bud-fcsp – BudEcosystem

What is Bud FCSP?

Modern AI infrastructure has one core problem: GPUs are expensive, and most of them are underutilized.

Bud FCSP (Fixed Capacity Spatial Partition) solves this by enabling secure, high-density GPU sharing across multi-tenant workloads — without vendor lock-in, driver modifications, or application changes.

If you are running AI inference, training, or mixed ML workloads in Kubernetes, FCSP turns every GPU into a flexible, governable, cost-efficient resource pool.

Why FCSP Exists

Most organizations face critical GPU infrastructure challenges

Wasted Resources

Small inference workloads wasting entire GPUs, leading to massive underutilization of expensive hardware.

Team Contention

Teams fighting for GPU access with no safe way to share resources between tenants.

Vendor Lock-in

Vendor-specific solutions that limit hardware choice and create dependency on single providers.

Poor Isolation

Basic time-slicing provides inadequate isolation, causing noisy neighbor problems.

Cost Explosion

Exponentially rising costs as GPU fleets scale to meet growing AI demands.

Scheduling Chaos

No priority-based scheduling or fair resource allocation across teams and projects.

FCSP allows you to split a single physical GPU into multiple secure virtual GPUs

Guaranteed memory limits Controlled compute usage Hard isolation between workloads Real-time usage visibility

What FCSP Delivers

Enterprise-grade GPU virtualization capabilities

True GPU Resource Isolation

Each container receives a virtual GPU with complete isolation and guaranteed resources.

Fixed memory quota (hard enforced)
Defined compute share
Independent execution environment
Protection from noisy neighbors

If one workload spikes, it cannot consume another tenant's GPU memory or compute.

Higher GPU Utilization

Instead of one job per GPU, you can run multiple workloads simultaneously.

Multiple inference services
Mixed inference + fine-tuning jobs
Batch + real-time workloads together
Bursty microservices sharing capacity

2x–4x higher GPU utilization, translating to millions saved annually at scale.

Hardware-Agnostic Virtualization

Unlike proprietary stacks, FCSP works across heterogeneous accelerators.

NVIDIA GPUs
AMD GPUs
Intel accelerators
Gaudi, Ascend NPUs, and more

A unified virtualization layer across mixed hardware environments.

Kubernetes-Native Integration

FCSP integrates directly into your Kubernetes cluster seamlessly.

Device plugins
Scheduler extensions
Mutating admission webhooks
In-container virtualization layer

Your developers keep using CUDA, PyTorch, TensorFlow, vLLM — unchanged.

Software or MIG Mode

Flexible partitioning options based on your hardware and requirements.

Dynamically orchestrate MIG partitions on supported NVIDIA GPUs
Combine hardware isolation with FCSP governance
Advanced software slicing on other GPUs
Mix modes across nodes

Choose the right isolation level for each workload.

Who Benefits from FCSP

AI Platform Teams

If you manage shared AI infrastructure:

Eliminate GPU contention between teams
Enforce quotas automatically
Prevent memory leaks from affecting others
Enable safe multi-tenancy

Move from GPU chaos to GPU governance.

AI SaaS & Inference Providers

If you run LLM APIs or AI services:

Serve multiple customers per GPU
Improve cost per token
Protect SLAs from noisy workloads
Enable priority-based scheduling

Higher density means higher margins.

Enterprises Running Private AI

If you operate AI in regulated or air-gapped environments:

Secure workload isolation
Fine-grained resource control
Vendor-neutral deployment
Support for heterogeneous hardware

Stay compliant while maximizing infrastructure ROI.

Research Labs & Universities

If GPU access is limited:

Share expensive GPUs across projects
Prevent resource monopolization
Enable fair scheduling policies
Maximize research throughput

More experiments, fewer bottlenecks.

Key Capabilities

Fractional GPU Allocation

Define compute percentage and memory limits per container.

Hard Memory Enforcement

Applications cannot exceed assigned GPU memory.

Compute Throttling

Time-shard scheduling prevents GPU hogging while maintaining concurrency.

Priority Scheduling

Pause lower-priority workloads for urgent inference tasks.

Real-Time Monitoring

Observe per-container GPU usage and adjust policies dynamically.

Oversubscription Support

Safely run more workloads than physical GPUs when workloads are bursty.

The Business Value

If you operate 100+ GPUs, even a 20% utilization improvement can justify FCSP immediately. This is infrastructure efficiency that compounds over time.

Reduce GPU fleet size

Delay new hardware purchases

Increase revenue per GPU

Improve SLA predictability

Simplify multi-tenant governance

When Should You Use FCSP?

Use FCSP if:

You want to run multiple AI workloads per GPU
You need strong tenant isolation
You operate Kubernetes-based AI infrastructure
You want to avoid vendor lock-in
You need better GPU cost efficiency
You manage inference-heavy workloads

Do not use FCSP if:

You always dedicate one GPU per workload and cost is irrelevant
You require absolute hardware-level partitioning only (in that case, combine with MIG)

Bud FCSP