The Gemma 4 family of open-weights models from Google includes four variants, spanning a range of sizes from 2B effective parameters to 31B parameters and including both Mixture of Experts (MoE) and dense architectures. These multimodal models ingest text, vision, and for select variants, audio inputs and generate text outputs. They support context sizes of up to 256K tokens, and have been trained for thinking, coding, function calling, optical character recognition (OCR), object recognition and automatic speech recognition tasks. For relatively compact models they have outstanding language s
Day 0 Support for Gemma 4 on AMD Processors and GPUs… SGLang Model : qwen3-5-122b This must match the --served-model-name used when starting the SGLang server. Figure 5: OpenClaw CLI onboarding showing SGLang config Once entered, OpenClaw automatically configures the provider and sets the model as the default agent model. …
… Built on the new 4th Gen AMD CDNA™ architecture, these GPUs deliver exceptional efficiency and performance for training massive AI models, high-speed inference, and complex HPC workloads like scientific simulations, data processing, and computational modeling. …
… For relatively compact models they have outstanding language skills, understanding up to 140 different languages. Architecture changes from the Gemma 3 generation provide improved efficiency and improved long context quality. These are coupled with new architectures for vision and audio processing. …
… With MRC, providers of large language models can do large-scale AI model training with predictability, performance, and resilience across large scale AI training clusters. …
… AMD PACE serves LLM inference for both public and sovereign AI model families. Supported public model families include, Llama4 Causal, GPT-OSS, Gemma3, Phi3/4, Qwen 2, OPT and GPT-J; sovereign AI support covers Sarvam AI models from 2B to 7B parameters. …
… With AMD Silo AI, we are combining state-of-the-art research with scalable, open engineering to push that frontier forward while nurturing a new generation of European AI talent at UniMoRe and across our partner network.” The research work will emphasize three core thrusts: Multimodal world models … …
… And Hasani described a future where multiple specialized models work together on a single system, each optimized for a specific function: “It’s not one model doing everything. …
… MLPerf Endpoints bring in multiple new features including API centric architecture and rolling submissions enabling benchmarking at the speed of software updates. …
… Report bugs, request features, or submit pull requests directly through the GitHub repository. …
… May 06, 2026 Ansys Discovery on AMD Graphics: Jointly Scaling the Memory Wall AMD and Ansys have cooperated to deliver Ansys Discovery 2026 R1 to AMD Ryzen™ AI Max PRO and Max+ PRO Series processors, thanks to AMD ROCm™ and AMD HIP May 05, 2026 Accelerating Mixture-of-Experts Execution with FarSkip… …