sllm bets on shared GPU tokens

This is a plausible infra experiment, but the economics are where most “unlimited” plans go to die. If sllm can keep cohorts full, utilization high, and abuse low, it could be a strong alternative to both self-hosting and pay-per-token APIs.

• –The business hinges on capacity smoothing: pooled demand only works if most users are idle at different times.
• –“Unlimited tokens” is a marketing promise that still depends on hidden constraints like throughput, fairness, and possible throttling under load.
• –The privacy claims are good, but they also raise the operator trust bar since users are handing traffic to a shared inference layer.
• –This competes less with ChatGPT-style products and more with cheap self-hosted GPU setups and inference providers.
• –The main risk is not model quality, it’s unit economics: fill rate, churn, and peak demand will decide whether this is clever or expensive.