Diagnosing Your AI’s Fatal Flaw

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It started with a warning, then silence.

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The GPU bill climbed as if the accelerator never slept,

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yet outputs crawled like the lights went out.

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Dashboards were green, customers weren’t.

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The anomaly didn’t fit.

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Near zero GPU utilization, while latency spiked,

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no alerts fired, no red lines, just time evaporating.

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The evidence suggests a single pathology masquerading is normal.

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Here’s the promise.

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We’ll trace the artifacts, name the culprit,

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and fix the pathology.

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We’ll examine three failure modes, CPU fallback,

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version mismatch across CUDA and on an X10-O-RT

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and container misconfiguration,

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and we’ll prove it with latency, throughput,

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and GPU utilization before and after.

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Case set up.

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The environment and the victim profile.

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Every configuration tells a story,

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and this one begins with an ordinary tenant under pressure.

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The workload is taxed to image diffusion.

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Stable diffusion variance running at 512, 512,

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and scaling to 1024, 2424.

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Traffic is bursty.

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Concurrency pushes between 8 and 32 requests.

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Batch sizes float from 1 to 8.

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Service levels are strict on tail latency.

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P95 breaches translate directly into credits and penalties.

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The models aren’t exotic, but their choices matter.

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Or an X exported stable diffusion pipelines.

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Cross-attention optimizations like X-formers

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or scale-dot-product attention and scheduler selections

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that trade steps for quality.

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The ecosystem is supposed to accelerate

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when the plumbing is honest.

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Hardware looks respectable on paper.

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Nvidia RTX and A-Series cards in the cloud,

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16 to 32GB of VRAM.

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PCIe sits between the host and device like a toll gate.

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Fast enough when configured,

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punishing when IO binds fallback to pageable transfers.

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In this environment, nothing is accidental.

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The tool chain stacks in familiar layers.

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PyTorch is used for export,

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then on X runtime or 10-so-RT takes over for inference.

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Could a driver sit under everything?

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Attention kernels promise speed.

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If versions align.

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The deployment is strictly containerized.

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Immutable images, CI-controlled rollouts,

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blue-green-by-policy.

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That constraint should create safety.

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It can also freeze defects in amber.

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The business stakes are not abstract.

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Cost per request defines margin.

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GPU reservations price by the hour

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whether kernels run or not.

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When latency stretches from seconds to half a minute,

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throughput collapses.

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One misconfiguration turns an accelerator into a heater,

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expensive, silent, and busy,

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doing nothing that helps the queue.

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Upon closer examination, the victim profile narrows.

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Concurrency at 16 batches at 2 to stay under VRAM

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ceilings on 525-512, stepping to 2025 for quality.

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The tenant expects a consistent P95.

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Instead, the traces show erratic latencies,

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wide deltas between P50 and P95,

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and GPU duty cycles oscillating from 5% to 40%

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without an obvious reason.

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CPU graphs tell a different truth,

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cores pegged when no preprocessing justifies it.

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The evidence suggests three avenues.

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First CPU fallback.

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When the Gouda or 10-so-RT execution provider fails to load,

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the engine quietly selects the CPU graph.

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The model works by the 1030 X the latency,

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second version mismatch.

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O and NX runtime compiled against one,

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Suda nodes running another 10-so-RT engines invalidated

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and rebuilt with generic kernels.

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Utilization appears, but the fast paths are gone.

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Third, container misconfiguration, bloated images,

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missing GPU device mounts, wrong Nvidia container toolkits settings

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and memory arena’s hoarding allocations,

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amplifying tail latency under load.

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In the end, this isn’t the mystery about models.

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It’s a case about infrastructure truthfulness.

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We will trace the artifacts,

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provider order, capability logs, device mounts,

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and correlate them to three unblinking metrics,

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latency, throughput and GPU utilization.

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Evidence file A CPU.

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Fallback, the quiet saboteur.

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It started with a request that should have taken seconds and didn’t.

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The GPU meter was quiet, too quiet.

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The CPU graph, meanwhile, rose like a fire alarm.

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Upon closer examination, the engine had made a choice.

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It ran a GPU-priced job on the CPU.

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No alerts fired, the output returned eventually.

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This is the quiet saboteur CPU fallback.

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Why it matters is simple.

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Stable diffusion on a CPU is a time sync.

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The model works, but the latency multiplies 10 to 30 times slower

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and throughput collapses.

