Willow Care, the company behind Willow Voice, chose CoreWeave for reinforcement-learning infrastructure and forward-deployed support, according to a SiliconANGLE report published October 8, 2026. Willow says the arrangement lets its team focus on evaluating and aligning its speech system rather than managing the underlying infrastructure. The report does not publish Willow-specific performance results.
How Willow Voice turns speech into formatted text
Willow Voice combines speech recognition with a small language model that post-processes the recognized speech into written text. Willow co-founder and CTO Lawrence Liu described that second model as a way to format input in the form the user would have written it. The account is from Liu’s interview with theCUBE at CoreWeave’s Fully Connected event, as reported by SiliconANGLE.
That distinction matters: the reported system is not simply transcribing words. It first recognizes speech and then uses a language model to shape the result as text. Liu called the small model part of Willow’s “secret sauce,” but the article does not give its name, size, or technical details.
What Willow is training on
SiliconANGLE reports that Willow is fine-tuning its speech-recognition model on quiet, real-world audio. Liu offered the example of someone whispering in a coffee shop. That example suggests the team is addressing low-volume speech in everyday surroundings; it is not a full account of Willow’s training data or data practices.
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The report does not disclose the quantity or composition of the audio, the evaluation protocol, or how Willow measures improvements. It therefore does not establish a particular accuracy level or show that the approach outperforms another model.
Why Willow selected CoreWeave
Willow says it chose CoreWeave for reinforcement-learning infrastructure and forward-deployed support. Liu said that CoreWeave’s infrastructure means Willow does not have to manage what is “under the hood,” leaving the team to focus on evaluation alignment.
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That is Willow’s stated reason for the choice, not a head-to-head comparison of cloud providers. The report does not specify Willow’s CoreWeave configuration, contract terms, pricing, or workload scale, and it does not show that CoreWeave is faster or cheaper for Willow.
Training and inference demand
Liu described Willow’s work as mostly training at present, while saying inference demand is growing faster as people use dictation more. This is his account of the company’s workload, not a published usage dataset. The article gives no figures for requests, users, compute consumption, latency, or the rate of that growth.
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What the report does—and does not—show
The SiliconANGLE account documents Willow’s reported infrastructure choice and product approach, but it is not an independently audited technical case study. It provides no Willow-specific measured accuracy, latency, cost, model size, or before-and-after result. It also does not establish privacy or security properties.
CoreWeave separately said in an October 8, 2025 announcement that its Serverless RL offering achieved nearly 1.4× faster training and 40% lower costs, with no impact on model quality, compared with local H100 GPU environments. Those are CoreWeave’s own benchmark claims about that offering—not Willow results or an independent comparison. Details are in CoreWeave’s announcement.
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The event-reporting context is also relevant: theCUBE was a paid media partner for Fully Connected coverage. SiliconANGLE’s article says neither CoreWeave nor other event sponsors had editorial control.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Willow’s keyboard-free vision is a goal, not a result
Liu said Willow wants users to be able to speak to their computers without needing keyboards within two years. That is a forward-looking product aspiration, not evidence that keyboards will disappear or that Willow Voice already delivers that experience. The report does not identify any particular hardware as necessary or recommended.
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