H100 Local Cache Design: Capacity and Data Placement

H100 local cache design uses storage attached to or near GPU servers to keep reusable datasets, model weights, checkpoints, containers, or temporary artifacts closer to computation. The cache complements a durable shared data layer; it should not silently become the only copy of important data. Its value depends on workload reuse, miss cost, placement, lifecycle, and the ability to recover from node loss.

There is no universal cache capacity per H100 GPU. Training, fine-tuning, inference, data preparation, and checkpoint workflows have different access patterns. A defensible design starts with a measured working set and explicit policy, then validates whether local storage reduces the bottleneck without creating stale data, stranded capacity, or operational complexity.

What local cache changes in an H100 architecture

Without local cache, GPU nodes read required artifacts from shared file, object, or parallel storage. With cache, selected data is copied or generated near the node and reused. This can reduce repeated remote reads and protect shared storage from bursts, but it adds a data-placement system that must decide what to store, when to refresh it, and what can be discarded.

Size cache from the working set

Measure bytes, reuse, concurrency, and miss cost

Inventory the data each workload reads or creates, how frequently it is reused, how many jobs run concurrently, and what happens on a miss. Record model versions, dataset shards, image layers, checkpoints, scratch data, and safety headroom separately. Total dataset size alone is usually a poor sizing input because only part may be active on one node.

A simple planning model is: active reusable working set plus concurrent staging plus operational reserve. Apply it by workload class and failure domain. The result should be validated with traces because placement, job scheduling, and reuse distribution can make aggregate capacity look adequate while individual nodes still churn.

Decide the failure-domain boundary

Cache can be attached to one node, pooled within a rack, or supplied by a nearby storage tier. Node-local storage offers direct access but data disappears from that location when the node is unavailable. A shared cache can improve reuse across nodes but introduces its own network and service dependencies. Match the boundary to placement flexibility and recovery expectations.

Protect free space for operations

Do not allocate every usable byte to cached artifacts. Updates, log bursts, temporary files, compaction, failed cleanup, and checkpoint staging need room. Define warning, admission-control, and emergency thresholds from observed workload behavior. When space becomes constrained, the system should shed cache entries predictably rather than fail an unrelated platform service.

Choose explicit data-placement policies

Cache on first use or pre-warm

Cache-on-read is simple and avoids moving unused data, but the first job pays the miss cost and many simultaneous jobs may create a thundering herd. Pre-warming can make a scheduled workload predictable, but it consumes transfer time and capacity before demand is confirmed. Use orchestration context to pre-stage high-confidence artifacts and retain on-demand behavior for the rest.

Shard according to workload placement

Place the shards or models a node is expected to consume. Random copying can fill storage without increasing hits. When a scheduler can move jobs freely, placement metadata and cache awareness may improve decisions. The scheduler should not create hard affinity that strands GPUs merely to preserve a replaceable copy.

Define authoritative sources and write paths

For each artifact, identify the system of record. Read caches should be reconstructable from durable storage or a trusted registry. If checkpoints or outputs are written locally first, define when they become durable, how transfer completion is verified, and whether a job may report success before persistence completes.

OneSource Cloud's AI Storage Architecture treats local performance tiers and shared durable storage as complementary layers. The design should keep durability, metadata, access, and recovery explicit even when local NVMe absorbs high-rate activity.

Eviction and invalidation protect correctness

Eviction decides what leaves when capacity is constrained; invalidation decides when an artifact is no longer trustworthy. Common signals include recency, frequency, size, recomputation cost, expected next use, job priority, and version. A large artifact with a high transfer cost may deserve retention even if a small artifact was accessed more recently.

Key entries by immutable identifiers where possible, such as a content digest, model version, dataset snapshot, image digest, software stack, and preprocessing configuration. Never let the same path refer silently to different content. Verify integrity after transfer and before use, especially when cached artifacts influence model behavior or security.

Secure the cache as part of the data lifecycle

Apply identity and tenant boundaries

Local storage can outlive a job and expose artifacts to later processes if isolation is weak. Enforce workload identities, file permissions, mount policy, encryption, secrets handling, and cleanup according to the deployment model. In multi-user environments, do not assume that node-local means private.

Classify data before caching it

Some datasets, prompts, model weights, or outputs may not be permitted on local persistent media. Record approved artifact classes, encryption and key ownership, retention, deletion, and media-handling requirements. Logs should not expose sensitive paths or identifiers merely to make cache diagnosis easier.

Test deletion and node retirement

Define how entries are removed after jobs, model retirement, access revocation, tenant transition, or equipment service. Hardware replacement and decommissioning need procedures for data-bearing devices. Where deletion evidence matters, connect the process to inventory and an auditable completion record.

Observe cache behavior as a system

Track hit and miss behavior by artifact and workload class, bytes served locally and remotely, fill time, eviction, invalidation, capacity, write backlog, transfer errors, integrity failures, and the performance of the authoritative store. A high hit rate is not automatically good if retained items are cheap to fetch while an important workload misses large artifacts.

Correlate cache data with scheduler placement, GPU idle periods, job start time, checkpoint duration, shared-storage load, model version, and recent changes. A managed AI infrastructure team can use those relationships to tune placement and capacity instead of treating local storage as a fixed server specification.

Validate the design with representative failure tests

Run cold-cache and warm-cache workloads, concurrent starts, capacity pressure, invalidation, corrupt or incomplete entries, unavailable nodes, slow shared storage, and interrupted checkpoint transfer. Confirm workload correctness, detection, fallback, cleanup, and recovery. Measure whether the cache improves the business outcome without making failure behavior less predictable.

Repeat targeted tests after dataset, model, node topology, scheduler, storage, driver, or orchestration changes. Preserve a configuration and workload baseline so an observed improvement or regression can be attributed to a real design change.

FAQ

How much local cache does an H100 node need?

There is no fixed amount. Size from the active reusable working set, concurrent staging, checkpoint or scratch behavior, miss cost, node placement, and operating reserve. Validate with workload traces and per-node pressure rather than dividing the total dataset evenly by GPU count.

Can local NVMe replace shared AI storage?

Usually not. Local NVMe can provide cache, scratch, and staging performance, while shared storage supplies durable access, authoritative data, collaboration, recovery, and cross-node availability. The boundary depends on workload design, but important data should have an explicit durable system of record.

Should checkpoints be written to local cache?

Local staging can absorb checkpoint bursts, but completion semantics must be clear. A checkpoint needed for recovery should not be considered durable until transfer and integrity verification finish. The design also needs behavior for node loss, backlog, insufficient space, and shared-storage failure.

What cache key should model weights use?

Prefer an immutable model identifier or content digest combined with relevant runtime or format information. A mutable filename can return the wrong version. Access policy, encryption, source verification, and invalidation must remain associated with the cached artifact.

Which metrics show whether local cache is helping?

Measure workload start and completion outcomes alongside hits, misses, bytes, fill time, eviction, capacity pressure, shared-storage load, GPU waiting, transfer errors, and integrity failures. Segment by workload and artifact class so an aggregate rate does not hide a costly miss pattern.

Summary

H100 local cache design is a data-placement and lifecycle problem, not a fixed storage-per-GPU calculation. Size from measured working sets, keep authoritative data durable, choose explicit warming and eviction policies, preserve integrity and isolation, observe workload outcomes, and test failures. The right design reduces avoidable data movement without weakening correctness or recovery.

Next step: Review OneSource Cloud's AI storage architecture approach to map local cache, shared data, checkpoints, and recovery to your H100 workload.