Nvidia’s $20 billion strategic licensing deal with Groq represents one of the first clear steps in a four-front battle over the future AI stack. In 2026, that struggle will become clear for entrepreneurs.
For the technical decision makers we talk to every day – the people who build the AI applications and the data pipelines that power them – this deal signals that the era of the one-size-fits-all GPU as the default AI inference answer is coming to an end.
We are entering the era of disaggregated inference architecturewhere the silicon itself is split into two different types to accommodate a world that requires both massive context and immediate reasoning.
Why inference breaks the GPU architecture in half
To understand why Nvidia CEO Jensen Huang dropped a third of his shares reported a cash pile of $60 billion In a licensing deal, you have to look at the existential threats highlighted in his company’s reports 92% market share.
The industry reached a tipping point at the end of 2025: for the first time, inference – the phase where trained models actually run – was surpassed training in terms of total data center revenueaccording to Deloitte. In this new ‘Inference Flip’ the statistics have changed. While accuracy remains the starting point, the battle is now being waged over the latency and ability to maintain the ‘state’ of autonomous agents.
There are four fronts in that battle, and each front points to the same conclusion: Inference workloads are fragmenting faster than GPUs can generalize.
1. Bisecting the GPU: Prefilling vs. Decoding
Gavin Baker, an investor in Groq (and therefore biased, but also extremely fluent in architecture), in summary the core of the Groq deal is clear: “Inference is splitting into pre-completion and decoding.”
Prime And decode are two different phases:
The pre-filling phase: Think of this as the user’s “prompt” phase. The model must process massive amounts of data (whether it’s a 100,000-line codebase or an hour of video) and compute a contextual understanding. This is ‘compute bound’ and requires massive matrix multiplication which Nvidia’s GPUs have historically been excellent at.
The generation (decoding) phase: This is the actual token-by-token ‘generation’. Once the prompt is recorded, the model generates one word (or token) at a time, feeding each word (or token) back into the system to predict the next. This is “memory bandwidth bound”. If the data cannot be moved from memory to the processor fast enough, the model will stutter, no matter how powerful the GPU is. (This is where Nvidia was weak, and where Groq’s dedicated language processing unit (LPU) and associated SRAM memory excelled. More on that later.)
Nvidia has announced an expectant one Vera Rubin family of chips that it is specifically designing to address this split. The Rubin CPX Part of this family is the designated “prefill” workhorse, optimized for massive context windows of 1 million tokens or more. To deal with this scale in an affordable way, it takes away from the eye-watering costs of high bandwidth memory (HBM) – Nvidia’s current gold standard memory that sits right next to the GPU chip – using 128 GB of new kind of memory instead, GDDR7. While HBM offers extreme speed (though not as fast as Groq’s static random-access memory (SRAM)), GPU offerings are limited and cost is a scale barrier; GDDR7 provides a more cost-effective way to ingest massive data sets.
Meanwhile, the “Groq-flavored” silicon that Nvidia is integrating into its inference roadmap will serve as the fast “decode” engine. This is about neutralizing a threat from alternative architectures like Google’s TPUs and maintaining their dominance CUDA, Nvidia’s software ecosystem that has served as its main moat for more than a decade.
All this was enough for Baker, the Groq investor, to predict that Nvidia’s move to license Groq will cause all other specialized AI chips to be canceled – that is, outside of Google’s TPU, Tesla’s AI5 and AWS’s Trainium.
2. The differentiated power of SRAM
The core of Groq’s technology is SRAM. Unlike the DRAM in your PC or the HBM on an Nvidia H100 GPU, SRAM is etched directly into the processor’s logic.
Michael Stewart, managing partner of Microsoft’s venture fund M12, describes SRAM as the best for moving data over short distances with minimal energy. “The energy to move a little in SRAM is about 0.1 picojoule or less,” said Stewart. “Moving between DRAM and the processor is 20 to 100 times worse.”
In the world of 2026, where agents must reason in real time, SRAM acts as the ultimate “scratchpad”: a fast workspace where the model can manipulate symbolic operations and complex reasoning processes without the “wasted cycles” of shuttling external memory.
However, SRAM has a major disadvantage: it is physically bulky and expensive to produce, meaning its capacity is limited compared to DRAM. This is where Val Bercovici, chief AI officer at Weka, another company offering memory for GPUs, sees the market segmentation.
Groq-friendly AI workloads – where SRAM has the advantage – are the ones that use small models of 8 billion parameters and below, Bercovici said. However, this is not a small market. “It’s just a huge market segment that wasn’t being served by Nvidia, which is edge inference, low latency, robotics, voice and IoT devices – things we want to run on our phones without the cloud for convenience, performance or privacy,” he said.
