Nvidia’s $20 billion strategic licensing deal with Grok represents one of the first clear steps in a four-front battle over the future AI stack. 2026 is the time when that battle will become clear for enterprise makers.
For the tech decision makers we talk to every day — the people building AI applications and the data pipelines that run them — this deal is a sign that the era of one-size-fits-all GPUs as the default AI inference answer is coming to an end.
we are entering the era different estimation architectureWhere silicon is being divided into two different types to accommodate a world that demands both large-scale context and instantaneous logic.
Why is Speculation breaking the GPU architecture into two parts?
To understand why Nvidia CEO Jensen Huang dropped a third of his reported $60 billion cash pile on a licensing deal, you have to look at the existential threats looming over his company’s reported 92% market share.
According to Deloitte, the industry reached a tipping point in late 2025: For the first time, inference – the stage where trained models are actually run – overtook training in terms of total data center revenue. in this new "guess flip," The metrics have changed. While accuracy remains the baseline, the battle is now being fought on latency and maintainability "State" In autonomous agents.
There are four fronts to that battle, and each front points to the same conclusion: inference workloads are fragmenting faster than GPUs can generalize.
1. Breaking the GPU into two parts: prefill vs decode
Gavin Baker, an investor in Grok (and therefore biased, but also unusually fluent on architecture), summarized the main driver of the Grok deal clearly: “The estimate is different across prefill and decode.”
prefill And to decipher There are two different stages:
- Prefill Step: Treat it like a user "ready" stage. The model must take in massive amounts of data – whether it’s a 100,000-line codebase or an hour of video – and compute a contextual understanding. it is "calculated," Requires large-scale matrix multiplication at which Nvidia’s GPUs have historically excelled.
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Generation (decode) phase: This is real token-to-token "Generation.” Once the signal is ingested, the model generates one word (or token) at a time, feeding each back into the system to predict the next. This is "Memory-bandwidth bound." If data can’t move from memory to the processor fast enough, the model stalls, no matter how powerful the GPU. (This is where Nvidia was weak, and where Grok’s specialized language processing unit (LPU) and its associated SRAM memory shine. More on that in a bit.)
Nvidia has announced an upcoming vera rubin The family of chips that it is designing specifically to handle this division. rubin cpx The component of this family is named "prefill" Workhorse, optimized for huge reference windows of 1 million tokens or more. To handle this scale cost-effectively, it moves away from eye-watering expenses High Bandwidth Memory (HBM) – Nvidia’s current gold-standard memory which is located right next to the GPU die – and uses a new kind of memory instead of 128 GB, gddr7While HBM provides immense speed (though not as fast as Grok’s static random-access memory (SRAM), it is in limited supply on GPUs and its cost is a barrier to scale; GDDR7 provides a more cost-effective way to accommodate large-scale datasets,
meanwhile, "grok-flavored" The silicon, which Nvidia is integrating into its speculative roadmap, will serve as high-speed "to decipher" engine. It’s about neutralizing the threat from alternative architectures like Google’s TPUs and maintaining dominance cuda, Nvidia’s software ecosystem that has served as its primary moat for more than a decade.
All this was enough for Grok investor Baker to predict that Nvidia’s move to license Grok would cancel out all other specialized AI chips — that is, outside of Google’s TPU, Tesla’s AI5, and AWS’s Trenium.
2. Differentiated power of SRAM
At the heart of Grok’s technology sramUnlike the DRAM found in your PC or the HBM on an Nvidia H100 GPU, SRAM is imprinted directly into the processor’s logic,
Michael Stewart, managing partner of M12, Microsoft’s venture fund, describes SRAM as best for moving data over short distances with minimal energy. "SRAM has little vibration energy of 0.1 picojoules or less," Stewart said. "Moving it between DRAM and processor is about 20 to 100 times worse."
In the world of 2026, where agents must reason in real time, SRAM serves as the ultimate "scratchpad": a high-speed workspace where models can be manipulated without symbolic operations and complex logic processes "waste cycle" Of external memory shuttling.
However, SRAM has one major drawback: it is physically heavy and expensive to manufacture, meaning its capacity is limited compared to DRAM. That’s where Val Bercovici, chief AI officer at Weka, another company that offers memory for GPUs, sees the market segmentation.
