Adrian Macneil’s Post

💡 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

CUDOS acknowledges the incredible capabilities of NVIDIA GPUs and has integrated a wide range of NVIDIA GPUs to provides users with exceptional access to powerful computational resources. The partnership with NVIDIA ensures that CUDOS stays at the forefront of NVIDIA’s latest technological advancements, allowing it to offer high-performance computing resources across various domains. check out my recently published article which highlights all the powerful NVIDIA GPUs utilized by CUDOS and their various usescases. 🔗https://lnkd.in/dRGCQJYY

Implementing CUDA Graphs in llama.cpp Modern GPUs are incredibly fast, and NVIDIA has introduced CUDA Graphs to further optimize performance. #CUDA Graphs allow work to be defined as a graph of operations rather than as single operations, solving issues by enabling multiple GPU operations to be launched through a single CPU operation. Reducing Overheads: Speedup varies across model sizes and #GPU variants. 🔵 Benefits increase as: ✅ Model size decreases ✅ GPU capability increases Aligning with Expectations: 🔵 CUDA Graphs reduce overheads, particularly for small problems on fast GPUs. Highest Achieved Speedup: ✅ 1.2x speedup for the smallest Llama 7B model ✅ Achieved on the fastest NVIDIA H100 GPU When performing inference on Meta's #Llama models on #NVIDIA GPU, #CUDAGraphs enabled the scheduling of multiple GPU activities as a single computational graph in unison, leading to more efficient execution. #cudaprogramming #machinelearning

Boost Your Workflow with the Right GPU for Topaz Video AI 5.1! Key Insights: 🔹 Top Performer: NVIDIA RTX 4090 leads with unmatched speed and efficiency. 🔹 Best Value: AMD’s 7900 XTX offers great performance with more VRAM for less money. 🔹 Budget-Friendly: Intel Arc A770 provides decent performance at a fraction of the cost. Get the full breakdown of our benchmark results and discover which GPU fits your needs! https://hubs.ly/Q02QRTrP0

"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.

NVIDIA has launched a powerful new AI superchip, the Blackwell B200 GPU and GB200 "superchip". The B200 GPU boasts an impressive 20 petaflops of FP4 horsepower with 208 billion transistors, while the GB200 combines two B200 GPUs and a Grace CPU for 30x LLM inference workload performance, with 25x efficiency over H100. Training a 1.8 trillion parameter model now takes only 2,000 Blackwell GPUs and 4 megawatts, compared to 8,000 Hopper GPUs and 15 megawatts previously. The clear focus of these new chips is on performance, making them a game-changer in the world of AI. #NVIDIA #AI #BlackwellB200 #GB200 #ArtificialIntelligence #Superchip #Performance

𝐙𝐌𝐋: 𝐀 𝐇𝐢𝐠𝐡-𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐀𝐈 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐒𝐭𝐚𝐜𝐤 𝐭𝐡𝐚𝐭 𝐜𝐚𝐧 𝐏𝐚𝐫𝐚𝐥𝐥𝐞𝐥𝐢𝐳𝐞 𝐚𝐧𝐝 𝐑𝐮𝐧 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 𝐨𝐧 𝐕𝐚𝐫𝐢𝐨𝐮𝐬 𝐇𝐚𝐫𝐝𝐰𝐚𝐫𝐞 ZML is a new AI inference stack with the ability to efficiently run models across different hardware—from GPUs, TPUs to edge devices. Example below shows a prototype running a LLaMA2 model sharded on 1 NVIDIA RTX 4090, 1 AMD 6800XT, and 1 Google Cloud TPU v2. All accelerators hosted in different locations. Github: https://lnkd.in/gJF4X8he Documents: https://lnkd.in/g6waxp_q #AI #GenAI #LLM #MLLM #VLM #Inference #stack

AMD might have a pretty decent road map of MI300x, MI325x and MI355x, but trying to compete with Nvidia is really is David vs Goliath especially with their CUDA moat. https://lnkd.in/e6W8Bupq

Curious about the NVIDIA RTX A6000 and how it stacks up for your high-performance computing #HPC needs? Check out our in-depth breakdown to see if this powerful GPU is the right fit for your workload. From #deeplearning to real-time ray tracing, discover the specs, performance benchmarks, and cost-effectiveness of the A6000. Read the full breakdown and overview ⬇

🚀💡 **𝐁𝐥𝐚𝐜𝐤𝐰𝐞𝐥𝐥: 𝐀 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐋𝐞𝐚𝐩 𝐢𝐧 𝐀𝐈 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞!** 💥🔬 𝘍𝘳𝘰𝘮 𝘊𝘩𝘪𝘱𝘴 𝘵𝘰 𝘛𝘳𝘪𝘭𝘭𝘪𝘰𝘯-𝘗𝘢𝘳𝘢𝘮𝘦𝘵𝘦𝘳 𝘔𝘰𝘥𝘦𝘭𝘴! 🌟 1. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐏𝐨𝐰𝐞𝐫𝐡𝐨𝐮𝐬𝐞: B200 GPU offers 20 petaflops of FP4 horsepower with 208 billion transistors, while GB200 combines 2 B200 GPUs and a Grace CPU for 30x LLM inference workload performance. #Blackwell #AIInfrastructure #PerformanceBoost 2. 𝐄𝐧𝐞𝐫𝐠𝐲 & 𝐂𝐨𝐬𝐭 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: Training a 1.8 trillion parameter model now requires 2,000 Blackwell GPUs and 4 megawatts, compared to 8,000 Hopper GPUs and 15 megawatts previously. #EnergyEfficiency #CostSavings 3. 𝐆𝐏𝐓-3 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤: GB200 outperforms H100 by 7x and offers 4x the training speed, showcasing remarkable performance improvements. #PerformanceBenchmark #AIInference 4. 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐜𝐚𝐥 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐦𝐞𝐧𝐭𝐬: Blackwell chip achieves 2.5x performance in FP8 for training and 5x in FP4 for inference, setting new standards in GPU performance and memory architecture efficiency. #GPUAdvancements #MemoryEfficiency 5. 𝐒𝐮𝐩𝐞𝐫𝐜𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐌𝐢𝐥𝐞𝐬𝐭𝐨𝐧𝐞𝐬: DGX SuperPOD, powered by NVIDIA GB200 Grace Blackwell Superchips, delivers 11.5 exaflops of computing power at FP4 precision, ushering in a new era of AI supercomputing. #AI #Supercomputing #DGXSuperPOD #NVIDIA