Together AI’s $305M bet: Reasoning models like DeepSeek-R1 are increasing, not decreasing, GPU demand

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

Because it seems, that’s not essentially the case. At the least, in keeping with Together AI, the rise of DeepSeek and open-source reasoning has had the precise reverse impact: As a substitute of lowering the necessity for infrastructure, it’s rising it.

That elevated demand has helped gas the expansion of Collectively AI’s platform and enterprise. Right this moment the corporate introduced a $305 million collection B spherical of funding, led by Common Catalyst and co-led by Prosperity7. Collectively AI first emerged in 2023 with an goal to simplify enterprise use of open-source massive language fashions (LLMs). The corporate expanded in 2024 with the Collectively enterprise platform, which permits 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 general year-over-year. The corporate’s prospects embrace enterprises in addition to AI startups equivalent to  Krea AI, Captions and Pika Labs.

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

The massive impression DeepSeek-R1 is having on AI infrastructure demand

DeepSeek-R1 was vastly disruptive when it first debuted, for quite a lot of causes — one in every of which was the implication {that a} forefront open-source reasoning mannequin could possibly 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 it’s good to distribute it over a number of servers. And since the standard is increased, there’s typically extra demand on the highest finish, which suggests you want extra capability.”

Moreover, he famous that DeepSeek-R1 typically has longer-lived requests that may final two to a few minutes. Great consumer 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 embrace:

• Coding brokers: Reasoning fashions assist break down bigger issues into steps.
• Decreasing hallucinations: The reasoning course of helps to confirm the outputs of fashions, thus lowering hallucinations, which is vital for purposes the place accuracy is essential.
• Enhancing non-reasoning fashions: Clients 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 massive quantities of human-labeled knowledge.

Agentic AI can be driving elevated demand for AI infrastructure 

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

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

To assist assist agentic AI workloads, Collectively AI just lately has acquired CodeSandbox, whose know-how offers light-weight, fast-booting digital machines (VMs) to execute arbitrary, safe code inside the Collectively AI cloud, the place the language fashions additionally reside. This permits Collectively AI to cut back the latency between the agentic code and the fashions that have to be referred to as, bettering the efficiency of agentic workflows.

Nvidia Blackwell is already having an impression

All AI platforms are going through elevated calls for. 

That’s one of many the reason 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 value 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 knowledgeable (MoE) fashions, that are skilled throughout a number of InfiniBand-connected servers. He famous that Blackwell chips are additionally anticipated to supply an even 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 equivalent to Groq and Samba Nova which might be 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 high. This permits prospects to simply construct with open-source fashions or develop their very own fashions on the Collectively AI platform. The corporate additionally has a concentrate on analysis growing optimizations and accelerated runtimes for each inference and coaching.

“For example, 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.”