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Use both, for different jobs. Keep authoritative model weights, datasets, and durable checkpoints in object storage; stage the data an inference worker needs immediately on local NVMe/SSD, a cache, or a low-latency parallel file system. Object storage provides a durable, shareable source of truth at scale. Local SSD can cut access latency and handle I/O-intensive work, but data on an instance’s local disk may disappear when that instance stops or is terminated.
The right question is not which storage is universally faster. It is which tier should serve each data access pattern without leaving GPUs waiting for data or making recovery depend on an ephemeral disk.
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What is the difference between object storage and local SSD?
Object storage holds data as objects accessed through a service, rather than presenting it as a disk physically attached to an individual compute instance. Local SSD or NVMe is storage close to the compute worker, often physically attached to it. Those differences shape latency, sharing, durability, and operations—not just peak speed.
Cloud object storage is built for large collections of durable data and access by multiple workers. Google Cloud describes Cloud Storage as designed for massive datasets used in training or bulk inference, and recommends it for scalable, cost-efficient training data, asynchronous checkpointing, and model weights. AWS likewise positions object storage for read-heavy machine-learning workflows. A local SSD is better understood as a high-performance working tier: Google Cloud describes its Local SSD as physically attached, temporary storage with very low latency and high IOPS, suitable for caches and scratch processing.
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For AI inference, “data” means more than model weights. It can include hot features, input batches, temporary embeddings, scratch transforms, runtime state, and checkpoint files. They do not all need the same persistence or access pattern. Put each in the tier that matches its role.
- Object storage: the durable, scalable source of truth for canonical datasets, model artifacts, and recovery checkpoints.
- Local SSD/NVMe: a fast, temporary working area for frequently accessed or regenerable data near the inference process.
- Cache or parallel file system: an intermediate option when workers need repeated access to data from a shared, low-latency tier.
A retail NVMe SSD installed in a computer is not the same thing as cloud instance-store Local SSD. The cloud service’s attachment, lifetime, failure behavior, and billing are tied to its platform and instance; do not assume that cloud local-disk behavior follows from owning a consumer SSD.
Which storage should hold model weights, features, and checkpoints?
Keep the canonical copy in object storage, then stage active data closer to inference workers when latency or repeated reads justify it. Treat the local copy as a performance optimization, not the only copy.
| Data or workload | Recommended tier | Reason |
|---|---|---|
| Canonical datasets and archived features | Object storage | Durable, scalable, shareable source of truth. |
| Model registry artifacts and authoritative model weights | Object storage, with optional staging | Supports centralized distribution; staging can shorten cold starts for workers that need the model immediately. |
| Large read-heavy datasets accessed by many workers | Object storage, optionally with a cache or colocated high-performance class | Capacity and aggregate scale; caching can reduce repeated remote reads. |
| Model cold start for a latency-sensitive service | Pre-warmed object-storage cache or local SSD | Places weights closer to the inference worker before the request arrives. |
| KV-cache offload, synchronous checkpointing, or high metadata concurrency | Low-latency parallel file system or local SSD, depending on sharing and recovery needs | Latency and metadata behavior can matter more than bulk object throughput. |
| Scratch transforms, temporary embeddings, and shuffle data | Local SSD/NVMe | Provides a high-IOPS work area when the data can be regenerated. |
| Durable checkpoints and restart state | Object storage, with optional fast local staging | Preserves recovery data beyond the life of a worker. |
The KV-cache row needs particular care: it is a performance-sensitive runtime-data case, not a blanket instruction to put every inference system’s KV cache on SSD. The right choice depends on whether the workload actually offloads that state, what latency it can tolerate, and whether the state must survive or be shared. If durable restart state is required, a local-only copy is not sufficient.
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Is local SSD faster than object storage for inference?
Local SSD is generally the lower-latency, higher-IOPS tier for a worker’s nearby reads and writes. That does not make it the best answer for every inference workload: object storage can scale across large datasets and workers, and caching or specialized object-storage acceleration can narrow the gap for particular access patterns.
