GPU pricing assumes 30% utilisation — you pay for the idle 70%. Here’s how dynamic placement achieves 5× better utilisation and why that drives packet.ai’s pricing.
Dynamic GPU placement on packet.ai schedules workloads based on real-time VRAM and compute usage — not static allocation. The result is GPU infrastructure that runs efficiently and charges you for what you actually use, not worst-case peak capacity.
Key takeaways
Most GPU cloud pricing is built on a simple assumption: customers need GPUs statically allocated to them. You rent a GPU, you have it 100%, and you pay whether you use it or not. That’s fine for some workloads. But it means the GPU economy runs at ~30% efficiency by default.
Hard slicing (MIG, vGPU) carves a GPU into fixed partitions — for example, 7 equal slices of an A100. Simple and isolated, but completely inflexible. If your workload needs more VRAM but less compute than the slice provides, you’re stuck paying for a shape that doesn’t fit your actual profile.
Oversubscription throws multiple jobs onto the same GPU and hopes the usage patterns don’t collide. It can look cheap on paper — until it doesn’t work. CUDA kernel conflicts, memory pressure spikes, and unpredictable latency make this unsuitable for anything touching production.
⚠ Why oversubscription fails in practice
Oversubscription assumes usage patterns are predictable. LLM inference is bursty — a single long context request can spike memory demand by 10× in milliseconds. Without intelligent scheduling, one job’s spike causes others to OOM or throttle.
hosted·ai’s scheduler — the layer packet.ai runs on — is built on a single observation: modern AI workloads rarely consume all aspects of a GPU simultaneously. A memory-heavy inference workload has different compute and bandwidth characteristics than a training job. Placed correctly, they can coexist without interfering.
Real-time resource tracking
The scheduler monitors GPU usage across multiple dimensions simultaneously — VRAM usage, SM activity, memory bandwidth, compute throughput — not just the binary occupied/not flag that traditional allocation uses.
Intelligent workload placement
When a new workload arrives, it’s profiled and placed on the GPU where its resource profile creates the least contention with existing workloads. Memory-heavy inference alongside compute-heavy batch training, for example.
Automatic rebalancing
If actual usage patterns shift and contention would impact performance, workloads are automatically moved or queued. Performance is prioritised over density — the system never degrades a workload to pack more jobs in.
Predictable execution
From your perspective, it feels like running on a well-behaved dedicated GPU. Full VRAM allocation, full compute, no interference. The efficiency happens at the infrastructure layer — invisible to your workload.
Traditional GPU pricing is built on the assumption that average utilisation is ~30%. That means for every dollar a customer pays, 70 cents is wasted idle hardware. The provider bakes that waste into the price.
When hosted·ai’s dynamic placement drives utilisation up to 5× higher, the same physical GPU can serve more customers without degrading anyone’s experience. The economics change completely. packet.ai H100 from $0.65/hr, H200 from $2.25/hr, B200 from $3.75/hr — browse available clusters for current pricing — are made possible by infrastructure that doesn’t waste 70% of what it provisions.
Last reviewed: 10 June 2026. Browse GPU clusters on packet.ai →
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