DeepSeek-V2, we partition all routed experts into ? groups {E1, E2, ..., E?}, and deploy each group on a single device. The device-level balance loss is computed as follows: LDevBal = ?2 ? ∑︁ ?=1 ? ′ ? ?′ of FlashAttention-2 (Dao, 2023). We conduct all experiments on a cluster equipped with NVIDIA H800 GPUs. Each node in the H800 cluster contains 8 GPUs connected using NVLink and NVSwitch within nodes. attain a relatively high Model FLOPs Utilization (MFU). During our practical training on the H800 cluster, for training on each trillion tokens, DeepSeek 67B requires 300.6K GPU hours, while DeepSeek-V2

point operation) is a basic unit of computation used to measure processing power, representing a single arithmetic calculation involving decimal numbers. In AI, total FLOPs are often used to estimate Momentum Performance, 16-bit FLOP/s +150% / Year Enabled by 1.6x annual growth in chips per cluster and 1.6x annual growth in performance per chip Performance of Leading AI Supercomputers (FLOP/s) size of 418 average USA homes – it was built in half the time it typically takes to construct a single American house. Per NVIDIA Co-Founder & CEO Jensen Huang, What they achieved is singular, never

Applications that integrate LLMs but don’t use them to control workflow execution—think simple chatbots, single-turn LLMs, or sentiment classifiers—are not agents. More concretely, an agent possesses core characteristics incremental approach. In general, orchestration patterns fall into two categories: 01 Single-agent systems, where a single model equipped with appropriate tools and instructions executes workflows in a loop agents Let’s explore each pattern in detail. 13 A practical guide to building agents Single-agent systems A single agent can handle many tasks by incrementally adding tools, keeping complexity manageable

slower response times, which leads to higher costs. Sampling controls LLMs do not formally predict a single token. Rather, LLMs predict probabilities for what the next token could be, with each token in the configuration settings that determine how predicted token probabilities are processed to choose a single output token. Temperature Temperature controls the degree of randomness in token selection. Lower machine learning. A low temperature setting mirrors a low softmax temperature (T), emphasizing a single, preferred temperature with high certainty. A higher Gemini temperature setting is like a high softmax

runtime ● Virtual machine as a new runtime for Relay ● Dynamic codegen (WIP) ○ Kernel dispatch for a single op ○ Graph dispatch for a (sub-)graph In collaboration with Jared Roesch, Zhi Chen, Wei Chen© 2019 Dynamic codegen: op dispatch (proposal) ● Goal: support codegen for dynamic shape ● Challenges ○ Single kernel performs poor across different shapes ○ Different templates for the same op ○ TVM compute

user space (~10 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)

Binary New Hardware Design in Verilog VerilatorToward Unified IR InfraOverview of New IR Infra Single unified module/pass, type system, with function variants supportCompilation Flow under the New Infra

delivers groundbreaking AI performance. 。 Performs /mxeo-Drecsion matrix multiply and accumulate in a single operation. Background 全于由 。TensorCore 。 Poograrm171aple matrix-multiply-and-accumulate