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Latest Small Island Research Notes
A Second Path Beyond the GPU? Architectural Thinking Behind NVIDIA’s Licensing Agreement with Groq
2026-03-05
Executive Summary
NVIDIA’s licensing agreement with Groq is worth watching not only because the technology itself is extreme, but because it may signal that AI compute architecture is being reconsidered. Even after GPUs have become the dominant platform for AI training and inference, NVIDIA is still willing to engage seriously with an execution model that runs almost counter to the mainstream path. That suggests the demands of the inference era may be making determinism important again.
At the core of Groq’s architecture is the use of scratchpad SRAM and static compiler scheduling in place of traditional cache hierarchies and runtime decision making. This shifts complexity away from hardware and toward the compiler. While this approach has often been seen as too aggressive in the past, it may become viable again under highly regular workloads such as deep learning inference.
This article argues that what NVIDIA may value is not Groq’s current product capability alone, but also its years of accumulated experience in dataflow compiler development and its access to an execution model that differs fundamentally from the GPU. Combined with NVIDIA’s strengths in synchronization control, packaging, and thermal management, some of the constraints that once made Groq difficult to scale may be partially eased.
Whether this agreement will become a second AI compute path remains uncertain. Its success still depends on compiler maturity, synchronization stability, and whether deterministic scheduling is truly well suited to large model inference. From a strategic perspective, however, this agreement may be better understood as an execution option. If future inference workloads place greater weight on latency determinism and system efficiency, NVIDIA will not be caught unprepared. If no structural shift takes place, the agreement may still be understood as an expensive but rational experiment.
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Latest Small Island Research Notes
A Second Path Beyond the GPU? Architectural Thinking Behind NVIDIA’s Licensing Agreement with Groq
2026-03-05
Executive Summary
NVIDIA’s licensing agreement with Groq is worth watching not only because the technology itself is extreme, but because it may signal that AI compute architecture is being reconsidered. Even after GPUs have become the dominant platform for AI training and inference, NVIDIA is still willing to engage seriously with an execution model that runs almost counter to the mainstream path. That suggests the demands of the inference era may be making determinism important again.
At the core of Groq’s architecture is the use of scratchpad SRAM and static compiler scheduling in place of traditional cache hierarchies and runtime decision making. This shifts complexity away from hardware and toward the compiler. While this approach has often been seen as too aggressive in the past, it may become viable again under highly regular workloads such as deep learning inference.
This article argues that what NVIDIA may value is not Groq’s current product capability alone, but also its years of accumulated experience in dataflow compiler development and its access to an execution model that differs fundamentally from the GPU. Combined with NVIDIA’s strengths in synchronization control, packaging, and thermal management, some of the constraints that once made Groq difficult to scale may be partially eased.
Whether this agreement will become a second AI compute path remains uncertain. Its success still depends on compiler maturity, synchronization stability, and whether deterministic scheduling is truly well suited to large model inference. From a strategic perspective, however, this agreement may be better understood as an execution option. If future inference workloads place greater weight on latency determinism and system efficiency, NVIDIA will not be caught unprepared. If no structural shift takes place, the agreement may still be understood as an expensive but rational experiment.