Subvolts take Inference is the new AI frontier; learn how specialized infrastructure powers enterprise AI at scale. Jan 17, 2026 - Developers
- Learn about specialized hardware and serverless platforms for efficient AI model deployment and operation.
- Business
- Understand why inference is the critical bottleneck for AI in production and how new infrastructure addresses enterprise needs.
This episode features a product leader discussing a key technical shift in AI deployment, relevant to enterprise adoption.
#314 Nick Pandher: How Inference-First Infrastructure Is Powering the Next Wave of AI
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From Subvolts
Summary and analysis by Subvolts editorial, AI-assisted.
Craig Smith interviews Nick Pandher on Eye on AI about the shift from AI model training to inference at scale. Pandher, VP of Product at Cirrascale, explains how enterprises now prioritize performance, latency, and cost efficiency. The discussion covers inference-first infrastructure, the limitations of hyperscalers, the rise of neoclouds, and how agentic AI drives constant inference needs. It highlights inference-optimized hardware and serverless platforms as key to future enterprise AI deployment.
AI is moving from just building smart computer programs to actually using them all the time. This episode explains why making AI work smoothly and affordably for businesses is the next big challenge, like building better roads for cars to drive on.
This episode explains why inference, not training, is the current frontier for AI in production. It's for business leaders and IT professionals deploying AI, detailing how specialized infrastructure and new cloud models address enterprise needs for performance and security. This connects to frontier labs' efforts to scale AI services, as efficient inference is crucial for widespread adoption of models like those from OpenAI, Google Gemini, and Anthropic.
- HostCraig S. Smith
- GuestNick PandherVP of Product, Cirrascale
- Guest: Nick Pandher, VP of Product at Cirrascale
- Show: Eye on AI
- Episode Title: How Inference-First Infrastructure Is Powering the Next Wave of AI
- Length: 56 minutes
- Key Topic: Shift from AI model training to inference at scale
- Emerging Concept: Neoclouds
Inference is now the biggest challenge in enterprise AI. In this episode of Eye on AI, Craig Smith speaks with Nick Pandher, VP of Product at Cirrascale, about why AI is shifting from model training to inference at scale. As AI moves into production, enterprises are prioritizing performance, latency, reliability, and cost efficiency over raw compute. The conversation covers the rise of inference-first infrastructure, the limits of hyperscalers, the emergence of neoclouds, and how agentic AI is driving always-on inference workloads. Nick also explains how inference-optimized hardware and serverless AI platforms are shaping the future of enterprise AI deployment.
If you are deploying AI in production, this episode explains why inference is the real frontier.
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(00:00) Preview
(00:50) Introduction to Cirrascale and AI inference
(03:04) What makes Cirrascale a neocloud
(04:42) Why AI shifted from training to inference
(06:58) Private inference and enterprise security needs
(08:13) Hyperscalers vs neoclouds for AI workloads
(10:22) Performance metrics that matter in inference
(13:29) Hardware choices and inference accelerators
(20:04) Real enterprise AI use cases and automation
(23:59) Hybrid AI, regulated industries, and compliance
(26:43) Proof of value before AI pilots
(31:18) White-glove AI infrastructure vs self-serve cloud
(33:32) Qualcomm partnership and inference-first AI
(41:52) Edge-to-cloud inference and agentic workflows
(49:20) Why AI pilots fail and how enterprises succeed
- Inference
- Nick Pandher
- Using a trained AI model to make predictions or generate outputs.
- Hyperscalers
- Nick Pandher
- Large cloud providers like Amazon AWS, Microsoft Azure, and Google Cloud.
- Neoclouds
- Nick Pandher
- Newer cloud providers specializing in AI workloads, offering alternatives to hyperscalers.
- Agentic AI
- Nick Pandher
- AI systems that can autonomously perform tasks and make decisions.
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WithAndrii Hrytseniuk
Questions people ask
- Why is AI inference the biggest challenge now?
- As AI moves into production, enterprises prioritize performance, latency, reliability, and cost efficiency, making inference at scale the main hurdle.
- What is inference-first infrastructure?
- It is infrastructure designed specifically to optimize the performance and cost of running AI models after they have been trained, focusing on speed and efficiency for live applications.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to Craig S. Smith.