Outdated Drivers Are Slowing You Down
One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchPC Slower Than It Used to Be?
A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11iTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
A drive’s advertised sequential-read or sequential-write figure cannot tell you whether it will keep an AI job supplied with data. A useful benchmark combines controlled SSD tests for bandwidth, IOPS and latency with workload-level tests for training, checkpointing, inference or vector retrieval. It also separates cold-cache from warm-cache behavior and records the complete storage path, because a fast SSD can still produce slow data loading when the bottleneck is memory, preprocessing, metadata, PCIe topology or shared storage.
This guide gives you a repeatable test plan, safe fio examples, an AI-workload matrix and a reporting format that makes comparisons meaningful.
What an AI storage benchmark must answer
Begin with the question your purchase or tuning decision must answer. Are you trying to determine whether a local NVMe drive can feed training GPUs, whether checkpoint saves will interrupt a job for too long, whether a serving system can read model and KV-cache data at low tail latency, or whether a vector database can sustain retrieval traffic? Those are different storage problems.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsMLCommons describes MLPerf Storage as measuring how fast storage systems can supply training data while a model is trained. Its v3.0 suite includes separate training, KV-cache and vector-database workloads. Results are intended to be compared within the same workload and configuration, not collapsed into one ranking across unlike jobs.
#1 Best Overall
- Ideal for high speed, low power storage
- Gen 4x4 NVMe PCle performance
- Up to 6,000MB/s read, 4,000MB/s write
- Includes Acronis cloning software
- 5-year limited warranty
Write down these facts before selecting a test:
- Storage location: local SSD, a filesystem on local drives, RAID or another volume, or shared/network storage.
- Workload phase: training, fine-tuning, checkpoint save, checkpoint restore, model serving, KV-cache access or vector retrieval.
- Dataset size, file-count and file-size distribution, and whether the data fits in system memory or a local cache.
- Read/write balance, expected sample or request size, number of data-loader workers, concurrent jobs and accelerator count.
- Capacity, interface, cooling and platform limits that could prevent the drive from operating at its intended link speed.
NVIDIA’s DGX SuperPOD H200 storage guidance makes the same practical point: characterize workloads and organizational needs before finalizing performance and capacity requirements. A single SSD therefore has no universal AI pass/fail number.
Why the advertised sequential number is insufficient
Vendor sequential figures normally describe a large, contiguous transfer under a particular queue depth, test size and firmware state. Training data loaders may issue randomized sample reads, often in parallel. Checkpointing creates sustained writes followed by restore reads. Serving can combine model-file access with small, latency-sensitive KV-cache operations. Vector search has its own mixture of index, metadata and vector accesses.
Small and random operations can dominate despite a high sequential rate. NVIDIA’s GPUDirect Storage benchmarking guidance identifies smaller I/O sizes and random patterns as common in production and treats IOPS as a necessary benchmark dimension. A result that reports only gigabytes per second hides whether the drive completes those operations quickly enough and whether tail latency rises when workers contend.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
Burst behavior is another trap. A consumer or enterprise SSD may use spare flash or dynamic cache to deliver a short peak, then slow during a long write. Run long enough to expose steady-state behavior, and report both the early burst and the sustained region when they differ. The test should also show whether the host, filesystem or network path—not the NAND media—is limiting throughput.
Build a test matrix before running benchmarks
Use a matrix so every important access pattern is tested under more than one concurrency level. The following set is a practical minimum for a local SSD; add rows that match your application.
| Test | What it reveals | Variables to sweep |
|---|---|---|
| Large sequential read | Bandwidth for contiguous dataset or model transfers | Block size, queue depth, jobs, cold and warm cache |
| Large sequential write | Checkpoint and staging throughput, including post-burst behavior | Runtime, queue depth, jobs, durability or sync mode |
| Small random read | IOPS and latency for samples, metadata or cache entries | Block size, read ratio, queue depth, jobs |
| Small random write | Cache updates, indexes and write-heavy service behavior | Block size, write ratio, queue depth, jobs |
| Mixed read/write | Contention when input, output and background work overlap | Read/write mix, concurrency and runtime |
| AI workload test | Whether the complete path keeps accelerators or requests supplied | Real files, workers, batches, model phase and cache state |
For every row, collect throughput, IOPS and latency. Report at least average latency and tail latency such as p95 and p99 when the tool provides them. Tail values expose stalls that an average can conceal.
