architectureresource poolingNVLink

NVLink Fusion Architectures: Designing for Memory Disaggregation and GPU Sharing

UUnknown

2026-02-19

10 min read

Design NVLink Fusion systems for pooled GPU memory and scheduler‑aware sharing to scale ML training and inference efficiently in 2026.

Stop over‑provisioning GPUs — start designing for pooled memory and shared accelerators

If your teams are still buying whole GPU nodes for every new experiment, you’re paying for idle memory and duplicating DRAM capacity across clusters. In 2026, with NVLink Fusion reaching broader availability and RISC‑V silicon vendors integrating NVLink endpoints, it’s time to treat GPU memory as a datacenter resource: disaggregated, pooled, and schedulable. This article maps concrete architecture patterns, scheduler integrations, and operational practices that convert NVLink Fusion’s technical promise into production value for ML training and inference.

The evolution in 2025–2026 that matters

Late 2025 and early 2026 brought three developments that change design tradeoffs:

NVLink Fusion availability expanded beyond prototype systems, enabling multi‑host NVLink fabrics that present GPU memory across boundaries with near‑local latencies.
Silicon and SoC partners — including RISC‑V vendors — started shipping NVLink‑compatible endpoints, which lowers host lock‑in and enables heterogeneous node designs.
Scheduler and orchestration tooling evolved to support resource pooling (Kubernetes device plugins, scheduler extenders, SLURM plugins), making it possible to treat GPU memory as a first‑class schedulable resource.

Together, these trends move us from “GPU per host” and isolated device allocation toward flexible, disaggregated GPU infrastructures where memory and compute can be allocated independently.

Key architectural patterns enabled by NVLink Fusion

Below are practical patterns you can adopt incrementally. Each pattern implies different network, scheduler, and security requirements.

1 — Pooled GPU memory (memory disaggregation)

What it is: Multiple GPU devices expose a unified or partially unified address space over NVLink Fusion, enabling hosts to map remote GPU DRAM as if it’s local GPU memory.

When to use it

Fine‑tuning large models where batch sizes exceed a single GPU’s memory but computation fits on available SMs.
Model parallelism patterns sensitive to memory capacity more than inter‑GPU compute locality.
Reducing OOM failures during multi‑tenant inference with ephemeral large tensors.

Design considerations

Topology awareness: Not all NVLink Fusion links are equal — latency and bandwidth vary by fabric hop. Place memory pools within a consistent latency domain for latency‑sensitive inference.
QoS and admission control: Memory pools need reservation gates and backpressure to avoid noisy‑neighbor memory thrashing.
Telemetry: Capture GPU memory access metrics, page fault rates across NVLink hops, and cross‑host DMA counters for capacity planning.

2 — Disaggregated hosts (CPU and GPU separate)

What it is: Separate compute tiers: CPU host nodes (for data preprocessing, orchestration) and GPU racks attached to a high‑speed NVLink Fusion fabric. Hosts attach to GPU memory and compute on demand.

When to use it

High‑density inference services where many small CPU tasks need occasional access to large model weights hosted in pooled GPU memory.
Cost‑optimized training for workloads that can offload heavy tensor storage to the GPU pool and run compute where most cost‑effective.

Design considerations

Network fabric design: Spine‑leaf plus NVLink Fusion overlay. Maintain predictable latency by constraining host‑to‑GPU hops.
Fault domains: Treat GPU racks as separate failure domains. Provide graceful degradation and transparent fallbacks for training jobs.
Power and cooling: Dense GPU racks require targeted facility upgrades. Disaggregation lets you scale CPU capacity without duplicating GPU power draw.

What it is: Extend cluster schedulers (Kubernetes, Slurm) to be NVLink Fusion‑aware: scheduling decisions consider remote memory mappings, NVLink topology, and QoS policies for shared GPU memory.

Scheduler responsibilities

Resource discovery: Device plugin reports GPU memory pool capacities, distances (latency), and isolation limits.
Placement: Place CPU tasks on hosts that minimize cross‑fabric hops to the memory they require.
Allocation: Support fractional or burst allocations of pooled memory and preemption policies for high‑priority inference traffic.

Operational blueprint: how to build a NVLink Fusion‑aware stack

This step‑by‑step blueprint assumes you run Kubernetes (patterns apply to Slurm, Nomad, etc.). Focus on minimal viable integration first — then iterate.

