2024

Edge

Cloud

<p><a href="https://bitcoinworld.co.in" rel="noopener noreferrer nofollow">BitcoinWorld</a><br> <img src="https://bitcoinworld.co.in/wp-content/uploads/quadric-on-device-ai-growth.jpg"><br> <a href="https://bitcoinworld.co.in/quadric-on-device-ai-inference-growth/" rel="noopener noreferrer nofollow">On-Device AI Surge: Quadric’s Strategic Pivot from Cloud to Edge Inference Pays Off Spectacularly</a></p>
<p><strong>San Francisco, March 2025</strong> – A profound architectural shift is reshaping the artificial intelligence landscape, moving critical processing from centralized cloud data centers to the devices in our hands, cars, and offices. Consequently, this transition is creating significant winners, with chip-IP startup Quadric emerging as a notable beneficiary. The company’s strategic focus on powering on-device AI inference is now delivering substantial financial returns, evidenced by a projected licensing revenue surge to between $15 million and $20 million for 2025.</p>
<h2 id="quadric-capitalizes-on-the-on-device-ai-inference-revolution">Quadric Capitalizes on the On-Device AI Inference Revolution</h2>
<p>The era of sending every AI query to a distant cloud server is rapidly evolving. Companies and governments globally are now aggressively pursuing tools for local AI execution. This strategic move aims to dramatically reduce cloud infrastructure costs and build sovereign technological capability. Quadric, founded by veterans of the early bitcoin mining firm 21E6, is positioning its technology at the heart of this shift. The startup licenses programmable AI processor intellectual property (IP), essentially providing a blueprint that customers embed into their own silicon designs.</p>
<p>CEO Veerbhan Kheterpal explained the core market dynamic in an exclusive interview. “The widespread adoption of transformer-based models in 2023 pushed inference into ‘everything,'” he stated. This created a sharp business inflection over the past 18 months. More enterprises now seek to run AI locally rather than rely entirely on cloud-based services. For instance, real-time functions in automotive driver assistance systems cannot tolerate latency. Therefore, on-device processing becomes not just economical but essential.</p>
<h3 id="the-financial-and-strategic-payoff">The Financial and Strategic Payoff</h3>
<p>Quadric’s financial trajectory underscores the market’s validation. The company’s licensing revenue is expected to leap from approximately $4 million in 2024 to between $15 million and $20 million in 2025. Furthermore, it is targeting up to $35 million this year as it builds a royalty-driven business model. This growth has significantly buoyed the company’s valuation. Its post-money valuation now sits between $270 million and $300 million, a substantial increase from around $100 million during its 2022 Series B funding round.</p>
<p>Investors are clearly taking note of this momentum. Quadric recently announced a $30 million Series C funding round led by the ACCELERATE Fund, managed by BEENEXT Capital Management. This investment brings its total funding to $72 million. Kheterpal connected this investor interest directly to the broader industry trend. “The raise comes as investors and chipmakers look for ways to push more AI workloads from centralized cloud infrastructure onto devices and local servers,” he told Bitcoin World.</p>
<h2 id="expanding-from-automotive-into-a-broader-ecosystem">Expanding from Automotive into a Broader Ecosystem</h2>
<p>Quadric’s journey began in the automotive sector, a natural early adopter for on-device AI. Here, low-latency inference powers critical real-time functions like advanced driver-assistance systems (ADAS). However, the company’s vision and market have expanded decisively. Today, its technology targets a diverse portfolio including AI-powered laptops, industrial devices, and printers.</p>
<p>The startup’s customer base now spans major industry players. It includes Kyocera and Japan’s automotive supplier Denso, which builds chips for Toyota vehicles. Kheterpal confirmed that the first commercial products based on Quadric’s IP are slated to ship this year, beginning with laptops. This expansion represents a deliberate scaling strategy. The company is moving from a niche automotive focus to addressing the ubiquitous demand for edge AI processing.</p>
<p><strong>Key Advantages of Quadric’s Approach:</strong></p>
<ul>
