How Dynamic GPU Placement Enables Lower Prices

A lot of platforms claim "sharing", but what they usually mean is one of two things that create real problems for users.

The Typical Approaches (And Their Problems)

Hard Slicing (MIG, vGPU)

Technologies like MIG or vGPU carve a GPU into fixed partitions. This is simple, but inflexible. If your workload needs more VRAM but less compute, or vice versa, you're stuck paying for a shape that doesn't really fit. And you're still only getting a fraction of the GPU's capability.

Oversubscription

Multiple jobs are thrown onto the same GPU and hope for the best. This can look cheap on paper, but it creates unpredictable latency, throttling, and failure modes that are unacceptable for anything beyond experimentation.

packet.ai: Dynamic Placement

We take a fundamentally different approach. Our scheduler is built around a simple observation: modern AI workloads rarely consume all aspects of a GPU at the same time.

By understanding how different workloads stress the GPU, we can co-locate jobs that complement each other rather than collide. For example, a memory-heavy inference workload can run alongside a compute-heavy task without either seeing meaningful slowdown.

How It Works

1. Real-time resource tracking — We monitor GPU usage across multiple dimensions, not just "occupied or not"
2. Intelligent placement — Workloads are placed based on their actual resource profiles
3. Automatic rebalancing — When contention would impact performance, workloads are moved or queued automatically
4. Predictable execution — The system prioritises performance over density

The Result

From your perspective, it feels like running on a well-behaved, dedicated GPU—not a noisy shared environment. You get full performance characteristics while we achieve higher infrastructure utilisation.

Why This Means Better Pricing

Once you can safely drive higher utilisation across your GPU fleet, the economics change completely. The same physical GPU can serve more customers without degrading anyone's experience.

Traditional GPU pricing assumes low average utilisation, so prices have to cover idle hardware. Our dynamic placement technology removes much of that waste. The cost of each GPU is spread across more customers, which means lower hourly rates for everyone.

That's why packet.ai offers rates starting at $0.66/hour—without compromising on performance.