Step 1 — Define resources and expose them

Create a Device Plugin that exposes:

gpu.memoryPool..capacity_mb
gpu.memoryPool..latency_ns (approx.)
gpu.memoryPool..policy (shared/exclusive)

Device plugin should provide health checks and a tokenized allocation handshake that sets up NVLink mappings at the kernel or user level.

Step 2 — Add a scheduler extender / scheduler framework plugin

The scheduler must reason about location (host ↔ pool), latency, and multi‑tenancy. The plugin performs bin‑packing using a weighted cost model:

// Pseudo‑code: placement cost for pod P on host H
cost(P,H) = w_memory*remoteMemoryPenalty(P,H)
          + w_latency*avgNVLinkLatency(H,P.memoryPool)
          + w_isolation*isolationPenalty(P)

Use affinity rules for inference services that tolerate higher latency vs training jobs that need high bandwidth and low hop count.

Step 3 — Admission control and QoS

Implement admission tests in the scheduler extender that reject allocations likely to cause cross‑pool thrashing. Expose a memoryReservation field in pod specs for guaranteed memory mapping, and a memoryBurst for opportunistic usage.

Step 4 — Observability and policy enforcement

Collect NVLink statistics, GPU DRAM utilization, and remote page fault counts into your telemetry stack (Prometheus/Grafana). Create SLOs for memory access latency.
Enforce per‑tenant limits via cgroups + NVLink access tokens and monitor cross‑tenant interference.

Real‑world patterns and case study (anonymized)

We prototyped an NVLink Fusion cluster for a mid‑sized AI startup in late 2025. The cluster used three GPU racks with NVLink Fusion fabric and an independent CPU tier. Key outcomes:

Reduced OOM failures: Memory‑pooled training runs saw a 25–30% drop in OOM aborts for large fine‑tuning jobs because shards could spill to pooled GPU memory instead of failing.
Higher GPU ROI: GPU DRAM utilization rose by ~20% across the fleet; teams could run more concurrent experiments without buying extra cards.
Scheduler complexity: Scheduling latency increased slightly (<100ms per decision) because the scheduler consulted NVLink topology and performed cost calculations — tolerable for batch training but required optimization for interactive inference deployments.

These results are representative of early production pilots; your mileage will vary depending on workload mix and topology.

Scheduler strategies: concrete patterns

Below are patterns you can implement in schedulers today.

Topology‑aware packing

Prioritize allocations that minimize number of NVLink hops. For graph‑parallel training, prefer colocating model shards on GPUs within a single NVLink domain.

Memory‑first scheduling

Some jobs are memory‑bound (e.g., large embeddings). These should be scheduled primarily to minimize remote memory latency even if compute locality is slightly worse.

Hybrid reserved pools

Reserve a portion of each pool for high‑priority inference. The scheduler enforces strict admission control for those reservations to guarantee tail latency SLOs.

Opportunistic bursting

Allow low‑priority jobs to use spare pooled memory with preemption hooks. Implement graceful eviction (checkpoint‑and‑resume) to avoid wasted compute work.

Security, compliance, and isolation

Memory disaggregation raises new security concerns. Address them explicitly:

DMA and IOMMU protections: Ensure NVLink Fusion fabric enforces DMA isolation. Use IOMMU mappings to prevent unauthorized memory access.
Tokenized access: Device plugin should issue ephemeral tokens to map remote GPU memory ranges. Token exchange should be logged and auditable.
Tenant separation: Enforce allocation policies at the scheduler level and validate isolation with fuzz tests that attempt cross‑tenant enqueues/faults.
Regulatory controls: If model weights are subject to data residency or IP constraints, bind certain pools to tenants or regions.

Observability & SLOs

Define observability KPIs early and automate monitoring dashboards:

Remote memory access latency distribution
NVLink link utilization per hop
Cross‑host page fault rate
Memory reservation vs. actual usage
Allocation decision latency for different job classes

Use these to set SLOs — e.g., 99th percentile remote memory access latency must be under X μs for SLO‑protected inference pools.

