Zain Asgar, a Stanford adjunct professor and a founder with a history of successful exits, has secured an $80 million Series A funding round for his latest startup. This new venture is dedicated to astutely resolving the AI inference bottleneck problem, with Menlo Ventures leading the investment.
The company, Gimlet Labs, introduces what it describes as the inaugural and sole "multi-silicon inference cloud." This innovative software facilitates the simultaneous execution of an AI workload across a diverse array of hardware types. It possesses the capability to distribute an AI application's tasks across traditional CPUs, specialized AI-tuned GPUs, and high-memory systems alike.
“We basically run across whatever different hardware that’s available,” Asgar informed TechCrunch.
Tim Tully, the lead investor from Menlo Ventures, elaborated on this necessity in a blog post about the funding. He explained that a single AI agent often chains together multiple steps, each demanding distinct hardware resources: “Inference is compute-bound; decode is memory-bound; and tool calls are network-bound.”
Tully further noted that no single chip currently offers a comprehensive solution. However, with the continuous rollout of new hardware and the redeployment of aging GPUs, “the multi-silicon fleet is ready — it’s just missing the software layer to make it work.” This, Tully asserts, is precisely what Gimlet Labs provides.
Highlighting the economic impact of current trends, McKinsey estimates that data center spending could reach nearly $7 trillion by 2030 if the prevailing "deploy-more-compute" strategy persists. Asgar points out that existing, deployed hardware is utilized by applications only “somewhere between 15 to 30 percent” of the time.
“Another way to think about this: you’re wasting hundreds of billions of dollars because you’re just leaving idle resources,” he stated. Asgar emphasized, “Our goal was basically to try to figure out how you can get AI workloads to be 10x more efficient than ever, today.”
To achieve this ambitious goal, Asgar, alongside co-founders Michelle Nguyen, Omid Azizi, and Natalie Serrino, developed orchestration software. This software intelligently segments agentic workloads, enabling their simultaneous distribution across all available hardware.
Gimlet Labs claims its technology reliably accelerates AI inference by 3 to 10 times, without increasing cost or power consumption. The company further asserts its ability to slice the underlying AI model itself, allowing different portions to run on different architectures, thereby leveraging the optimal chip for each segment of the model.
The company has already forged partnerships with prominent chip manufacturers, including NVIDIA, AMD, Intel, ARM, Cerebras, and d-Matrix.
Gimlet's product, offered either as standalone software or via an API to its proprietary Gimlet Cloud, is not designed for the average AI app developer. Instead, it targets the largest AI model laboratories and data centers.
Having publicly launched in October, Gimlet Labs reported impressive eight-figure revenues (exceeding $10 million) right from the outset. Asgar revealed that the company’s customer base has more than doubled in the past four months, now including a major model developer and an exceptionally large cloud computing enterprise, though he refrained from disclosing their names.
The co-founding team previously collaborated at Pixie, a startup that developed an open-source observability tool for Kubernetes. Pixie was acquired by New Relic in 2020, merely two months after its launch with a $9 million Series A investment led by Benchmark. Pixie's technology has since been integrated into the open-source organization that governs Kubernetes.
The journey to this Series A funding began approximately a year ago when Asgar had a chance encounter with Tully, followed by angel investments from Stanford professors, which subsequently attracted venture capitalists. Post-launch, a term sheet quickly materialized. Asgar noted that once VCs learned he was considering offers, “we got a pretty big swarm of funding,” leading to the round being swiftly oversubscribed.
Including its prior seed funding, the startup has now accumulated a total of $92 million. This includes contributions from a distinguished roster of angel investors such as Bill Coughran of Sequoia, Stanford Professor Nick McKeown, former VMware CEO Raghu Raghuram, and Intel CEO Lip-Bu Tan. Gimlet Labs currently maintains a team of 30 employees.
Other notable investors in the company include Factory, which led the seed round, along with Eclipse Ventures, Prosperity7, and Triatomic.
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