Situational Awareness increased its exposure to semiconductor firms Micron and Sandisk in the second quarter, while the hedge fund cut options positions against Nvidia, Broadcom and Advanced Micro Devices https://t.co/NLihqFNXtg ...

RT None (@None)Today's @OrderBookShow with timestamps.Michael Burry Is Getting Squeezed — And He Just Doubled Down00:00 Intro7:20 Stream Starts9:35 BTC Chart25:30 ETH Chart30:00 Solana Chart30:50 HYPE Chart33:45 S&P500 Chart41:10 IBM, Bloom Energy, NVIDIA, MU, SNDK,46:00 Life's too short to be unhappy49:00 Tron Chart51:30 NEAR Chart54:00 FET Chart56:30 LIT Chart58:05 Chainlink Chart59:45 CD Project Red Chart1:01:20 XRP Chart1:04:00 Quitting Job to go full time crypto trading1:10:30 ONDO Chart1:12:00 Pumpfun ...

Financial Performance and Market Trends - OpenAI is on track to generate annualized revenue exceeding $40 billion, doubling its run rate from the end of 2025[1] - OpenAI's consumer business surpassed 1 billion active weekly users, driven partly by its coding assistant Codex[1] - AMD raised $4.75 million in its largest-ever dollar bond sale with 875 new dollars in bonds due next month[2] - Applied Materials delivered a better-than-expected forecast, though shares fell 4.6% as investors focused on future expectations[2][3] - DeepSeek is sharply raising prices for its flagship models, with peak hour rates more than quadrupling starting August 16 to focus on profitability[3] - Nintendo's global sales for its hit Nintendo Switch game topped 5 million units since its launch in March[4] Industry Challenges and Strategic Risks - Compute costs remain a massive expense, with OpenAI citing getting enough compute as its biggest obstacle[1] - Citigroup and Bank of America strategists noted the chips index is in "bubble land" after rising two standard deviations, highlighting a duration mismatch between AI infrastructure realities and investor expectations[1][2] - Broadcom shares dropped 5.3% following reports of a VMware security vulnerability actively exploited across several countries[2] - A new bill backed by Zohran Mamdani threatens Amazon's delivery model in New York City, where 2.5 million packages are delivered daily, requiring direct employment of delivery workers[1][5][6] - Alex Cooper's beverage business, launched in partnership with Nestlé, is shutting down production in the fall amid high competition in the beverage market, even as its parent company Unwell raised money at a $5 billion valuation[24][26]

Market Dynamics and Industry Trends - AMD positions itself as an open foundation for the age of AI-powered robots [1] - AMD maintains active engagement across major digital and social media platforms including Discord, Facebook, Twitter, Twitch, LinkedIn, and Instagram [1] Intellectual Property and Corporate Identity - Advanced Micro Devices, Inc. holds trademark rights for AMD, the AMD Arrow Logo, and related combinations in the United States and other jurisdictions as of 2026 [1]

Technology Capabilities - Primus enables reliable, debuggable, and high-performance large-scale training on AMD Instinct GPUs [1] - The platform supports the latest open-source training frameworks, models, and is expanding compatibility to cutting-edge model architectures, training techniques, and data types [1] Market Competitiveness - Primus delivers state-of-the-art pre-training and post-training performance proven at scales of thousands of GPUs [1] - Advanced Micro Devices positions the AMD Instinct GPU as a competitive solution for model development at frontier laboratories, enterprises, and artificial intelligence startups [1]

Infrastructure Strategy - AMD introduced Helios as a rack-scale AI infrastructure designed to accelerate training and inference for next-generation AI workloads [1] - The architecture encompasses compute, networking, memory, and system design considerations [1] Market & Economic Impact - Rack-scale optimization enhances performance, scalability, efficiency, and total cost of ownership (TCO) for enterprise and cloud AI deployments [1]

Application Trends and Challenges - AI training models have evolved from simple single-GPU setups to complex architectures requiring tensor parallelism, pipeline parallelism, and mixture of experts (MoE) [1][3][4] - Inference applications demand low latency where every microsecond impacts user experience and costs, requiring the elimination of staging buffers and direct GPU-to-GPU High Bandwidth Memory (HBM) writes [5][6][16] - Large-scale training running across tens of thousands of GPUs faces critical challenges including network fabric congestion, job bootstrap time increasing to tens of seconds or minutes, and memory resource consumption on High Bandwidth Memory (HBM) and qubits [7][8][9] Rekall Library Innovations - AMD collective communication library Rekall utilizes system Direct Memory Access (SDMA) copy engines to move data across GPUs, freeing up compute units for matrix-matrix multiplication and achieving significant speedups [10][11][15] - Single-node optimizations for small messages implement one-shot and two-shot algorithms, achieving significant speedups across different collectives with a maximum speedup of 3.7% on an 8-GPU MI350 node [17][19][20] - Multi-node communication implements a topology-aware hierarchical algorithm that achieves a speedup of up to 3.6% (or 3.6 GB) across 16 nodes with 8 GPUs (MI350) per node [21][23][24] - GPU-initiated communication enables compute and communication fusion, using a single Compute Unit (CU) via SDMA to achieve higher or similar performance compared to default algorithms using up to 64 CUs [25][26][28][29] - Congestion-aware spray traffic (CAS) technology measures round-trip times across queue pairs to dynamically distribute load, achieving bandwidth close to optimal and a speedup of up to 1.7% in multi-node all-to-all operations [35][36][37][39] Roxamen Runtime Performance - Roxamen acts as a GPU-initiated runtime implementing OpenCMN APIs, utilizing SDMA with a single Compute Unit (CU) to deliver up to 35 times higher bandwidth for larger messages on 8 MI355 GPUs compared to default algorithms using 64 CUs [41][42][43] - Supports multiple Network Interface Cards (NICs)—including AMD Pensando Polara, Thor, and Connect7—achieving high bus bandwidth close to 50 gigabytes per second and highly competitive latencies for 8-byte messages [44][45][46]

For the next about 20 minutes, I'll talk about our inferencing framework called ATOM. And what's behind that is a collection of the kernels, which is called AITER. I think earlier today, when you hear about the keynote speech, you might realize that AMD has delivered a lot of the very good hardware.But to light up those GPUs, we actually need a very mature and sophisticated software stacks to make sure that we could leverage whatever that they offer by our hardware. So that's exactly what I'm going to prese ...

Modern AI inference is increasingly constrained by KV cache movement across prefill, decode, and storage tiers rather than GPU compute. This session demonstrates how a lightweight software layer enables RDMA-class performance on standard Ethernet networks without application changes, supporting high-performance disaggregated vLLM inference. Live benchmarks highlight improvements in time-to-first-token (TTFT) and inter-token latency (ITL). Discover more: https://www.amd.com/en/corporate/events/advancing-ai/s ...

Thank you so much. Thank you for showing up and coming with me to join this session. And what we plan to do today, over the next 20 minutes, is to really cover.there's a lot to cover about inference, but I would like to sort of tell you a little bit about how we approach open source inference from vLLM, which is the inference engine that lets you run any large language model on data center hardware. And really there to. hopefully this will be a start and a spark of discussion and inspiration for you all.And ...