What Inference Providers Actually Look For | Compute Exchange, Hosted.ai, Tencent Cloud, Yotta Labs

Описание: Recorded live at NeoCloud Summit 2026, Hotel Kabuki, San Francisco.

When inference providers choose a GPU partner, the lowest hourly price rarely tells the whole story. Availability, delivery, reliability, utilization, and the infrastructure around the GPU all shape the real cost of serving tokens, and the cheapest GPU hour doesn't always produce the cheapest inference.

In this panel, Carmen Li, CEO of Silicon Data and Compute Exchange, moderates a conversation with Nilesh Shah of Hosted.ai, Anderson Lin of Tencent Cloud, and Johnny Liu of Yotta Labs on what inference providers actually look for when choosing GPU infrastructure.

Johnny explains why availability and lead time come first in today's market, followed by whether a workload actually fits on a given chip, and only then cost. Anderson shares why delivery and accountability matter so much in long-term contracts, after seeing delivery dates come and go without capacity showing up.

Nilesh makes the case that not all tokens are created equal. He explains why networking, storage, KV cache infrastructure, and CPU compute all need to be part of the picture, because any one of them can quietly eat away at the economics of a low sticker price.

TOPICS COVERED

• What matters most when choosing a GPU or infrastructure provider
• Availability, lead time, and delivery reliability
• Workload feasibility across different chips
• Cost per outcome vs. cost per GPU hour
• Why not all tokens are created equal
• Networking, storage, KV cache, and CPU requirements for inference
• When to choose bare metal vs. inference services
• Security and compliance as drivers of bare metal
• Reserved tokens vs. on-demand consumption
• Team capability and stack optimization
• GPU utilization and overcommit technology
• Utilization vs. availability tradeoffs for enterprise SLAs
• Profitability metrics across the value chain
• Multi-cloud, multi-silicon, and workload portability
• Bare metal vs. token growth in the year ahead

The panel also digs into utilization. Nilesh points out that many training clusters run at only 30 to 35% utilization, and inference often runs even lower. Anderson explains why enterprise SLAs sometimes require leaving headroom for traffic surges, and Johnny argues that business value matters more than raw utilization.

The conversation closes on multi-cloud and multi-silicon flexibility. The panelists explain why portability matters for uptime, capacity, and cost, and why building for it early saves pain later. In a final lightning round, they weigh in on whether bare metal or tokens will grow faster in the coming year.

Watch the full panel for a practical look at how inference providers evaluate GPU partners and the true economics of serving tokens.

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