SambaNova Surges in Asia as Firms Abandon Nvidia for Cost and Efficiency Gains

Asia’s AI chip market is undergoing a seismic shift. In 2025, a growing number of firms are abandoning Nvidia’s dominant GPU solutions for SambaNova’s reconfigurable dataflow architecture, driven by a pursuit of cost efficiency, power savings, and superior performance in inference workloads. This trend is reshaping the competitive landscape, with SambaNova’s rise signaling a broader industry pivot toward specialized hardware optimized for the application phase of AI.

The Cost Efficiency Catalyst

The move toward SambaNova stems from its ability to slash infrastructure costs while maintaining—or even surpassing—Nvidia’s performance. For instance, deploying the DeepSeek-R1 671B large language model (LLM), SambaNova’s SN40L RDU chips achieve 198 tokens per second per user using just 16 chips, whereas Nvidia’s solution requires 40 racks of 320 GPUs. This reduction collapses hardware costs by an estimated 96%, according to SambaNova CEO Rodrigo Liang.

The savings extend beyond hardware. SambaNova’s architecture supports open-source LLMs like DeepSeek-R1 natively, enabling firms to fine-tune models at 5% of the cost of proprietary training. This aligns with Asia’s cost-conscious tech ecosystem, where startups and enterprises seek affordable AI solutions without compromising performance.

Power Efficiency: A Regional Imperative

Asia’s energy challenges have amplified the appeal of SambaNova’s low-power design. Nvidia’s H100 GPUs consume 140–150 kW per rack, demanding costly upgrades to data center cooling and electrical systems. In contrast, SambaNova’s racks operate at just 10 kW, comparable to standard CPU servers.

This efficiency is critical in regions like Japan and South Korea, where energy costs are high, and in markets like India, where grid reliability remains a concern. SambaNova’s power profile also aligns with Asia’s sustainability goals, reducing carbon footprints in a region projected to account for 40% of global data center energy consumption by 2025.

The Inference Workload Revolution

The industry’s shift from training to inference is central to SambaNova’s rise. Liang argues that 90% of future AI computing will involve inference, a phase where Nvidia’s GPUs face limitations due to memory bandwidth constraints. SambaNova’s architecture, designed for batch=1 inference, delivers 1084 tokens/second for Llama3-8B—five times faster than Nvidia’s H100.

For Asian businesses deploying AI in real-time applications—such as customer service chatbots or document generation—SambaNova’s low-latency performance is a game-changer. Its ability to host multiple model checkpoints on a single platform also reduces hardware sprawl, addressing scalability issues for firms fine-tuning dozens of variants.

Regional Strategy and Partnerships

SambaNova is doubling down on Asia with localized “token factories” in India, designed to support regional data center operators and comply with data sovereignty laws. The company’s APAC VP, Toshinori Kujiraoka, notes that Asian demand for AI solutions that avoid U.S. export restrictions is surging.

The firm’s $5.1 billion valuation and partnerships with Saudi Aramco and Accenture bolster its credibility. Meanwhile, its SNCloud API provides Asian firms with a low-barrier entry point, enabling rapid prototyping before full-scale adoption.

Market Dynamics and Risks

While SambaNova’s momentum is clear, risks persist. Nvidia retains a 90% market share and continues innovating with chips like the H100. However, SambaNova’s focus on inference—a segment expected to grow at a 22% CAGR through 2027—positions it to capitalize on under-served niches.

The global AI chipset market, valued at $47.77 billion by 2025, is ripe for disruption. SambaNova’s $1.14 billion in 2021 funding and technical advantages suggest it could carve out a significant share in Asia, where China alone plans to invest $35.57 billion in AI by 2025.

Conclusion: The New AI Chip Equation

SambaNova’s rise in Asia underscores a paradigm shift in AI infrastructure. By addressing cost, power, and performance pain points ignored by legacy players, the firm is redefining what it means to scale AI. With Asia’s AI chipset market growing at breakneck speed and firms demanding sustainable, scalable solutions, SambaNova’s reconfigurable architecture is not just an alternative—it’s a blueprint for the future.

Investors should watch closely as SambaNova capitalizes on Asia’s hunger for innovation. With its technical edge and strategic regional focus, the company could emerge as a leader in an industry where inference, not training, will soon reign supreme. The question isn’t whether Asia will adopt SambaNova—it’s how quickly the rest of the world will follow.