… For healthcare organizations this means faster time to production, expert support proven on their real workloads, and a system co-designed for their scale, not generic benchmarks run in isolation. …
… May 06, 2026 AMD and OpenAI Advance AI Networking at Scale with MRC AMD, OpenAI and partners advance AI networking with MRC—boosting scalability, resilience and real-world performance for large AI clusters. …
… MoRI supports multi-level quantized communication: MoRI-EP combine kernel micro-benchmark on the MI355X GPUs EP8, BF16 input, max tokens=4096, hidden dim=7168, scale dim=56, zero-copy=0, dispatch=128/16, combine=128/16, 10-round average, combine latency only Case Path Combine Latency Normal no-scal… …
… View All A hub for easy access to 1M+ pre-trained state-of-the-art models A ML framework developed for efficient serving of LLM and vision-language models A lightweight Python wrapper for 8-bit and 4-bit quantization functions A PyTorch native library for large-scale model training AMD Infinity Hub… …
… The platform combines production-ready GPU infrastructure with a full-stack cloud to deliver operational simplicity and predictable economics at scale. …
… Hardware: With 288 GB on-chip memory and 8 TB/s bandwidth, AMD Instinct MI355X comfortably drives inference at MiMo-V2.5-Pro's full 1T-parameter scale and 1M-token context window. …
… The comprehensive evaluations of Qwen3.6-35B-A3B against peer-scale models across a wide range of tasks and modalities. …
… Rack-scale architectures with higher scale-up domains provide real value at low interactivity, where larger batch sizes and higher GPU parallelism help most. rack-scale answer from AMD is “Helios” with MI450 GPUs, planned for 2H 2026, targeting exactly that regime. …
… May 28, 2026 Win on TCO: How AMD Instinct™ MI355X GPUs Achieve Cost-Competitive Distributed Inference Through SGLang with MoRI AMD Instinct™ MI355X GPUs with SGLang + MoRI delivers lower-cost, production-scale DeepSeek-R1 inference. …
… Scale out Finally, we scale out gpt-oss-120b aggregated configurations to a multi-node llm-d deployment to compare multi-node aggregated deployments to a 2 node disaggregated setup. …