smallest.ai on LinkedIn: Nvidia GPU Showdown - A100 vs T4 With two of Nvidia's most popular GPUs… (2024)

Supermicro X13 SuperBlade review.Popular Use Cases:- Data Analytics- Artificial Intelligence and Machine Learning- High-Performance Computing- Cloud Computing- Networking and Communicationsand not onlyAsk ASBIS for yours.

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With Nuvolos, you can now have the power of Nvidia's A100 GPUs at your fingertips. Read more about it in our feature update: https://lnkd.in/e62yAU9D#gpu#hpc#machinelearning

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#The #PowerEdge XE9680 is the first 8-way GPU platform to ship with either the @NVIDIA H100 GPUs or #NVIDIA A100 GPUs! #GTC23Read the @storagereview article to discover more: https://dell.to/3zkRN0D #Iwork4dell

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Read our best practices for performing in-game GPU profiling while monitoring the state of the background driver optimizations, using #DirectX 12 on NVIDIA GPUs.#GameDev

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The room was electric and I was beating my chest (think Wolf of Wall Street, Oh oh oh oh) as I watched NVIDIA's CEO Jensen Huang talk to his partners about the GPU is changing the face of computing as we know it. CPUs have not evolved to the same degree as GPUs and the power of how organizations can harness them are incredible. SQream is doing something first of kind with GPUs by leveraging them for Data Processing at scale. More data (100x), exponentially faster (days to hours, hours to minutes, minutes to seconds), and much more cost efficient (1/10th of the footprint) than CPU only resources. The future is here and I could not be more excited. #ohcaptainmycaptain

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🚨 NEW GPU ALERT 🚨 The new B200 GPU is here and it's packing some serious power! With up to 20 petaflops of FP4 horsepower from its 208 billion transistors, this GPU is a game-changer. But that's not all, the GB200 takes it to the next level by combining two of these GPUs with a single Grace CPU. This combo can offer 30 times the performance for LLM inference workloads while also being up to 25x more efficient than an H100. Are you excited to see the impact this new technology will have on the industry? Let us know in the comments! #GPU #Technology #Innovation

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Comparison from Databricks and AMD of the AMD MI250 GPU vs NVIDIA A100 and H100. Quite competitive to A100 on training workload with the additional bonus of having 128GB vRAM vs 40/80. Will be nice to have a basket of potential GPU vendors in the mix. Don't know the inference performance at this point. Will be interesting to see MI300X specs when they come out. https://lnkd.in/gAumPd-E

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The P5 instances are the fourth generation of #GPU-based compute nodes that AWS has fielded for #HPC simulation and modeling and now AI training workloads – there is P2 through P5, but you can’t have P1 – and across these, there have been six generations of GPU nodes based on various Intel and #AMD processors and Nvidia accelerators. It is interesting to be reminded that #AWS skipped the “Pascal” P100 generation in these P-class instances; that had somehow escaped us. AWS tested the HPC waters with Nvidia “Kepler” #K80 accelerators back in 2016 and jumped straight to the “Volta” #V100s a year later, and has put out two variations of these Volta instances – the first based on Intel’s “Broadwell” #Xeon E5 CPUs, and the other used a fatter “Skylake” Xeon SP processor – and two variations based on the “Ampere” #A100 GPUs – one using #A100s with 40 GB of memory and the other using A100s with 80 GB of memory.https://lnkd.in/eusVVQA9

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Do you know?Kubernetes can support GPUs! GKE supports TPU as well.CPU is Central Processing UnitGPU is Graphical Processing UnitTPU Tensor Processing Unit (found by google for deep learning)GPUs were extensively useful for rendering videos/games/graphics. But over the time, their parallel computing capabilities have paved way for AI. A typical CPU is good at doing sequential things and has limited cores ( intel Xeon 9282 processor has 56 cores/112 threads)AI needs lot of computations to be done and in parallel.GPU usually has thousands of cores 2500 to 5000. Latest NVIDIA’s GeForce RTX 4090, has 16,384 cores. Which makes GPU ideal choice for AI work loads. #kubernetes #gke #cpu #gpu #tpu #nvidia #intel #amd #ai #ml #k8s

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Check it out, #NVIDIA Video SDK added support for AV1 in Ada GPUs. AV1 also beats h264 and #HEVC in their encoding benchmarks. AV1 is a modern open-source and 100% royalty free video codec. It brings superior compression rates preserving good quality balance compared to other codecs, though most of the current CPU bound encoders are noticeably slow compared to x264/x265. However, if you run on Nvidia GPUs, the encoding speed is not an issue anymore.

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