 <li><strong>Programmability:</strong> Unlike fixed-function AI accelerators, Quadric’s IP is programmable. This allows customers to support new AI models through software updates instead of costly hardware redesigns.</li>
 <li><strong>Chip-Agnostic Design:</strong> The technology is not tied to a specific silicon process. Customers can integrate the IP into their preferred manufacturing node.</li>
 <li><strong>Full Stack Solution:</strong> Quadric provides not just the processor blueprint but also a complete software stack and toolchain to run models for vision, voice, and other tasks on-device.</li>
</ul>
<h3 id="the-rising-tide-of-sovereign-ai-strategies">The Rising Tide of Sovereign AI Strategies</h3>
<p>Beyond commercial applications, a powerful geopolitical and economic driver is fueling Quadric’s growth: the global push for sovereign AI. Nations are increasingly seeking to reduce their strategic dependence on U.S.-based cloud infrastructure for critical AI capabilities. This involves building domestic expertise across the compute, model, and data spectrum. Kheterpal noted that Quadric is actively exploring opportunities in markets like India and Malaysia, where this sentiment is particularly strong.</p>
<p>The company counts Moglix CEO Rahul Garg as a strategic investor, specifically to help shape its “sovereign” approach for the Indian market. This strategic direction aligns with analysis from major consulting firms. For example, EY highlighted in a November 2024 report that the sovereign AI approach has gained significant traction. Policymakers and industry groups are now pushing for domestic AI capabilities rather than relying entirely on foreign infrastructure.</p>
<h2 id="navigating-the-hardware-vs-software-evolution-challenge">Navigating the Hardware vs. Software Evolution Challenge</h2>
<p>A central challenge in the AI semiconductor industry is the mismatch between development cycles. AI model architectures, like the shift from convolutional neural networks (CNNs) to transformers, can evolve in months. In contrast, designing and manufacturing a new chip typically requires multiple years. Kheterpal identified this disparity as a critical pain point for customers. They need processor IP that can keep pace through software updates, avoiding expensive and time-consuming silicon redesigns with every architectural shift.</p>
<p>Quadric positions its programmable solution as a direct answer to this problem. “We were looking to build a similar CUDA-like or programmable infrastructure for on-device AI,” Kheterpal said, drawing a parallel to Nvidia’s dominant data-center software ecosystem. However, unlike Nvidia or Qualcomm, which integrate their AI technology into their own proprietary chips, Quadric remains an IP licensor. This model aims to avoid locking customers into a specific vendor’s silicon roadmap.</p>
<p><strong>Comparison of AI Semiconductor Approaches:</strong></p>
<table>
 <thead>
  <tr>
   <th><strong>Company</strong></th>
   <th><strong>Business Model</strong></th>
   <th><strong>Key Differentiator</strong></th>
   <th><strong>Target Market</strong></th>
  </tr>
 </thead>
 <tbody>
  <tr>
   <td><strong>Quadric</strong></td>
   <td>Licenses programmable AI processor IP</td>
   <td>Software-updatable, avoids vendor lock-in</td>
   <td>Embedded devices, laptops, automotive</td>
  </tr>
  <tr>
   <td><strong>Nvidia</strong></td>
   <td>Sells complete GPU chips and systems</td>
   <td>Dominant CUDA software ecosystem for data centers</td>
   <td>Cloud data centers, high-performance computing</td>
  </tr>
  <tr>
   <td><strong>Qualcomm</strong></td>
   <td>Sells complete SoCs with integrated AI</td>
   <td>Strong presence in mobile and connected devices</td>
   <td>Smartphones, laptops, XR headsets</td>
  </tr>
  <tr>
   <td><strong>Synopsys/Cadence</strong></td>
   <td>Sells fixed-function NPU IP blocks</td>
   <td>Integrated into broader chip design toolflows</td>
   <td>Broad semiconductor industry</td>
  </tr>
 </tbody>
</table>
<h3 id="the-distributed-ai-infrastructure-imperative">The Distributed AI Infrastructure Imperative</h3>
<p>The economic rationale for distributed, on-device inference is becoming increasingly compelling. The rising cost of operating massive, centralized AI cloud infrastructure is a burden for many enterprises. Additionally, numerous countries lack the resources or geographic suitability to build hyperscale data centers. This reality prompts greater interest in setups where AI inference runs locally—on laptops, smartphones, or small on-premise servers within office buildings.</p>
