Puget Systems’ Post

Choosing the right GPU is crucial for maximizing performance in Topaz Video AI. Our recent analysis provides valuable insights: NVIDIA's RTX 6000 Ada delivers the best performance, while the AMD Radeon PRO W7900 offers a more budget-friendly alternative with slightly lower performance. Discover which GPU matches your workflow best in our detailed analysis. Save time, improve quality, and work seamlessly with Topaz Video AI! https://hubs.ly/Q02WRxBv0

Topaz Video AI 5.1 Consumer GPU Performance Analysis – Maximize Your Video Processing Efficiency! Wondering which GPU gives you the edge for video upscaling and AI-based enhancements? Here’s what we found out: 🔹 NVIDIA GeForce RTX 4090 delivers the highest performance for video upscaling and denoising, boosting your productivity as a video editor. 🔹 NVIDIA GeForce RTX 4070 offers a cost-effective solution, hitting the sweet spot between performance and affordability. 🔹 AMD GPUs like the RX 7900 XTX are closing the gap but still fall behind NVIDIA's dominance in video AI tasks. What this means for you: 🔹 Faster renders mean less downtime for content creators and filmmakers. 🔹 Better video quality for VFX artists and 3D designers with demanding workflows. Read the full breakdown and see how your GPU compares! https://hubs.ly/Q02V7qdd0

With the recent overhaul of our Blackmagic Design DaVinci Resolve benchmark, we thought it was a good time to do an extensive analysis of various consumer GPUs, including NVIDIA GeForce, AMD Radeon, and Intel Corporation Arc. There was a lot to discover across the expanded tests for RAW codecs and GPU Effects, as well as the newly added AI tests! https://lnkd.in/gfwD6R-d #davinciresolve #pugetbench #AMD #Intel #NVIDIA #benchmark #GPU #AI

Tried to install NVIDIA Isaac Sim to run some local reinforcement learning experiments, but it turns out my (expensive at the time) TITAN V GPU is not compatible. Isaac Sim requires an RTX-branded GPU (which offer hardware-accelerated ray tracing). Fun fact: Even thee latest NVIDIA H100 data center GPUs don't support ray tracing - their flagship GPUs are optimized for AI training + inference. For simulation workloads, you need something like an an L4, or one of the desktop-class GeForce (gaming) or A6000 (pro workstation) GPUs. NVIDIA refer to this as the "3 GPU architecture" (simulation, training/inference, and on-robot). So I've purchased a GeForce RTX 4090, and damn this thing is huge. "Titan" V for scale. Barely fits in my desktop case, and turns out I need to order some more power cables off Amazon before I can even plug it in. More of my RL journey to come.

⚡ Announcing new GPUs! ⚡ We’re excited to unveil new GPU plans, powered by NVIDIA RTX 4000 Ada Generation GPU cards and optimized for a range of use cases, from media transcoding to AI inference, starting at $0.52 per hour. Available now in: 🇯🇵 Osaka 🇸🇬 Singapore 🇫🇷 Paris 🇩🇪 Frankfurt 🇺🇸 Chicago 🇺🇸 Seattle Read more in our blog post: https://lin0.de/JdNk5v

"Old Nvidia or AMD GPUs for Private Ai? Forget about it" I built an experimental GPU rig for Llama-3. I had old Nvidia GTX 1060 x 6 GPUs just laying around collecting dust. Let me tell you this, turtles move faster than this system. Sure, you have enough VRAM to load the Large Language model, but your text inferencing will be terribly slow. Seriously, I have beefy CPUs that do text inferencing faster than this. I got skin in this game so I'm trying to get the biggest bang for my buck. If you're building your own GPU rig, go with Nvidia RTX 3060s and up. Get 6x of those for $250 a pop and connect them up. As of the year 2024 don't even think about using AMD GPUs unless you have an infinite amount of time on your hands for getting parallel processing to work without constant crashing. image: Ai generated on a 1x Geforce RTX 3060.

Topaz Video AI GPU Performance: What’s Best for Your Workflow? Looking for the ultimate GPU for your Topaz Video AI workflow? Here are the top performers: 🔹 NVIDIA RTX 6000 Ada: Unmatched performance with 50% higher speeds than AMD’s Radeon W7900. 🔹 NVIDIA RTX 5000 Ada: Best value—1.5x faster than W7900 at a similar price. 🔹 AMD Radeon PRO W7900: Strong but falls behind NVIDIA in Topaz Video AI performance. Accelerate your projects with the right hardware for Topaz Video AI. Don’t settle for less—upgrade now! https://hubs.ly/Q02VJ6n_0

💡 Interesting Facts about H200 GPUs ❓ A single NVIDIA H200 Tensor Core GPU generated about 3,000 tokens/second (enough to serve about 300 simultaneous users) in an initial test using the version of Llama 3 with 70 billion parameters. clust.ai https://lnkd.in/d576ujA2

Scatter recently updated our Depthkit Studio hardware package, unlocking the ability to record 1440p color resolution from all 10 sensors on a single PC. So what changed? 🤔 Hardware accelerated video encoding performance has stagnated for a while now, and has even declined since the Pascal architecture days. The reason for this is that the Turing and Ampere generation GPUs only had one video encoder chip, while the bigger Pascal GPUs had multiple. 💪 Luckily, NVIDIA has returned to putting multiple encoder chips on their GPUs with the Ada Generation: two on the RTX 4000 Ada, and three on the RTX 6000 Ada. 🔬 Using our internal benchmarking tool built on the Depthkit pipeline, we can see how concurrent video encoding performance scales across three generations of NVIDIA GPUs. As you can see from the graph below, we can now achieve real-time encoding of up to ten 1440p streams (and even 10x4K streams with the RTX 6000, just barely! 😮) using a contemporary GPU. This all adds up to improved clarity and texture resolution for Depthkit Studio captures. For more information about the hardware package update, check out Scatter's blog post here: https://lnkd.in/gc3UeJGs If you're curious about how many encoders your NVIDIA GPU has, take a look at this handy matrix: https://lnkd.in/gMG-kjKb

"Depending on who you ask, the AI boom means that accessing powerful GPUs in the cloud is a breeze – or, it’s nearly impossible." "Neural Magic utilized their software optimizations to achieve an average cost of $0.27 per 1,000 summarization requests using RTX 4000 GPUs, a 60% reduction in cost compared to the reference deployment."