How to establish a reproducible SSD baseline with fio
Prepare a safe test target
Run synthetic tests on a dedicated test file or a test filesystem. A raw-device test can destroy data; never substitute a production device path for the file path in these examples. Keep the test file large enough that it cannot be held entirely in client memory, and record the SSD model, capacity, firmware, interface and link width before starting.
Recommended Free Tools
Rank #2
- MEET THE NEXT GEN: Consider this a cheat code; Our Samsung 990 PRO Gen4 SSD helps you reach near max performance with lightning-fast speeds; Whether you’re a hardcore gamer or a tech guru, you’ll get power efficiency built for the final boss
- REACH THE NEXT LEVEL: Gen4 steps up with faster transfer speeds and high-performance bandwidth; With a more than 55% improvement in random performance compared to 980 PRO, it’s here for heavy computing and faster loading
- THE FASTEST SSD FROM THE WORLD'S FLASH MEMORY BRAND: The speed you need for any occasion; With read and write speeds up to 7450/6900 MB/s you’ll reach near max performance of PCIe 4.0 powering through for any use
- PLAY WITHOUT LIMITS: Give yourself some space with storage capacities from 1TB to 4TB; Sync all your saves and reign supreme in gaming, video editing, data analysis and more
- IT’S A POWER MOVE: Save the power for your performance; Get power efficiency all while experiencing up to 50% improved performance per watt over the 980 PRO; It makes every move more effective with less consumption
Record the host CPU and memory, operating system and kernel, filesystem, fio version, I/O engine, direct-I/O setting, queue depth, job count, block size, test size and runtime. Also note drive temperature and any thermal-throttling observation.
Measure sequential bandwidth
fio --name=seq-read --filename=/mnt/ssd-test/fio.bin --size=1T --rw=read --bs=1M --iodepth=32 --numjobs=1 --direct=1 --runtime=300 --time_based --group_reportingThis measures a large-block read through the mounted path while bypassing the page cache. Change the file size to fit your test device and capacity; do not assume the example size is suitable. Repeat with --rw=write on a disposable file or freshly prepared test area. A write test can consume the drive’s spare area and should not be run against valuable data.
Measure random IOPS and latency
fio --name=rand-read --filename=/mnt/ssd-test/fio.bin --size=256G --rw=randread --bs=16k --iodepth=32 --numjobs=4 --direct=1 --runtime=300 --time_based --group_reportingUse block sizes that resemble the application rather than treating 16 KiB as a universal AI standard. Run additional small-block cases and a larger sample-sized case. Repeat with --rw=randwrite only on disposable data. Capture IOPS and latency percentiles, not just the headline bandwidth.
Sweep queue depth and concurrency
Run each important pattern at a low-concurrency point, then increase iodepth and numjobs systematically. Low concurrency can under-drive a fast SSD and make it look slow; excessive concurrency can raise tail latency and produce a result that no application can use. Keep the host, file, runtime and cache state constant while changing one variable.
Test mixed traffic and sustained behavior
fio --name=mixed --filename=/mnt/ssd-test/fio.bin --size=256G --rw=randrw --rwmixread=70 --bs=32k --iodepth=16 --numjobs=4 --direct=1 --runtime=900 --time_based --group_reportingA mixed test is useful when data loading overlaps checkpoint writes, logging or index maintenance. The 70/30 split is an example, not a claim about every AI job; replace it with the measured application mix. Use a long enough run to see behavior after any short burst phase.
Separate cold-cache and warm-cache results
A warm run may measure data served from system memory or a filesystem cache rather than from the SSD. Publish cold and warm results as separate measurements. State whether you cleared caches between runs, used direct I/O, restarted the process or recreated the test file, and whether another service could have populated the cache.
