Collectively AI’s $305M guess: Reasoning fashions like DeepSeek-R1 are rising, not reducing, GPU demand

When DeepSeek-R1 first emerged, the prevailing concern that shook the {industry} was that superior reasoning might be achieved with much less infrastructure.

Because it seems, that’s not essentially the case. A minimum of, in line with Collectively AI, the rise of DeepSeek and open-source reasoning has had the precise reverse impact: As an alternative of decreasing the necessity for infrastructure, it’s rising it.

That elevated demand has helped gas the expansion of Collectively AI’s platform and enterprise. At present the corporate introduced a $305 million sequence B spherical of funding, led by Common Catalyst and co-led by Prosperity7. Collectively AI first emerged in 2023 with an purpose to simplify enterprise use of open-source giant language fashions (LLMs). The corporate expanded in 2024 with the Collectively enterprise platform, which allows AI deployment in digital non-public cloud (VPC) and on-premises environments. In 2025, Collectively AI is rising its platform as soon as once more with reasoning clusters and agentic AI capabilities. 

The corporate claims that its AI deployment platform has greater than 450,000 registered builders and that the enterprise has grown 6X total year-over-year. The corporate’s prospects embody enterprises in addition to AI startups comparable to  Krea AI, Captions and Pika Labs.

“We are actually serving fashions throughout all modalities: language and reasoning and pictures and audio and video,” Vipul Prakash, CEO of Collectively AI, advised VentureBeat.

The large influence DeepSeek-R1 is having on AI infrastructure demand

DeepSeek-R1 was vastly disruptive when it first debuted, for various causes — one in all which was the implication {that a} vanguard open-source reasoning mannequin might be constructed and deployed with much less infrastructure than a proprietary mannequin.

Nevertheless, Prakash defined, Collectively AI has grown its infrastructure partially to assist assist elevated demand of DeepSeek-R1 associated workloads.

“It’s a reasonably costly mannequin to run inference on,” he stated. “It has 671 billion parameters and you must distribute it over a number of servers. And since the standard is greater, there’s usually extra demand on the highest finish, which suggests you want extra capability.”

Moreover, he famous that DeepSeek-R1 usually has longer-lived requests that may final two to a few minutes. Great person demand for DeepSeek-R1 is additional driving the necessity for extra infrastructure.

To fulfill that demand, Collectively AI has rolled out a service it calls “reasoning clusters” that provision devoted capability, starting from 128 to 2,000 chips, to run fashions at the absolute best efficiency.

How Collectively AI helps organizations use reasoning AI

There are a variety of particular areas the place Collectively AI is seeing utilization of reasoning fashions. These embody:

• Coding brokers: Reasoning fashions assist break down bigger issues into steps.
• Lowering hallucinations: The reasoning course of helps to confirm the outputs of fashions, thus decreasing hallucinations, which is necessary for purposes the place accuracy is essential.
• Enhancing non-reasoning fashions: Prospects are distilling and bettering the standard of non-reasoning fashions.
• Enabling self-improvement: The usage of reinforcement studying with reasoning fashions permits fashions to recursively self-improve with out counting on giant quantities of human-labeled knowledge.

Agentic AI can also be driving elevated demand for AI infrastructure 

Collectively AI can also be seeing elevated infrastructure demand as its customers embrace agentic AI.

Prakash defined that agentic workflows, the place a single person request ends in 1000’s of API calls to finish a process, are placing extra compute demand on Collectively AI’s infrastructure.

To assist assist agentic AI workloads, Collectively AI not too long ago has acquired CodeSandbox, whose expertise gives light-weight, fast-booting digital machines (VMs) to execute arbitrary, safe code throughout the Collectively AI cloud, the place the language fashions additionally reside. This enables Collectively AI to scale back the latency between the agentic code and the fashions that should be referred to as, bettering the efficiency of agentic workflows.

Nvidia Blackwell is already having an influence

All AI platforms are going through elevated calls for. 

That’s one of many the explanation why Nvidia retains rolling out new silicon that gives extra efficiency. Nvidia’s newest product chip is the Blackwell GPU, which is now being deployed at Collectively AI.

Prakash stated Nvidia Blackwell chips price round 25% greater than the earlier era, however present 2X the efficiency. The GB 200 platform with Blackwell chips is especially well-suited for coaching and inference of combination of professional (MoE) fashions, that are educated throughout a number of InfiniBand-connected servers. He famous that Blackwell chips are additionally anticipated to supply a much bigger efficiency enhance for inference of bigger fashions, in comparison with smaller fashions.

The aggressive panorama of agentic AI

The market of AI infrastructure platforms is fiercely aggressive. 

Collectively AI faces competitors from each established cloud suppliers and AI infrastructure startups. All of the hyperscalers, together with Microsoft, AWS and Google, have AI platforms. There’s additionally an rising class of AI-focussed gamers comparable to Groq and Samba Nova which are all aiming for a slice of the profitable market.

Collectively AI has a full-stack providing, together with GPU infrastructure with software program platform layers on prime. This enables prospects to simply construct with open-source fashions or develop their very own fashions on the Collectively AI platform. The corporate additionally has a deal with analysis growing optimizations and accelerated runtimes for each inference and coaching.

“As an illustration, we serve the DeepSeek-R1 mannequin at 85 tokens per second and Azure serves it at 7 tokens per second,” stated Prakash. “There’s a pretty widening hole within the efficiency and price that we are able to present to our prospects.”