This 8B sweet spot is important because an explosion occurred in 2025 model distillationwhere many companies shrink huge models into highly efficient smaller versions. While SRAM isn’t practical for the trillion-parameter “frontier” models, it is perfect for these smaller, high-speed models.
3. The anthropic threat: the rise of the ‘portable stack’
Perhaps the most underrated driver of this deal is Anthropic’s success in making its stack portable through accelerators.
The company has pioneered a wearable tech approach for training and inference – essentially a software layer that allows the Claude models to run across multiple AI accelerator families – including Nvidia’s GPUs and Google’s Ironwood TPUs. Until recently, Nvidia’s dominance was protected because running high-performance models outside the Nvidia stack was a technical nightmare. “It’s anthropic,” Weka’s Bercovici told me. “The fact that Anthropic was able to… build a software stack that could run on both TPUs and GPUs, I don’t think is appreciated enough in the marketplace.”
(Disclosure: Weka has been a sponsor of VentureBeat events.)
Anthropic recently agreed to gain access to 1 million TPUs from Google, which amounts to more than a gigawatt of computing capacity. This multi-platform approach ensures that the company is not held hostage by Nvidia’s price or supply constraints. So for Nvidia, the Groq deal is just as much a defensive move. By integrating Groq’s ultra-fast inference IP, Nvidia ensures that the most performance-sensitive workloads – such as those running small models or as part of real-time agents – can be accommodated within Nvidia’s CUDA ecosystem, even as competitors attempt to move to Google’s Ironwood TPUs. CUDA is the special software that Nvidia provides to developers to integrate GPUs.
4. The agentic ‘state war’: Manus and the KV Cache
The timing of this Groq deal coincides with Meta’s acquisition of the agent pioneer Manus just two days ago. Part of Manus’s significance was his obsession with it stateliness.
If an agent can’t remember what he did ten steps ago, he’s useless for real-world tasks like market research or software development. KV cache (key value cache) is the “short-term memory” that an LLM builds during the prefill phase.
Manus reported that for production quality agents, the ratio of input tokens to output tokens can reach 100:1. This means that for every word an agent says, he ‘thinks’ and ‘remembers’ 100 others. In this environment, the number of KV Cache hits is the most important metric for a production agent, Manus said. If that cache is “deleted” from memory, the agent loses its train of thought and the model has to burn enormous energy to recompute the prompt.
Groq’s SRAM can be a “job” for these agents – although, again, mostly for smaller models – because it allows for near-instantaneous retrieval of that status. Combined with Nvidias Dynamo frame and the KVBM, Nvidia is building an “inference operating system” that will allow inference servers to distribute this state across SRAM, DRAM, HBM, and other flash-based offerings like Bercovici’s Weka.
Thomas Jorgensen, senior director of technology enablement at Supermicro, which specializes in building clusters of GPUs for large enterprises, told me in September that compute power is no longer the main bottleneck for high-end clusters. Inputting data to GPUs was the bottleneck, and breaking that bottleneck requires memory.
“The entire cluster is now the computer,” Jorgensen said. “Networks are becoming an internal part of the beast…feeding the beast with data is becoming increasingly difficult as bandwidth between GPUs is growing faster than anything else.”
This is why Nvidia turns to disaggregated inference. By separating the workloads, enterprise applications can use specialized storage tiers to feed data with memory-class performance, while the specialized “Groq-inside” silicon handles fast token generation.
The verdict for 2026
We are entering an era of extreme specialization. For decades, incumbents could win by advancing one dominant general-purpose architecture – and their blind spot was often what they ignored at the edges. Intel’s long neglect of energy efficiency is the classic example, Michael Stewart, managing partner of Microsoft’s venture fund M12, told me. Nvidia indicates it will not repeat this mistake. “If even the leader, even the lion of the jungle is going to acquire talent, acquire technology – that’s a sign that the entire market just wants more options,” Stewart said.
For tech leaders, the message is: stop designing your stack as if it is one rack, one accelerator, one answer. In 2026, the advantage will go to the teams that explicitly label workloads – and route them to the right level:
prefill-heavy vs. decode-heavy
long context versus short context
interactive vs batch
small model versus large model
edge constraints versus data center assumptions
Your architecture will follow these labels. In 2026, the ‘GPU strategy’ will no longer be a purchasing decision, but a routing decision. The winners will not ask which chip they purchased; they will ask where each token went and why.
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