I think that the training of…$10 billion models, yeah, could start sometime in 2025. Around these core compute costs sit additional high-cost layers: research, data acquisition and hosting, and a mix Beneficiary of AI CapEx Spend …These kinds of timelines are no longer the exception. With prefabricated modules, streamlined permitting, and vertical integration across electrical, mechanical, and software systems inference acceleration. Google’s TPU (Tensor Processing Unit) and Amazon’s Trainium chips are now core components of their AI stacks. Amazon claims its Trainium2 chips offer 30-40% better price-performance

activated for each token, and supports a context length of 128K tokens. We optimize the attention modules and Feed-Forward Networks (FFNs) within the Trans- former framework (Vaswani et al., 2017) with our limits the maximum batch size and sequence length. 2.1.2. Low-Rank Key-Value Joint Compression The core of MLA is the low-rank joint compression for keys and values to reduce KV cache: c?? ? = ? ???h?

MobileNetv2 LaneNet 图TFLite1core 图TFLite4core 国QNNPACK 1core 四QNNPACK4core 四TVM1core 四TVM4core AiOS 1驱动万物智能 Alios TVM @ ARM CPU FP32 。，NHWC layout 。 For pointwise

Sparse Transformers, etc - Reduce precision with int8/float16 - very helpful to maintain model in core-private L1 dcaches - Use rational approximations for transcendentals (exp, tanh, erf, etc) - very lines of Relay IR) - A few days of work - TVM sampling model running in 30us on single server CPU core - Beat hand-written, highly optimized baselines (https://github.com/mozilla/LPCNet) by ~40% - Bonus:

multiple employees to contribute to TVML. ee Today we'ltouch on a few of those contribution areas: o Core Infrastructure Improvements to TVM o_uTVM: support for microcontrollers in TVM o_ Virtual Machine dynamic NNs support (w/ AWS folks) o_ Improved NLP support, with focus on transformers QQ octoML Core Infrastructure Refactors ee New Integer Analysis Infrastructure o_ Supports the ability to handle

chatbots, single-turn LLMs, or sentiment classifiers—are not agents. More concretely, an agent possesses core characteristics that allow it to act reliably and consistently on behalf of a user: 01 It leverages building agents Agent design foundations In its most fundamental form, an agent consists of three core components: 01 Model The LLM powering the agent’s reasoning and decision-making 02 Tools External

Amazon Web Services, Inc. or its Affiliates. All rights reserved. Evaluation • Intel Cascade Lake 12-core Server • TFLite Pre-quantized Hosted Models© 2019, Amazon Web Services, Inc. or its Affiliates. All