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Fixed Capacity Spatial Partition

Bud FCSP

GPU Virtualization for High-Density AI Infrastructure. Turn every GPU into a flexible, governable, cost-efficient resource pool.

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.

GPU Infrastructure

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

1

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.

2

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.

3

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.

4

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.

5

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.

Performance That Matters

FCSP delivers industry-leading performance through lock-free shared memory design and intelligent stream classification

1000×
Faster Context Creation
FCSP: 78μs vs vGPU: 84ms
3600×
Faster Memory Enforcement
FCSP: 0.3μs vs HAMi: 1.1ms
3×
Better Isolation
Complete memory and compute isolation between tenants
$14M
Annual Savings
Projected savings for 1000-GPU enterprise deployment

Lock-Free Shared Memory

Atomic operations enable concurrent access without mutex locks, eliminating context creation bottlenecks

Smart Stream Classification

NCCL bypass for distributed training, intelligent kernel categorization for attention and FFN workloads

Zero-Copy Memory Access

Direct memory operations without intermediate copies, maximizing throughput efficiency

Technical Architecture

Four core modules work together to deliver comprehensive GPU resource isolation

01

Memory Tracker

Lock-free shared memory design using atomic operations for concurrent context access.

  • Efficient memory accounting without mutex locks
  • 78μs context creation (vs 84ms for vGPU)
  • Real-time memory usage tracking per container
  • Automatic memory limit enforcement
02

Kernel Rate Limiter

Precise compute throttling with microsecond-level granularity.

  • Time-based SM (Streaming Multiprocessor) allocation
  • Fair scheduling between concurrent workloads
  • Burst handling for latency-sensitive tasks
  • Configurable throttling policies per tenant
03

Stream Classifier

Intelligent workload categorization for optimized GPU scheduling.

  • NCCL bypass for distributed training efficiency
  • Attention kernel prioritization
  • FFN (Feed-Forward Network) batch optimization
  • Dynamic priority adjustment based on workload type
04

Process Manager

Container-aware process isolation and lifecycle management.

  • Per-container GPU process tracking
  • Automatic resource cleanup on container exit
  • Process priority enforcement
  • Multi-tenant security boundaries
How FCSP Integrates
Application CUDA/PyTorch/TensorFlow
→
FCSP Layer Interception & Governance
→
GPU Driver NVIDIA/AMD/Intel
→
Physical GPU Hardware Resources

FCSP vs. Alternatives

See how FCSP compares to other GPU virtualization approaches

Feature FCSP NVIDIA MIG NVIDIA vGPU Time-Slicing
Context Creation 78μs N/A (static) 84ms ~1ms
Memory Enforcement 0.3μs Hardware ~100μs 1.1ms
Memory Isolation ✓ Hard Limit ✓ Hardware ✓ Software ✗ None
Compute Isolation ✓ Rate Limited ✓ SM Partition ◐ Partial ✗ None
GPU Compatibility All Vendors A100/H100 only NVIDIA only NVIDIA only
Dynamic Partitioning ✓ ✗ Requires restart ✓ ✓
Multi-Tenant Support ✓ ◐ Limited ✓ ✗
Kubernetes Native ✓ ◐ ◐ ✓
License Cost Included Free Per-GPU License Free
✓ Best-in-class performance with microsecond-level operations
✓ Hardware-agnostic: works across NVIDIA, AMD, Intel, and custom accelerators
✓ No vendor lock-in or per-GPU licensing fees

Flexible Isolation Modes

Configure resource enforcement to match your workload requirements — from zero overhead to strict quotas

None

~40ns overhead

Zero enforcement mode for trusted single-tenant environments or performance benchmarking.

  • Minimal interception overhead
  • Full GPU access for workload
  • Ideal for development testing
Recommended

Balanced

Default mode

Optimal balance between isolation and resource sharing for multi-tenant production environments.

Guaranteed Floor 20%
Shared Pool 40%
Burst Capacity 40%
  • Work-conserving scheduling
  • Up to 143.9% efficiency vs static allocation
  • Automatic resource rebalancing

Strict

MIG-equivalent

Hard quota enforcement matching MIG behavior for compliance-sensitive workloads.

  • No resource sharing between tenants
  • Guaranteed SLA boundaries
  • Regulatory compliance ready

Adaptive

Smart adjustment

Dynamically adjusts isolation level based on real-time contention and workload patterns.

  • Automatic contention detection
  • 0.996 fairness index (MIG: 1.0)
  • 4.66% noisy neighbor impact

Work-Conserving Scheduling

Idle GPU resources automatically flow to active tenants, maximizing utilization without manual effort. When three tenants are idle and one is active, FCSP sustains ~100% utilization while MIG wastes 67% of resources.

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

FCSP vs MIG: Which Should You Choose?

Choose FCSP When:

  • No MIG-capable hardware available (V100, T4, RTX series, AMD, Intel)
  • Dynamic workload patterns requiring runtime adjustment
  • Variable batch sizes or bursty memory allocation (LLM inference)
  • Mixed GPU clusters with heterogeneous architectures
  • Development environments prioritizing flexibility
  • You need arbitrary partitioning (e.g., 25%/35%/40% splits)
  • Work-conserving scheduling to maximize utilization
  • Avoiding per-GPU licensing costs
Supported Hardware: All CUDA-capable GPUs (Compute Capability 3.0+), AMD GPUs, Intel accelerators

Choose MIG When:

  • Maximum hardware-level isolation is mandatory
  • Regulatory compliance explicitly requires hardware partitioning (HIPAA, PCI-DSS)
  • Financial trading or medical imaging workloads
  • Guaranteed, hard QoS requirements with exact resource guarantees
  • Predictable, always-on static workloads
MIG Limitations: Only A100/A30/H100/H200 GPUs, static partitions require restart, limited profiles (e.g., 1g.10gb, 2g.20gb, 3g.40gb on A100)

Best of Both Worlds

FCSP can orchestrate MIG partitions on supported NVIDIA GPUs, combining hardware isolation with software governance. Use MIG for hardware boundaries, FCSP for dynamic management and monitoring.

Ready to Unlock Higher GPU Efficiency?

If you are scaling AI infrastructure and GPU costs are rising, the real question is not whether you need virtualization.

"The question is whether you want to control your GPU fleet — or let it control your budget."
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