<p>The World Economic Forum recently highlighted this architectural shift. It noted the movement of AI inference closer to end-users and away from purely centralized models. This trend reduces latency, enhances data privacy, and can lower operational expenses. For a startup like Quadric, this macro shift represents a vast and growing total addressable market. The company now employs nearly 70 people worldwide, with teams in San Francisco and Pune, India, to service this global opportunity.</p>
<h2 id="conclusion-a-promising-trajectory-with-execution-ahead">Conclusion: A Promising Trajectory with Execution Ahead</h2>
<p>Quadric’s rising revenue and valuation clearly demonstrate that the market for on-device AI inference is not just theoretical—it is generating real economic value today. The company has successfully positioned itself at the intersection of several powerful trends: the need for cost-effective AI, the demand for technological sovereignty, and the requirement for hardware that can adapt to rapidly evolving software models. Its programmable, licensable IP model offers a distinct alternative to both merchant chip vendors and traditional IP block suppliers.</p>
<p>Nevertheless, the company acknowledges it is still in the early phases of its buildout. While it has secured several key design wins and signed customers, its long-term success hinges on converting these licensing agreements into high-volume product shipments and the recurring royalty streams they generate. The strategic pivot from cloud to on-device AI inference is undeniably underway. For Quadric, riding this wave has already begun to pay off spectacularly, setting the stage for the next chapter in the distributed intelligence era.</p>
<h2 id="faqs">FAQs</h2>
<p><strong>Q1: What is on-device AI inference, and why is it important?</strong><br>On-device AI inference refers to running trained artificial intelligence models directly on a local device—like a laptop, smartphone, or car computer—instead of sending data to a remote cloud server for processing. This approach is crucial for reducing latency, lowering cloud costs, enhancing data privacy, and enabling functionality in areas with poor connectivity.</p>
<p><strong>Q2: How does Quadric’s business model differ from companies like Nvidia or Qualcomm?</strong><br>Quadric does not manufacture or sell physical chips. Instead, it licenses the intellectual property (IP) design for a programmable AI processor. Customers integrate this “blueprint” into their own custom silicon designs. This contrasts with Nvidia (sells complete GPU hardware) and Qualcomm (sells complete system-on-chip packages), offering clients more flexibility and avoiding vendor lock-in.</p>
<p><strong>Q3: What is “sovereign AI,” and how does it relate to Quadric’s strategy?</strong><br>Sovereign AI is a national or organizational strategy to develop and control domestic AI capabilities—including compute infrastructure, data, and models—to reduce dependence on foreign technology providers. Quadric is exploring this market by offering its IP to companies and governments in regions like India and Malaysia, helping them build local AI hardware expertise.</p>
<p><strong>Q4: What are the main challenges of designing hardware for AI?</strong><br>The primary challenge is the rapid evolution of AI model architectures (e.g., from CNNs to Transformers), which can happen in months, while hardware design and manufacturing cycles typically span years. Quadric addresses this by offering programmable IP that can be updated via software to support new models without a full chip redesign.</p>
<p><strong>Q5: What markets is Quadric targeting beyond automotive?</strong><br>While Quadric started in automotive for applications like driver assistance, it has significantly expanded. Its technology now targets AI-powered laptops, industrial IoT devices, printers, and other edge computing applications where local, efficient AI processing provides a competitive advantage.</p>
<p>This post <a href="https://bitcoinworld.co.in/quadric-on-device-ai-inference-growth/" rel="noopener noreferrer nofollow">On-Device AI Surge: Quadric’s Strategic Pivot from Cloud to Edge Inference Pays Off Spectacularly</a> first appeared on <a href="https://bitcoinworld.co.in" rel="noopener noreferrer nofollow">BitcoinWorld</a>.</p>

On-Device AI Surge: Quadric’s Strategic Pivot from Cloud to Edge Inference Pays Off Spectacularly

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