Google Cloud says Local SSD offers superior IOPS and very low latency compared with its Hyperdisk and Persistent Disk storage. Microsoft Azure’s infrastructure guidance says to use local NVMe/SSD file systems when performance is the top priority for I/O-intensive training and real-time inference. These are provider-specific recommendations, not a universal benchmark showing that every local SSD beats every object-storage product under every workload.
Peak throughput alone is a poor predictor of inference performance. A workload that reads many small files or performs frequent metadata operations can behave differently from one that streams large sequential files. Likewise, the apparent storage speed may not be the bottleneck if the request path is limited elsewhere. Measure the deployed path and request pattern rather than relying on a single “faster storage” label.
Published figures: compare the scope, not just the headline
| Product or configuration | Published figure | What the figure describes |
|---|---|---|
| One 375-GiB Google Cloud NVMe Local SSD | 170,000 read IOPS; 90,000 write IOPS; 660 MiB/s read throughput; 350 MiB/s write throughput | Google Cloud’s 2026 Local SSD performance documentation for one disk. |
| 32 Google Cloud NVMe Local SSD disks | 3,200,000 read IOPS; 6,240 MiB/s read throughput | Google Cloud’s 2026 documentation for the stated 32-disk configuration—not a single disk. |
| Google Cloud Rapid Bucket | Up to 15 TB/s throughput | Google Cloud’s 2026 documentation for its documented Rapid Bucket product tier. |
| Google Cloud Rapid Cache | Up to 2.5 TB/s throughput | Google Cloud’s 2026 documentation for its documented Rapid Cache product tier. |
| Amazon S3 Standard | 99.99% availability; 99.999999999% annual object durability | Figures from AWS’s current S3 FAQ; availability and durability describe different service properties. |
These figures do not establish a direct, like-for-like race. The Local SSD values describe specific disk configurations; Rapid Bucket and Rapid Cache figures are “up to” values for their documented product tiers. A service’s aggregate throughput is not the same as a single request’s latency, and a maximum throughput number does not promise that a given application will achieve it.
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How can object storage be fast enough for inference?
Object storage does not have to be the immediate serving path for every read. A cache or colocated high-performance tier can keep frequently needed content close to workers while leaving the authoritative copy in object storage.
Google Cloud documents Rapid Bucket and Rapid Cache as object-storage acceleration options for AI: the described tier offers sub-millisecond open-file latency and very high aggregate throughput. These are product-specific capabilities, not a general property of all object storage or a guarantee for every region, object, request pattern, or deployment. Confirm the service’s actual availability and performance characteristics for your chosen configuration.
A cache is especially useful when many workers repeatedly read the same data. Its value depends on the working set and hit rate: if each request needs different data, or the cache is cold when a worker starts, remote reads may still be on the critical path. A pre-warmed cache can help with model cold starts, but it needs a population and invalidation strategy so that workers do not serve stale artifacts after a model update.
How do you prevent storage latency from starving GPUs?
Keep the data path ahead of the compute demand. The practical approach is to store durable originals remotely, stage the active working set before it is needed, and measure whether data access is actually causing GPU idle time.
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- Put authoritative copies in object storage. Store canonical datasets, model artifacts, and durable checkpoints there so recovery and distribution do not depend on one worker’s local disk.
- Identify the hot working set. Determine which weights, features, or other inputs workers repeatedly need and how much of that set can be staged near them.
- Prefetch at deployment or scale-out. Populate a colocated cache, parallel file system, or local NVMe before the worker needs the data for latency-sensitive inference.
- Keep ephemeral storage disposable. Restrict local SSD contents to data that is regenerable or replicated elsewhere. Design for a stopped, terminated, or replaced instance to lose its local data.
- Checkpoint to the durable tier. Prefer asynchronous checkpointing to object storage unless the workload requires synchronous, low-latency checkpoint semantics. If it does, select a fast tier for the critical write and still maintain a recovery copy in durable storage.
- Measure end to end. Track model-load time, cache-hit rate, p95 and p99 read latency, metadata operations, network throughput, and GPU idle time. Compare the measurements with the service’s request and recovery requirements.
- Consider direct GPU I/O only when justified. NVIDIA describes GPUDirect Storage as enabling direct transfers between GPU memory and NVMe or NVMe-oF, with submillisecond storage performance for real-time inference. Consider it when CPU copies or bounce buffers are the bottleneck and the platform supports it.