Bercovici said the grok-friendly AI workloads – where SRAM has the advantage – are those that use small models of 8 billion parameters and below. However, this is not a small market. “It’s just a huge market segment that wasn’t served by Nvidia, which was edge penetration, low latency, robotics, voice, IoT devices – things we want to run on our phones without the cloud for convenience, performance or privacy," He said.
this 8b "sweet spot" Important because an eruption seen in 2025 model distillationWhere many enterprise companies are converting larger models into highly efficient smaller versions. While SRAM is not practical for trillion-parameter "marginal" models, it is perfect for these small, high-velocity models.
3. Anthropogenic Threat: The Rise of the ‘Portable Stack’
Perhaps the most underappreciated reason for this deal is Anthropic’s success in making its stack portable to accelerators.
The company has pioneered a portable engineering approach to training and inference — basically a software layer that allows its cloud models to run across multiple AI accelerator families — including Nvidia’s GPUs and Google’s Ironwood TPUs. Until recently, Nvidia’s dominance was secure because running high-performance models outside the Nvidia stack was a technical nightmare. “It’s anthropological,” Vaca’s Bercovici told me. “The fact that Anthropic was able to create a software stack that could work on TPUs as well as GPUs, I don’t think is being appreciated enough in the market.”
(Disclosure: Veka has been a sponsor of VentureBeat events.)
Anthropic has recently committed to access 1 mil tpu From Google, represents more than one gigawatt of compute capacity. This multi-platform approach ensures that the company is not held hostage by Nvidia’s pricing or supply constraints. So for Nvidia, the Groke deal is equally a defensive move. By integrating Grok’s ultra-fast inference IP, Nvidia is ensuring 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 try to jump ship on Google’s Ironwood TPUs. CUDA is a special software that Nvidia provides to developers to integrate GPUs.
4. Agentic ‘Statehood’ Wars: Manus and KV Cash
The timing of this Grok deal coincides with Meta’s acquisition of Agent Pioneer Manus Just two days ago. Manu’s importance was partly his obsession with statefulness,
If an agent can’t remember what it did 10 steps ago, it’s useless for real-world tasks like market research or software development. KV Cache (Key-Value Cache) Is "short term memory" The LLM is formed during the prefill phase.
Manus explained that for production-grade agents, The ratio of input tokens to output tokens can reach 100:1This means that for every word said by the agent it is "Thinking" And "remembering" 100 others. In this environment, KV cache hit rate is the most important metric for a production agent, Manus said. if it’s cash "Evicted" From memory, the agent loses its thought process, and the model has to burn a lot of energy to recompute the prompt.
Grok’s SRAM could be one "scratchpad" For these agents – although, again, mostly for smaller models – because it allows near-instant recovery of that state. combined with nvidia dynamo Framework and KVBM, Nvidia is building one "guess operating system" Which could split the situation into SRAM, DRAM, and other flash-based offerings like Bercovici’s Veca.
Thomas Jorgensen, senior director of technology enablement at Supermicro, which specializes in building clusters of GPUs for large enterprise companies, told me in September that compute is no longer the primary bottleneck for advanced clusters. Feeding data to the GPU was a bottleneck, and breaking that bottleneck requires memory.
"The whole cluster is now a computer," Jorgensen said. "Networking has become an intrinsic part of the beast… feeding data to the beast is becoming harder as the bandwidth between GPUs is growing faster than anything else."
That’s why Nvidia is pushing for different estimates. By separating workloads, enterprise applications can use specialized storage tiers to feed data at memory-class performance, while specialized "grok-inside" The silicon handles high-speed token generation.
2026 decision
We are entering an era of extreme specialization. For decades, incumbents could win by shipping a dominant general purpose architecture – and their blind spots were often undiscovered at the edges. Michael Stewart, managing partner of Microsoft’s venture fund M12, told me that Intel’s long neglect of low-power is a classic example of this. Nvidia is signaling it won’t repeat that mistake. “If the leader, even the lion in the jungle, will acquire talent, acquire technology — that’s a signal that the entire market wants more choice,” Stewart said.
For tech leaders, the message is Stop organizing your stack like it’s a rack, an accelerator, an answerIn 2026, the advantage will go to teams that clearly label workloads – and scale them to the right levels:
- Prefill-heavy vs decode-heavy
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Long-context vs. short-context
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interactive vs batch
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Small-model vs. large-model
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Edge constraints vs. data-center assumptions
Your architecture will follow those labels. In 2026, “GPU strategy” ceases to be a purchasing decision and becomes a routing decision. Winners won’t ask which chip they bought – they’ll ask where each token went, and why.
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