Dispatch Codegen Built Shared Library runtime::PackedFunc DNNLModule::GetFunction( const std::string& name, const std::shared_ptr& sptr_to_self) { if (name == "init") { return PackedFunc([sptr_to_self this->Init(args[0]); . }); } else { std::string curr_id = GetSubgraphID(name); return PackedFunc([sptr_to_self, curr_id, this](TVMArgs TVMRetValue* rv) { auto out = reinterpret_cast(args[args.size() - 1]>data); std::string encoded_name = kDnnlPrefix + curr_id; . auto func_s = reinter

Cycles Over Time – 1960s-2020s, per Morgan Stanley Note: Axis is logarithmic; i.e., there are expected to be tens of thousands more AI Era devices than Mainframe devices 1960 1970 1980 1990 2000 Jensen Huang, These AI data centers…are, in fact, AI factories. That race is moving faster than many expected. The most striking example may be xAI’s Colossus facility in Memphis, Tennessee which went from performance leadership is no longer a given. Open-source models are closing the gap – faster than many expected – and doing so at a fraction of the cost to users. Models like Llama 3 and DeepSeek have demonstrated

structuring the input you provide. This is where JSON Schemas come into play. A JSON Schema defines the expected structure and data types of your JSON input. By providing a schema, you give the LLM a clear blueprint