode issue, which correlates with what we heard earlier about potential causes. If this is true, Intel can fix the problem by publishing a software update and won't need to recall chips. Still, it's an awful spot for the company to be in, and something it needs to fix if it wants to pick itself up from its recent downfalls. = What do we conclude from this long post?? It is this: Software is not eating the world It is: Sloppy Software is eating the world. Duh - should we call this post a Non Sequitur?

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The results from Google show that with #Intel #ConfidentialComputing you can have security and confidentiality without sacrificing AI performance. Intel CPUs have built-in AI acceleration with #AMX to run optimized inference and fine-tuning.

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Gurobi Optimizer, which identifies optimal solutions to complex nonlinear problems for over 1,200 global customers across industries, achieved 23% faster runtime while using 46% less energy on the NVIDIA Grace CPU. Read the technical blog post. https://bit.ly/4cJOge3

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This is simply incredible. Sambanova just launched a cloud inference platform allowing developers access to this blazing fast AI platform that has the best inference speeds on Llama models in the market. What is unique here is that all models are run with 16 bit precision as well as being extremely cost efficient. Start developing #FastAI ➡ http://cloud.sambanova.ai

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At Intel Corporation, we are always interested in understanding #developers preferences, shifts in work, and what is most helpful. Want to have a voice?   A new Developer Nation Survey, run by SlashData, is looking for your unique tech insights, knowledge, and opinions spanning AI, web, cloud, and more. Answer questions about your favorite development tools and platforms, win some swag, and support your favorite charity. Deadline is July 10th. #AI #opensource #edgecomputing #techforgood #cloudnative https://lnkd.in/g3j855kg

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✅ DIY Code Completion with #Mamba 🐍 ➡️ https://nvda.ws/3SLBCDq ✨ ⚡ #TensorRT #LLM for speed 🤖 NIM for ease and ☁️ Deploy anywhere

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12 techniques to reduce your LLM API bill and launch blazingly fast products Cost-efficiency and minimal latency are critical for deploying production-ready LLM applications.   Sahar Mor's article sheds light on 12 innovative techniques to slash your LLM API bills, with gems like Semantic Caching and Summarizing Conversations leading the charge. This isn't just about saving tokens; it's a strategic playbook for scaling smartly in an AI-dominated landscape.  #MachineLearning #AI #NLP #CostOptimization #Innovation #TechTrends https://lnkd.in/ec3qst-M

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📣Today NVIDIA announced it has completed the acquisition of Run:ai, an Israel-based company focused on GPU orchestration software. The company’s products will continue to be offered under NVIDIA branding, as a standalone product, available On Prem, on leading cloud service providers, or through DGX Cloud on Tier 1 cloud service provider partners. You can read more on Run:ai’s blog. ➡️ bit.ly/4j00pPU

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