MLPerf Storage documentation describes NVMe testing with O_DIRECT to bypass page cache. Its training benchmark uses a dataset at least five times the aggregate client DRAM, a rule intended to reduce client-RAM effects. That five-times value is a benchmark rule for the described test, not a universal SSD-sizing ratio.
Rank #3
- BREAKTHROUGH PCIe 5.0 PERFORMANCE: Supercharge your workflow and gaming with PCIe 5.0, boasting up to 14,700/13,400 MB/s¹ sequential read/write speeds. Tackle massive files and power up your gaming with Gen5—twice as fast as the 990 PRO SSD.
- EVERY TASK, TURBOCHARGED: Speed past productivity limits. With random read/write speeds up to 1,850K/2,600K IOPS¹, enjoy fast game loads, seamless AI apps, and efficient multitasking. Virtually no lag, no limits—just nonstop performance.
- THINK FAST, CREATE FASTER: With random read/write speeds of up to 1,850K/2,600K IOPS¹, the 9100 PRO SSD fuels seamless AI content creation, swift loads, and smooth gameplay. Work, play, and create at lightning speed.
- SPEED, WHENEVER YOU NEED: From laptops, desktop PCs, to PS5 with added heatsink devices², experience blazing PCIe 5.0 speeds and up to 8TB of storage. Perfect for video editing, gaming, and creative tasks, with the compatibility to match your device.
- STAY COOL, RUN FAST: Push limits, not temperatures. A 5nm controller boosts power efficiency up to 49% over the 990 PRO SSD³, while advanced thermal control keeps performance smooth and reliable.
For an application test, make the dataset and access sequence representative. If production deliberately relies on a memory cache, measure that warm mode because it is a real operating mode; just do not present it as raw-drive performance. If you need to know whether the storage path can supply uncached data, use a cold or direct-I/O run and disclose the method.
The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →How do I test an SSD for AI training?
Reproduce the data-loader pattern
Use the actual framework and dataset when possible. Include randomized batch access, realistic sample sizes, the intended number of data-loader workers and the expected accelerator count. Measure storage throughput alongside accelerator utilization, batch delivery time and any queue of workers waiting for input.
A synthetic sequential read can be excellent while a training job remains input-bound because samples are small, scattered across many files, compressed, decoded on the CPU or limited by filesystem metadata. Keep those stages visible in the trace instead of attributing every delay to the SSD.
Use a supply-rate result, not a drive-only score
Report whether the training run keeps accelerators busy at the target batch rate and what happens when you increase workers or concurrent readers. If throughput stops improving while SSD utilization is low, investigate CPU preprocessing, memory bandwidth, file-open overhead or framework scheduling. If SSD utilization and queue depth rise while accelerators idle, the storage path may be the limiting stage.
Benchmark checkpoint save and restore separately
Checkpointing stresses sustained writes, durability behavior and subsequent restore reads. Measure save and restore as two tests; a drive that writes quickly may not restore quickly under the same concurrency or filesystem layout.
Include the checkpoint size, number of files, write pattern, synchronization or durability setting and elapsed interruption time. NVIDIA notes that checkpoint operations can be synchronous, so training may stop while a checkpoint is written. Report that pause rather than only the device’s write bandwidth.
Run several checkpoints or a long enough write so the result includes steady-state behavior. Observe temperature and throttling, and record whether compression, sharding or encryption moved work to the CPU.