NVIDIA’s storage hierarchy emphasizes that latency improves as storage sits closer to GPU memory. That is a useful way to reason about the path, but moving data closer is not free: it adds staging, capacity, and lifecycle decisions. First establish that storage is the source of GPU idle time, then optimize the specific stage causing the wait.
What can go wrong with a local-SSD design?
The main failure mode is confusing fast with durable. Google Cloud warns that Local SSD data is not automatically replicated and may be lost when an instance stops or is terminated. If an authoritative model, checkpoint, or unique input exists only there, a compute lifecycle event can become a data-loss or recovery incident.
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- Only copy on a worker: keep important data in object storage or another durable tier; replicate local data if it cannot be recreated.
- Cold worker after scale-out: include model and feature staging in startup planning, and measure the resulting time before declaring a service ready.
- Many small or metadata-heavy reads: evaluate a low-latency parallel file system or local staging rather than assuming bulk object throughput addresses metadata behavior.
- Cache misses dominate: measure cache-hit rate against the real workload; a cache is useful only when it captures enough of the active working set.
- Checkpoint writes block inference: use asynchronous object-storage checkpointing when semantics permit; if they do not, explicitly choose a low-latency path and durable recovery plan.
Recovery planning should define which data is authoritative, which can be rebuilt, how a replacement worker gets its working set, and whether a checkpoint is recent enough for the application. An object-storage copy supports recovery, but it does not automatically make a lost local cache instantly available; staging time remains part of restart behavior.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which option costs less?
There is no universal price winner. Object-storage cost depends on capacity, requests, retrieval, egress, and storage class. Local SSD cost is coupled to the compute instance and how long it runs. Google Cloud and Microsoft guidance support using object storage as a cost-efficient scalable tier and local SSD as temporary high-performance storage, but they do not establish a numeric break-even point for an arbitrary AI workload.
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To compare the actual designs, use the same region, data volume, read/write pattern, cache-hit rate, and instance schedule. Include the storage bill and the compute time spent waiting for data or populating caches. A design with cheaper storage can still be the wrong operational choice if it increases cold-start time or leaves expensive accelerators idle; a local tier can also be wasteful when the working set is small or seldom reused.
Do not infer a cost comparison from throughput figures alone. The published performance values above describe specific configurations and product tiers, not their total cost for a workload. Price a concrete pattern against the provider’s current regional terms before committing to an architecture.
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For most inference systems, begin with object storage as the durable source of truth and add a hot tier only where measurement shows that latency, metadata, or repeated reads warrant it. The simpler design is often enough for a workload that tolerates remote reads; a latency-sensitive service with a repeatable hot set may benefit from prefetching into a cache or local SSD.
- Durability first: object storage holds source datasets, model versions, and durable checkpoints.
- Latency where it matters: a cache, parallel file system, or local NVMe serves frequently accessed data near workers.
- Failure as a normal event: a worker can disappear without taking the only copy of anything important.
- Evidence before complexity: add a faster tier or GPU-direct path in response to measured bottlenecks, not because a peak-speed figure looks attractive.
Frequently Asked Questions
Does putting model weights on SSD make every inference request faster?
No. It can reduce the time needed to fetch weights, especially during a cold start, but it does not by itself speed up request processing when storage is not on the critical path. Compare end-to-end latency and GPU idle time before and after staging.
Can object storage be mounted and used exactly like a local disk?
Do not assume so. Object storage is accessed as an object service, while disk and file-system paths have different interfaces and behavior; applications that expect file-system semantics may need an appropriate cache, gateway, or parallel file system.
Should I use GPUDirect Storage for every inference deployment?
No. NVIDIA’s description is relevant when a supported platform can avoid CPU copies or bounce buffers that are actually limiting data movement. It is an optimization to evaluate after measuring the path, not a replacement for durable storage or a guaranteed improvement.
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How often should I copy local inference data back to object storage?
That depends on whether the local data is authoritative, regenerable, or required for recovery. Define the acceptable recovery point and keep durable state accordingly; temporary cache contents generally need a rebuild path rather than being treated as the sole record.
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