Turn every GPU into a flexible, governable resource pool.
GPUs are expensive and most of them sit underused. Bud FCSP - Fixed Capacity Spatial Partition - splits a single physical GPU into multiple secure virtual GPUs with real isolation, so small inference jobs stop consuming whole cards.
Six problems that show up at fleet scale.
Wasted resources
Small inference workloads occupying entire GPUs, leaving expensive hardware mostly idle.
Team contention
Teams competing for GPU access with no safe way to share between tenants.
Vendor lock-in
Vendor-specific solutions that limit hardware choice and create single-supplier dependency.
Poor isolation
Basic time-slicing gives inadequate isolation, so noisy neighbours degrade everyone.
Cost explosion
Costs rising faster than capability as the fleet grows to meet demand.
Scheduling chaos
No priority-based scheduling or fair allocation across teams and projects.
Five capabilities that change the economics.
Where the speed comes from.
Lock-free shared memory and intelligent stream classification, measured against the alternatives.
Lock-free shared memory
Atomic operations allow concurrent access without mutex locks, removing the context-creation bottleneck.
Smart stream classification
NCCL bypass for distributed training, with kernel categorisation for attention and FFN workloads.
Zero-copy memory access
Direct memory operations with no intermediate copies.
| Operation | Bud FCSP | NVIDIA vGPU | HAMi |
|---|---|---|---|
| Context creation | 78µs | 84ms | ~1ms |
| Memory tracking | 0.3µs | ~100µs | 1.1ms |
| Hardware support | Heterogeneous accelerators | A100 / H100 only | Limited |
| Scheduling overhead | ~40ns | Higher | Higher |
Four modules.
Memory tracker
Lock-free shared memory using atomic operations for concurrent context access.
Kernel rate limiter
Compute throttling at microsecond granularity.
Stream classifier
Workload categorisation for optimised GPU scheduling.
Process manager
Container-aware process isolation and lifecycle management.
From zero overhead to strict quota.
Idle GPU resources flow automatically to active tenants under work-conserving scheduling, so utilisation does not depend on someone rebalancing by hand.
What you can set per container.
Fractional GPU allocation
Define compute percentage and memory limits per container.
Hard memory enforcement
Applications cannot exceed their assigned GPU memory.
Compute throttling
Time-shard scheduling prevents hogging while keeping concurrency.
Priority scheduling
Pause lower-priority work for urgent inference.
Real-time monitoring
Per-container GPU usage, with policies adjustable live.
Oversubscription
Safely run more workloads than physical GPUs when demand is bursty.
They are not mutually exclusive.
FCSP can orchestrate MIG partitions on supported NVIDIA GPUs, combining hardware isolation with software governance. Where hardware-level partitioning is mandated, use MIG underneath; where you need heterogeneous hardware, finer granularity or oversubscription, FCSP covers what MIG cannot.
The business value
At 100+ GPUs, a 20% utilisation improvement usually justifies the layer on its own. It reduces fleet size, delays new hardware purchases, raises revenue per GPU, improves SLA predictability, and makes multi-tenant governance tractable.
Operating 100+ GPUs?
At that fleet size even a 20% utilisation improvement usually pays for the layer. We can model it against your workload mix.