Rank #4
- HUGE SPEED BOOST: Get random read/write speeds that are 40%/55% faster than 980 PRO; Experience up to 1400K/1550K IOPS, while sequential read/write speeds up to 7,450/6,900 MB/s reach near the max performance of PCIe 4.0*
- BREAKTHROUGH POWER EFFICIENCY: Use less power and get more performance; Enjoy up to 50% improved performance per watt over 980 PRO, plus optimal power efficiency with max PCIe 4.0 performance**
- SMART THERMAL CONTROL: Samsung's own nickel-coated controller delivers effective thermal control; With its slim size, 990 PRO is a perfect fit for desktops and laptops that meet the PCI-SIG D8 standard***
- THE CHAMPION MAKER: Up to 65% improvement in random performance enables faster loads for an ultimate gaming experience on PS5 and DirectStorage PC games****
- SAMSUNG MAGICIAN SOFTWARE: Get the most out of your SSD with Samsung Magician's advanced yet intuitive optimization tools; Monitor drive health, protect valuable data, and receive important updates for your 990 PRO
Test inference, KV-cache and vector retrieval as their own workloads
Model serving and KV-cache access
If serving depends on KV-cache reads and writes, use a cache test with the expected request concurrency, entry size and read/write ratio. Report requests or cache operations per second together with average and tail read latency. A sequential model-file test cannot predict this result.
MLPerf Storage v3.0 includes a KV-cache workload. Use its workload definition when comparing published results, and do not mix that score with training or vector-database scores as if they measured the same behavior.
Vector database retrieval
When retrieval is central, benchmark the vector database with its real index, metadata and query distribution. Measure query throughput, tail latency and background indexing or compaction impact. MLPerf Storage v3.0 added a vector-database test, providing a recognized workload distinct from ordinary sequential or random fio jobs.
Document the complete platform
The SSD is only one element in the data path. Include all of the following in the test record:
- SSD model, usable capacity, firmware and drive temperature.
- PCIe generation, link width and negotiated link speed.
- CPU model, memory capacity and memory-channel configuration.
- Operating-system release, kernel, filesystem, mount options and raw-device or filesystem mode.
- Benchmark version, exact command or configuration, I/O engine, block size, read/write mix, queue depth and job count.
- Test-file size, runtime, warm-up period, cache-clearing method and direct-I/O setting.
- Number of accelerators, data-loader workers, concurrent jobs and any network or shared-storage component.
- Temperature, power or throttling observations and whether other workloads ran during the test.
These details let another engineer determine whether a difference came from the drive, host, software path or test setup. Without them, two apparently similar scores may not be comparable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare SSD benchmark results correctly
Compare only like with like: same workload definition, platform path, cache state, test size, block size, queue depth, concurrency and runtime. Use the following axes for a decision:
Free tools Windows power users keep installed
One-click scans. No signup required.
| Comparison axis | When it matters | What to report |
|---|---|---|
| Sequential bandwidth | Large contiguous model, dataset or checkpoint transfers | Read/write rate, sustained duration and cache state |
| Random or small-I/O IOPS | Sample, metadata, cache and index access | IOPS at each block size and queue depth |
| Tail latency | Interactive serving and synchronized workers | p95, p99 and concurrency level |
| Sustained writes | Checkpointing, ingestion and indexing | Steady-state rate, temperature and pause time |
| Restore performance | Returning a job to service after a checkpoint | Elapsed restore time and read throughput |
| End-to-end supply | Training or serving efficiency | Accelerator utilization, batch/request rate and storage metrics |
| Capacity and compatibility | Deployment feasibility | Usable capacity, interface, cooling and topology |
A PCIe NVMe SSD is a sensible physical category for local dataset staging, but the benchmark should determine whether its capacity, cooling and interface fit your platform. Do not infer better AI results from a larger advertised sequential number alone.
Best Value
- SPEED UP PROJECTS. Launch creator applications fast with uncompromising PCIe 4.0 read speeds up to 7,100MB/s,[2] (1TB and 2TB[1] models) and write speeds up to 6,700MB/s[2] (1TB[1]-4TB[1] models).
- CREATE AND STORE MORE. Make more room for your 4K videos and high-resolution images with capacities from 500GB[1] up to 4TB[1] on M.2 2280 built with our trusted 8th generation SANDISK BiCS QLC 3D CBA NAND.
- IT GOES WHERE YOU GO. With an all-new power efficient design, your drive delivers high performance with low power, giving you more time to be productive while on the go.
