In recent days, a compelling argument has gone viral: that NVIDIA “admitted” its architecture is “broken” because it introduced an AI chip that skips HBM memory and uses GDDR memory instead. The phrase sounds like a perfect headline for social media, but the reality is more interesting—and, above all, more nuanced: NVIDIA is responding to a fundamental shift in how AI is deployed in production, where the battle is no longer just about training models but serving them cost-effectively when the workload jumps to hundreds of thousands or millions of tokens.
The key piece explaining this shift is Rubin CPX, a specialized accelerator designed specifically for one part of inference: context processing (prefill). Instead of trying to be “the all-in-one chip,” its approach is to separate phases and, in doing so, separate costs.
What is Rubin CPX and why is it attracting so much attention
According to NVIDIA itself, Rubin CPX is aimed at large-scale inference scenarios, where the system “reads” large volumes of information (documents, code repositories, long histories, transcribed videos, etc.) before generating a response. For this task, the bottleneck isn’t always the same as in token-by-token generation (decode), which brings the economic incentive: using a different memory than HBM.
NVIDIA positions Rubin CPX within a rack-scale platform, Vera Rubin NVL144, where accelerators designed for different inference needs coexist. The company emphasizes efficiency: making hardware better reflect real-world usage patterns.
Simply put: if one part of the workload requires heavy computation and another depends more on data movement, trying to address both with the same “hammer” can end up expensive. Rubin CPX signals that NVIDIA sees a market where prefill is no longer marginal, but a cost component that can be dominant for certain workloads.
The key: prefill and decode no longer behave the same
The trigger for this change is the rise of production AI with long contexts. In modern interactions — especially with enterprise assistants, agents, document analysis, or programming — the system may spend significant time “absorbing” information before even starting to respond.
Within this flow, two stages emerge:
- Prefill (context processing): the model ingests the entire prompt and builds internal states (like KV cache) to reason about what’s read.
- Decode (generation): the model produces output token by token, reusing those internal states.
The industry has lumped all this under “inference” for years, but the increase in context length has made it clear that, for certain cases, it’s no longer reasonable to optimize as if everything was decode. This “phase separation” approach isn’t new: academic and engineering work has long explored serving prefill and decode with different strategies to increase throughput and reduce interference between long and short requests.
This is where the “broken architecture” narrative falls short. What we’re seeing is something else: usage patterns are changing and hardware is adapting accordingly.
Software matters as much as silicon: Dynamo and state transport
Separating phases sounds good on a diagram, but it comes with significant technical costs: you need to move state (like KV cache) and coordinate which node does what, without adding latency. If the “coordination toll” eats up the savings, the idea collapses.
NVIDIA has been preparing the groundwork with Dynamo, an orchestration layer to scale inference and manage components such as routing, caching, and data transfer between stages and nodes. NVIDIA presents it as a way to “disaggregate” and optimize the production model serving process, leveraging libraries and mechanisms designed to reduce operational costs.
Viewed like this, Rubin CPX isn’t just “a weird chip without HBM,” but part of a broader package where architecture + software + platform are trying to align with the new paradigm: large contexts, multi-agent, multimodal, and with more long sessions.
Competitive pressure: TPUs, Trainium, and the “do it in-house” approach of hyperscalers
This shift isn’t happening in a vacuum. Major cloud providers have been pushing their own silicon for years to reduce dependence, control costs, and better tailor hardware to actual workloads.
- Google has been strengthening its TPU efforts, introducing Ironwood as a step toward efficiency and scalability for modern AI workloads.
- AWS has adopted a similar approach with Trainium, and Trainium 3 has been announced as generally available for training and serving models within its ecosystem.
- Market dynamics also play a role: reports indicate that Meta has explored agreements to use Google TPUs for some of its computation needs—showing that even GPU-focused investors are open to diversifying if it makes economic sense.
- Large-scale collaborations between model developers and infrastructure providers reinforce the idea that “alternate silicon” is no longer niche.
Meanwhile, forecasts for inference market growth—projecting very high figures by the end of the decade—fuel the race to reduce token costs.
So, is NVIDIA “admitting” it was wrong?
What can be stated conclusively is that NVIDIA is publicly acknowledging that there are workloads where inference benefits from resource separation, and where optimizing context processing differently from generation makes sense. That’s an important shift in emphasis.
However, jumping to the conclusion that “everything earlier was wrong” is a leap. GPUs with HBM remain critical for training, many bandwidth-dominated inference scenarios, and cases where integration and internal latency (including advanced interconnects) are part of the value proposition.
What’s clear is that the market is heading toward a world where:
- long contexts are no longer exceptional,
- profitability is measured by phases, and
- clients—especially hyperscalers—demand options that avoid paying a “luxury” premium where it’s unnecessary.
Rubin CPX positions itself as a response to this trend. As always, real confirmation will come not just from speeches but from deployments, metrics, and operational results.
Frequently Asked Questions
What types of companies benefit from a “separate prefill/decode” approach?
Organizations with heavy document analytics, internal assistants with large knowledge bases, agents querying systems for minutes or hours, or workflows involving large contexts (legal, compliance, engineering, technical support).
Why is it relevant that Rubin CPX uses GDDR instead of HBM?
Because it changes the cost structure of the accelerator and its industrial availability. The goal is to tailor hardware to workloads where HBM doesn’t provide as much relative advantage as in other scenarios.
Will this make inference cheaper in the cloud?
It can help, but it’s not automatic: it depends on how the service is packaged (managed services, token pricing, reservations), on competition (TPUs/Trainium), and operational costs (network, energy, cooling).
What should an IT team watch when evaluating infrastructure for AI?
More than just the “winning model,” it’s important to look at cost per token in production, actual latency with long contexts, capacity to scale simultaneous sessions, and compatibility with hybrid architectures (GPU + vendor-specific silicon).
Source: Shanaka Anslem Perera