- Intel Shared GPU -Memory Benefits LLMS
- Extended Vram -Do pools allow smoother execution of AI workloads
- Some games brakes when the memory is expanded
Intel has added a new capacity to its central ultra -systems that repeat a previous movement from AMD.
The feature, known as “Shared GPU memory breach”, allows users to allocate additional system RAM for use in integrated graphics.
This development is targeted at machines that depend on integrated solutions rather than discreet GPUs, a category that includes many compact laptops and mobile workstation models.
Memory allocation and game performance
Bob Duffy, who heads Graphics and AI evangelization at Intel, confirmed the update and advised that the latest Intel -arch drivers are required to activate the feature.
The change is presented as a way to improve system flexibility, especially for users who are interested in AI tools and workloads that depend on the memory availability.
The introduction of extra shared memory is not automatically an advantage for any application, as testing has shown that some games can load larger structures if there is more memory, which can actually cause the performance to dip rather than improve.
AMD’s previous “variable graphics memory” was largely framed as a game improvement, especially when combined with AFMF.
This combination allowed more game assets to be stored directly in memory, which sometimes produced measurable gains.
Although the impact was not universal, the results varied depending on the software in question.
Intel’s adoption of a comparable system suggests that it is eager to remain competitive, although skepticism remains over how wide it will benefit everyday users.
While players may see mixed results, those working with local models could stand to get more from Intel’s approach.
Running large language models locally becomes increasingly common and these workloads are often limited by available memory.
By expanding the pool of RAM available for integrated graphics, Intel places its systems to handle larger models that would otherwise be limited.
This can allow users to relieve more of the model of VRAM, reduce bottlenecks and improve stability when running AI tools.
For researchers and developers without access to a discreet GPU, this can offer a modest but useful improvement.