- UNCOMPROMISED RELIABILITY. With up to 1,200 TBW[3] (4TB[1] model) endurance rating, your drive is designed for creators.
- KEEP YOUR DRIVE UPDATED. Monitor your SSD’s performance and check for updates with the downloadable SANDISK Dashboard application.[5]
Why a fast SSD benchmark can still produce slow AI data loading
| Symptom | Likely cause | Next check |
|---|---|---|
High fio bandwidth, low accelerator utilization |
CPU decoding, preprocessing, metadata or too few loader workers | Profile CPU time, file-open operations, batch wait time and worker queues |
| Good cold run, much faster warm run | System or filesystem cache is serving the data | Repeat with a dataset larger than memory and disclose cache handling |
| Sequential score is strong, random workload is weak | Small or scattered reads expose IOPS and latency limits | Run application-sized random reads and inspect p95/p99 latency |
| Performance falls during a long write | Burst cache exhausted or thermal throttling | Extend runtime, monitor temperature and report steady-state rate |
| More workers make performance worse | Queue contention, filesystem limits or shared-path saturation | Sweep queue depth and concurrency while watching tail latency |
| Local test is fast, production is slow | Network, RAID, PCIe topology or a different filesystem path | Benchmark the complete production path, not only the bare drive |
| Checkpoint save stalls training | Synchronous durability or CPU-side serialization | Measure save pause, sync behavior, compression and file layout |
Model and dataset characteristics determine storage demand. NVIDIA’s architecture material contrasts cache-friendly NLP or large-language-model cases with large uncached image or video cases; those workloads can require very different storage paths even on the same host.
Use recognized AI suites without misreading their scope
MLPerf Storage v3.0 is useful when you need a framework-level workload and a published comparison method. Read the full submission and configuration details before comparing results: submissions run on submitters’ hardware under common rules, and the workload, client count, storage architecture and software path all affect the outcome.
The suite’s training dataset rule of at least five times aggregate client DRAM is designed to limit client-memory effects in that benchmark. It does not mean every deployment needs five times its memory in storage, nor does it define a target for one SSD.
Quick wins for a faster PC:
Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →NVIDIA’s DGX SuperPOD H200 documentation publishes architecture- and workload-specific system read/write guidance. Those figures describe named systems and workload classes, not a universal specification for an individual drive. Preserve the original system context whenever you use such a reference.
A practical reporting template
Publish one row per test and attach the exact configuration. A concise report should contain:
- Workload definition: training, fine-tuning, checkpoint, serving, KV-cache or vector retrieval; dataset and file distribution; read/write balance; concurrency.
- Storage path: local device, filesystem, RAID, volume or shared storage, including PCIe and network topology.
- Platform: SSD model and capacity, firmware, CPU, memory, accelerators, operating system, kernel and filesystem.
- Test settings: tool and version, I/O engine, block size, queue depth, jobs, direct-I/O mode, test size, runtime and warm-up.
- Cache state: cold or warm, cache-clearing or bypass method, dataset-to-memory relationship.
- Results: bandwidth, IOPS, average latency, p95/p99 latency, sustained behavior, temperature and throttling.
- Application outcome: batch or request rate, accelerator utilization, checkpoint interruption time or restore time.
- Interpretation: the bottleneck identified, the conditions under which the result applies and any limits on comparison.
This format prevents a single peak number from being mistaken for an end-to-end guarantee and makes a future retest possible when firmware, kernel, workload or storage topology changes.
Bottom line for an AI SSD decision
Benchmark the workload you intend to run, not the specification you hope will predict it. Use fio to isolate bandwidth, IOPS, latency, queue-depth and sustained-write behavior; then run training, checkpoint, serving or vector tests through the full data path. Separate cold and warm cache results, make the test data large enough to avoid accidental memory benchmarks, and publish every condition needed for a like-for-like comparison. The best SSD for an AI system is the one that meets the target workload’s supply rate, tail-latency and capacity requirements on the actual platform—not necessarily the one with the highest advertised sequential speed.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
