Training the model is only the beginning. Enterprise AI must continuously serve inference, orchestrate agents and connect with enterprise data and applications. See how @Oracle and AMD are building an integrated foundation for this new era:

Training a 100B parameter model normally means renting a block of matched, high-spec hardware and holding it unbroken for the length of the run. Orion-100B ran across five data centres instead, on single A100s at around $1.25 an hour each. That puts a full replica at roughly $20 an hour, and the entry cost about 2.5x below approaches that need high-spec nodes throughout. It ran as a viability proof rather than to a finished model, and it held. We're in Montreal on Monday talking about what that changes. @ExploitSummit

Training and rollout logprobs matched bit for bit on ROCm. The @RLKernel team integrated RL-Align/RL-Kernel with vllm-project/vime. A 200-step Qwen3-8B GRPO run on 8× @AMD MI300X recorded zero logprob mismatches between Megatron training and vLLM rollout. The strict path aligns reduction order, intermediate precision, rounding points, and math primitives across both sides. Deep dive: