Growing datasets and public benchmarks are making it harder to tell whether a model is being tested on something it hasn’t seen before.

On Thursday, Google DeepMind showed off what the company calls the first double-blind evaluation of a proprietary frontier-class AI model. The setup keeps Gemini’s model weights hidden from the evaluators while the test questions are hidden from Google.

The pilot tested Gemini 2.5 Flash Lite against private benchmarks from MLCommons and the Singapore AI Safety Institute, but rather than using the results to tout a new Gemini score, Google is focusing on how the tests were run, with neither side having access to the other’s data.

Benchmark leakage inflates scores

Google’s technical report cites earlier research that found signs of benchmark leakage in about half of the 31 models tested, and another study published this year that found contamination can inflate scores, particularly for larger models. This dynamic has already raised questions about where certain post-training coding gains actually come from.

Keeping benchmarks private would seem like the obvious answer, except closed models make that difficult. With a closed model, evaluators usually have to run their questions through the provider’s API, which means the company can see a test that was supposed to stay private, whereas running it independently would require the provider to hand over its model weights. Google’s setup is meant to give both sides another option.

Keeping benchmarks private would seem like the obvious answer, except closed models make that difficult.

How the enclave works

The pilot is using Google Cloud Confidential Space together with an NVIDIA H100 Confidential GPU and Intel TDX host memory encryption.

Google DeepMind provides Gemini with its weights and inference code, while the evaluator offers its benchmark prompts and evaluation code; these are then transmitted over encrypted connections into the enclave, where the evaluation takes place without either party gaining access to the other’s protected assets.

The model weights are stored in hardware-encrypted GPU memory, and the evaluation prompts are kept in encrypted host memory as well. After the test is complete, the evaluator is given the permitted results, and the temporary environment can then be destroyed.

Before either side sends over its private data, remote attestation verifies that the enclave is running the software they agreed on.

Code controls beyond encryption

The enclave protects what’s stored in memory, but the code running inside it can still create problems. If an application has unrestricted network access, for example, it could send sensitive information somewhere else.

OpenMined’s PySyft handles that part of the process by allowing Google and the evaluator to approve the code in advance and to block sensitive parts of the evaluation from making external connections.

That process takes some work, although compute doesn’t appear to be the problem. The paper puts the overhead at less than 5% and instead points to legal agreements and code reviews between organizations as some of the bigger hurdles to running these evaluations.

The researchers eventually want to make attestation much less hands-on, comparing the goal to the HTTPS lock icon in a browser, where the security checks happen without the user having to deal with the underlying hashes and keys.

Google still has a hand in verification because, although the Confidential Space guest OS is open source and its build process has been externally validated, individual builds rely on private signing keys and can’t be independently reproduced. At the same time, Google’s own services are used to sign and verify the attestation report.

The paper puts the overhead at less than 5% and points instead to the legal agreements and code reviews between organizations as some of the bigger hurdles to running these evaluations.

Scaling past one GPU

So while the system reduces how much the two sides need to trust each other, it doesn’t remove trust entirely from the equation. Some of it simply moves elsewhere, including to the hardware itself. There is still some trust involved in the hardware itself, including the assumption that the cloud provider and the hardware maker aren’t working together to circumvent the protections.

Then there’s the benchmark itself. MLCommons points out that it still needs to be carefully managed, no matter how well the questions are protected.

The pilot ran Gemini 2.5 Flash Lite on a single H100 80GB Confidential GPU. Still, the researchers are already looking at clusters of H100 and B200 GPUs connected via encrypted links to eventually evaluate models that are too large to fit on a single GPU.

If this approach catches on, developers could have another way to look beyond the benchmark score, with some proof that the company behind the model didn’t see the test before the results came out.

If this approach catches on, a benchmark score could offer some proof that the company behind the model didn’t get to see the test first.

The post Google found a way to test Gemini without seeing the questions appeared first on The New Stack.

AI coding company Replit is throwing its weight behind the model-routing trend by making its “intelligent model routing” system the default across every account.

The system automatically selects the underlying model to handle a task as it evolves, with Replit weighing quality, speed, and cost in its routing decisions.

The company says the feature, dubbed Auto mode, will become the default option for all users, though Core and Pro subscribers can still override it and manually select models when they want more control.

Model-routing momentum

The announcement comes hot on the heels of a flurry of activity in the model-routing realm. Earlier in August, Stripe agreed to acquire model gateway platform OpenRouter for a reported $8 billion. On the very same day, Ramp launched Router.com, which routes requests to the lowest-cost model that meets a specified performance bar.

Before all that, in July, SpaceX-owned Cursor launched its own router, which automatically selects models for coding requests and claims to deliver comparable performance at a substantially lower cost. Meanwhile, Meta is reportedly developing an internal router called “Switchboard” that scores coding tasks by difficulty and sends simpler jobs to cheaper models.

“Across one model family, per-token rates can span orders of magnitude. At the same time, the intelligence of cheaper, smaller models is now much closer to their larger frontier counterparts, providing us a lot of room for cost optimizations.”

Michele Catasta, president and head of AI at Replit, says that one reason for the wider push into routing is simple economics — the growing gap between what models cost and the level of capability developers actually need for a given task.

“Across one model family, per-token rates can span orders of magnitude,” Catasta tells The New Stack. “At the same time, the intelligence of cheaper, smaller models is now much closer to their larger frontier counterparts, providing us a lot of room for cost optimizations.”

Replit, for its part, has been moving in this direction for some time. Catasta says that the company has spent recent months experimenting with early versions of Auto mode, subagent routing, and multiple iterations.

“Like any pivotal launch, we thoroughly tested Intelligent Model Routing in beta for a long period of time before we decided to release it in public,” he says. “The most important learning is understanding from first principles the failure modes of every experiment, so we could keep hill climbing on the final system that we just shipped.”

Enter Auto mode

The foundation of that work surfaced last week when Replit introduced Free Mode, a lower-cost Agent mode that doesn’t consume usage credits and uses Auto to choose the model on theuser’ss behalf, subject to usage limits.

Now, that same Auto routing approach is being pushed more broadly across Replit. The company says intelligent model routing will become the default across every account, with all users starting in Free Mode and Replit deciding which model is best suited to the task.

Free Mode, it’s worth noting, isn’t “free” in the sense of unlimited usage. When it launched, Replit made it available to Core and Pro subscribers without consuming their usage credits, but imposed limits that reset every five hours, with higher allowances for Pro users. In Free Mode, users cannot manually select a model.

Core and Pro subscribers can, however, switch to Replit’s Power or Max modes, where they can turn off Auto and choose a model themselves. Replit may also suggest moving a task into one of those higher-powered modes when it determines that more capability is required, though those modes can incur usage costs.

For Enterprise customers, meanwhile, administrators can restrict Auto to an approved set of models for each workspace, allowing Replit to continue routing tasks automatically while keeping model choice within company policy.

The agent advantage

Even before SpaceX agreed to pay a cool $60 billion to acquire Cursor, the AI coding startup had long been investing in its own coding models, including its Composer family. More recently, under the auspices of SpaceX, Cursor has been developing more cutting-edge models, too.

Replit, by contrast, isn’t making ownership of the underlying model layer central to its pitch. Instead, it’s betting that controlling the agent and the systems around it gives Replit enough insight to make better model-selection decisions on the fly.

“Replit has owned, from the start, both the agent harness and the infrastructure surrounding models which in turn allows us to train sophisticated model routers.”

“Replit has owned, from the start, both the agent harness and the infrastructure surrounding models which in turn allows us to train sophisticated model routers,” Catasta said. “Only in this way can we always offer useful intelligence to our users at the most competitive price point.”

That becomes particularly relevant as an Agent task unfolds, with Replit noting that its system can change which model it uses as the task develops, seeking a better trade-off between capability and cost at different points in the process. But for Catasta, that kind of dynamic routing is still only one part of a much broader research problem around how agents should use models.

“Model routing is still in its early development phase, and we expect further research will move the needle on serving the best intelligence when customers most need it,” Catasta explains. “Routing is but one piece of the puzzle that is tightly integrated to many other aspects of our harness research.”

“No third-party router company could reproduce the same results for our own agent.”

Replit also argues that seeing how people use its own Agent gives it an advantage that a standalone routing provider would struggle to reproduce. Catasta says a router has to infer the nature, difficulty, scope, and intent of a request, with Replit able to train against proprietary usage data and observe those signals across its user base.

“No third-party router company could reproduce the same results for our own agent,” he says.

The post Replit’s new default: Auto mode picks the best model for each task appeared first on The New Stack.

Nvidia has reportedly agreed to buy Hugging Face for $12.9 billion, putting one of the biggest names in AI hardware in charge of a platform developers rely on to find and run open models.

The Information first reported the deal Wednesday, citing a person familiar with the agreement. Nvidia and Hugging Face had not publicly confirmed it as of publication.

Hugging Face doesn’t push developers toward one chipmaker, which is what makes the acquisition interesting. Its Optimum libraries work with Nvidia’s TensorRT-LLM and also support hardware from AMD, Intel, and AWS. Projects such as Optimum AMD and Optimum Intel let developers run Transformers and Diffusers models on non-Nvidia hardware.

The company also plays a role in what happens after a developer chooses a model, including how easily they can get it running on the hardware they want to use. The problem for Nvidia might be this: It is buying a platform whose value depends on openness and hardware neutrality, but if the purchase means Nvidia hardware is favored, that value might diminish.

The company also plays a role in what happens after a developer chooses a model, including how easily they can get it running on the hardware they want to use.

Hugging Face already sits between the model and the chip

Hugging Face has expanded well beyond file hosting. With Inference Endpoints, developers can deploy a model from the Hub while Hugging Face handles the underlying infrastructure.

Those hosted deployments can run on AWS, Microsoft Azure or Google Cloud, but most of the GPU options Hugging Face lists are Nvidia chips, including the T4, L4 and A100. That gives developers a wider choice of hardware through Hugging Face’s open-source libraries than through its hosted services.

If the deal goes through, Nvidia would own both sides of that experience.

Deployment defaults favor Nvidia

NIM (Nvidia Inference Microservices) already works with models hosted on Hugging Face. Developers can point NIM to an hf:// repository path and pull the model directly from the Hub.

Owning Hugging Face would give Nvidia more room to bring NIM and CUDA-optimized containers directly into the deployment experience. Nvidia hasn’t announced plans to make NIM the default, and support for AMD and Intel could remain exactly where it is.

Owning Hugging Face would give Nvidia more room to bring NIM and CUDA-optimized containers directly into the deployment experience.

The bigger question is what happens over time. Nvidia could provide earlier support for new models on its own hardware or make deployment easier. At the same time, AMD, Intel, and AWS may have to reconsider how much engineering work they want to contribute to integrations maintained within a competitor-owned platform. Some of that work could eventually move elsewhere.

For developers, the difference may come down to which path requires less work. A competing chip doesn’t have to disappear from Hugging Face to become less appealing if an Nvidia model deployment takes fewer steps. We’ve seen a similar fight over the layers between AI models and the developers using them, with Cloudflare building more of that infrastructure itself.

Open models counter custom chips

That tension also helps explain why Hugging Face could be worth considerably more to Nvidia than its revenue alone would suggest.

Nvidia has been expanding its own Nemotron family of open models while investing heavily across the AI ecosystem. At the same time, some of its biggest customers are working to reduce their dependence on Nvidia hardware.

Google has its TPUs, AWS has Trainium, and Microsoft has Maia. OpenAI and Anthropic are also developing their own AI server chips. The push extends beyond the hyperscalers. Earlier this month, five European companies committed to purchasing AI compute built around non-Nvidia hardware that hasn’t been manufactured yet — a sign that the appetite for alternative accelerators is strong enough to attract forward contracts.

OpenAI this week published results from its new Jalapeño accelerator, which showed 1.5 to 1.9 times more work per watt while cutting end-to-end latency by up to 3.6 times on large open-weight models — although the chip has not yet been deployed at anything approaching Nvidia’s scale. A strong open-model ecosystem gives Nvidia a counterweight to that trend.

Open models are often expected to run in very different environments, and Hugging Face helps developers make that possible. A model found on the Hub might end up running on Nvidia hardware, an AMD GPU, or a cloud accelerator.

That flexibility is part of what Nvidia would be buying. Pushing Hugging Face too heavily toward its own hardware could make the platform less useful to developers who rely on it to work across different systems.

Interest in those models is also growing. Models from companies including DeepSeek, Moonshot AI and Z.ai have narrowed the gap with proprietary systems. At the same time, Hugging Face CEO Clément Delangue told The Information in June that the company had doubled its number of paying subscribers during the first six months of 2026. Delangue later said the company was “close to profitability.”

The Information puts Hugging Face’s annualized revenue at about $150 million. Against a $12.9 billion price tag, that’s a multiple of roughly 86.

So Nvidia would be paying for much more than Hugging Face’s current business. It would be buying a place developers already turn to when they want to work with open models, including models that don’t have to run on Nvidia hardware.

It would be buying a place developers already turn to when they want to work with open models, including models that don’t have to run on Nvidia hardware.

Community resists easy forking

Buying Hugging Face wouldn’t give Nvidia control over everything developers find there. Libraries such as transformers and diffusers are open source, and models on the Hub remain subject to their own licenses. Openly licensed models can still be hosted elsewhere, while the underlying libraries can be forked.

Much harder to recreate is the community Hugging Face has built around them. Developers already know where to look for models and have built workflows around the Hub and its integrations.

The post Nvidia’s $12.9B Hugging Face deal has an open-source problem appeared first on The New Stack.