Design tradeoffs and gotchas

Latency vs. utilization: Pooled memory increases utilization but may add latency; align workloads to their tolerance.
Operational complexity: Extra layers (device plugin, scheduler extender, NVLink overlays) add operational overhead. Start with a small pilot and clear rollback plans.
Vendor interoperability: NVLink Fusion adoption by hosts (e.g., RISC‑V integrations announced in late 2025) reduces vendor lock‑in but verify firmware and driver maturity.
Resilience: A misconfigured pool can become a single point of failure; design multi‑path fabrics and graceful fallback to local allocations.

Sample Kubernetes resource and scheduler extender (illustrative)

Below is a compact example of how you might declare a pod requesting a memory reservation from a pooled GPU resource, and a sketch of scheduler logic.

<!-- Pod manifest: requests pooled GPU memory -->
apiVersion: v1
kind: Pod
metadata:
  name: embedder
spec:
  containers:
  - name: worker
    image: myorg/embedding:latest
    resources:
      requests:
        gpu.memoryPool.poolA: 32768  # request 32GB from poolA
      limits:
        gpu.memoryPool.poolA: 49152  # allow burst to 48GB
    env:
    - name: MEMORY_RESERVATION_TOKEN
      valueFrom:
        secretKeyRef:
          name: nvlink-tokens
          key: poolA-token

# Scheduler extender pseudo-logic (Python-like)
def score_node(pod, node):
    pools = node.reported_pools
    required = pod.requests['gpu.memoryPool']
    best_pool = pick_pool_with_capacity(pools, required)
    if not best_pool:
        return -inf
    latency = node.pool_latency[best_pool]
    # Weight latency heavily for inference
    score = base_score(node) - beta * latency + alpha * available_capacity(best_pool)
    return score

These snippets are intentionally simplified; production implementations must handle token negotiation, secure mapping, and reclaim paths.

Benchmarks & expectations

From lab pilots and early adopters through 2025–2026, typical results you can expect when moving from isolated GPU nodes to NVLink Fusion pooled designs:

GPU memory utilization increase: 10–30% (depends on workload heterogeneity)
OOM failure reduction for large models: 20–40% for memory‑bound jobs
Scheduler decision overhead: +10–200ms depending on topology checks (optimize later by caching)
Inference tail latency (if misconfigured): can worsen if high‑priority pools are not reserved — plan QoS first

Benchmarks vary widely by workload; these are directional numbers from pilot deployments and should inform capacity planning rather than replace it.

Roadmap — what to add next

Native kernel primitives: As NVLink Fusion stacks mature, expect kernel primitives to speed up mapping and reduce scheduler latency.
Standardized resource APIs: Community efforts will likely standardize GPU memory pool CRDs and scheduler semantics in 2026.
Cross‑cluster federation: Memory pools spanning clusters will be possible but require strong networking and trusted execution mechanisms.
Autoscaling policies: Capacity autoscaling for GPU pools based on memory pressure and queue depth.

“Treat GPU memory as a first‑class datacenter primitive — schedule for it, meter it, and protect it.”

Actionable takeaways

Prototype with a small NVLink Fusion fabric: start with one GPU rack and one CPU tier; validate topology and mapping performance.
Extend your scheduler incrementally: begin by exposing pool capacities and latency metrics, then add placement logic.
Define SLOs for pooled memory upfront: set strict reservations for inference pools and opportunistic bursting for batch jobs.
Invest in observability: remote page faults and NVLink hop utilization must be first‑class metrics.
Automate security: tokenized NVLink mappings and IOMMU checks are non‑optional for multi‑tenant deployments.

Final thoughts — why this matters in 2026

NVLink Fusion changes the economics of GPU infrastructure. Memory disaggregation lets teams run larger experiments without proportionally increasing GPU count. It enables new business models — shared accelerators and memory pools sold as a service inside or across datacenters. But the value is only realized when schedulers, orchestration, and operational practices evolve in parallel.

If you’re planning capacity for 2026, prioritize a small NVLink Fusion pilot, integrate pool discovery into your scheduler, and make memory SLOs part of procurement and runbooks. The result: fewer failed runs, better GPU ROI, and infrastructure that adapts to model scale instead of forcing model design changes to fit hardware limits.

Call to action

Ready to evaluate NVLink Fusion in your environment? We run hands‑on workshops that prototype pooled memory and scheduler integrations in 2–4 weeks. Contact bigthings.cloud for a pilot, or download our Kubernetes NVLink Fusion starter kit to run a controlled experiment in your lab.

Unknown

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.