Learn more about GPU IP core
Edge AI gets more interesting when a model does more than answer prompts. It plans, calls tools, and keeps working toward a goal. That is the appeal of agentic workloads, and it is why we explored running OpenCode through llama.cpp with Qwen 3.5 on Imagination E-Series. This blog explains the approach we took, why the software path was practical, what we optimized along the way, and why E-Series GPUs are a good fit for this class of workload.
Imagination GPU Driver 26.1 introduces key Vulkan advancements, including support for Android 17, enhancing performance and developer capabilities for modern graphics workloads.
Explore how the SpacemiT K3 processor combines RISC-V CPUs and GPUs to revolutionize high-performance SoCs for AI-driven applications. Discover the future of computing.
A full SoC tape-out at 5nm approaches $400M in fully loaded, non-recurring engineering and mask costs. At 3nm, estimates push past $600M. Every IP block on that die is a commitment to a set of assumptions about what the silicon will need to do. In AI, those assumptions have a shorter shelf life than they used to.
A GPU IP block with robust DirectX support solves a critical problem for new hardware designers: it removes a barrier to entry for Windows markets. Without it, even the most capable silicon cannot run mainstream PC games or enterprise workloads. In short, it makes your solution relevant to the largest graphics software base in the world.
Discover why the future of edge GPU design focuses on power efficiency over area, driven by thermal constraints at sub-2nm nodes. Learn about architectural shifts and Imagination's innovative solutions.