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In an environment selling milliseconds, that gap is fatal.

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The bill keeps counting GPU time,

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but the device doesn’t do the work.

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The timeline revealed the pattern.

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Containers that ran locally with CUDA flew.

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Deployed to a cluster node with a slightly different driver stack,

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the same containers booted, served health probes, and then degraded.

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The health endpoint only checked is the server up,

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so it never checked is the GPU actually executing.

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In this environment, nothing is accidental.

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Silence is an artifact.

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The core artifact is execution provider order in ON and X runtime.

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The engine accepts a list.

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Try 10-so-arty, then CUDA, then CPU.

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If CUDA fails to initialize, wrong driver, missing libraries,

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device not mounted, or RT will quietly bind the CPU execution provider,

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no exception, no crash, just align in the logs often below the fold.

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CUDA execution provider not available.

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Falling back to CPU.

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That line is the confession most teams never read.

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Here’s the weird part.

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Utilization charts look deceptively normal at first glance.

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Requests still complete.

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A service map shows green.

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But the GPU duty cycle hovers at 5%,

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while CPU user time goes high and flat.

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P50 latency quadruples and P95 unravels.

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Bursty traffic makes it worse.

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CUDA build an autoscale adds more replicas

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that all inherit the same floor.

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Think of it like a relay team, where the sprinter never shows up,

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so the librarian runs the leg.

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The button moves, but not at race speed.

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In other words, your system delivers correctness

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at the expense of the entire SLO budget.

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Artifacts pile up quickly when you trace the boot sequence.

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Provider load logs show CUDA initialization attempts

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with driver version checks.

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If the container was built against CUDA 12.2,

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but the node only has 12, e. initialization fails.

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If Nvidia container toolkit isn’t configured,

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the device mount never appears inside the container,

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no dev Nvidia, no lib CUDA.

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So.

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If the PotSpec doesn’t request GPUs explicitly,

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the scheduler never assigns the device.

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Any one of these triggers the silent downgrade.

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Reproduction is straightforward.

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On a misconfigured node, a simple inference prints providers,

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CPU execution provider, where you expect

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tensoret execution provider, CUDA execution provider.

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Push a single 5.12-12 prompt in the GPU remains idle.

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CPU threads spike the image returns in 2040 seconds

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instead of 2 to 6.

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Repeat on a node with proper drivers and mounts,

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the same prompt completes in a fraction of the time

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and the GPU duty cycle jumps into a sustained band.

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The evidence suggests the current guardrails are theatrical.

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Health probes return 200 because the server responds.

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There’s no startup assert that the GPU path is live.

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Performance probes don’t exist,

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so orchestration believes replicas are healthy.

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The system can’t tell the difference

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between acceleration and emulation.

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The countermeasure is blunt by design.

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Hard fail if the GPU execution provider is absent or degraded.

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Refused to start with CPU in production.

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At process launch, enumerate providers assert

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that tensor RT or CUDA loaded

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and that the device count matches expectations.

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Lock the capability set, QDN, tensor cores available,

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memory limits, and exit non-zero if anything is missing.

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Trade availability for integrity,

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let orchestrators reschedule on a healthy node.

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To make it stick, enforce I/O binding verification.

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Bind inputs and outputs to device memory

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and validate a trivial inference at startup.

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One warm run that exercises the fused attention kernel.

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If the timing crosses a latency gate,

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assume a degraded path and fail the pod.

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Add a canary prompt set with deterministic seeds

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compare latency against the baseline window.

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If drift exceeds your tolerance,

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page production, and stop rollout,

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this might seem harsh, but the alternative is worse.

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A cluster that works while hemorrhaging time and budget.

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Lock the provider order, reject CPU fallback,

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and make the system prove it’s fast before it’s considered alive.

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Only then does green mean accelerated.

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Evidence file B, version mismatch,

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CUDA, or next tensor RT incompatibility.

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If the GPU wasn’t used, the next question is whether

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it could perform at full speed even when present.

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The evidence suggests a subtler failure.

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Versions align enough to run, but not enough

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to unlock the fast path.

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The system looks accelerated until you watch the clocks.

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Why this matters is straightforward.

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Diffusion pipelines live or die on attention performance.

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When on-ex runtime and tensor RT

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can’t load the fused kernels they expect,

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because CUDA, QDN, or tensor RT versions don’t match.

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They quietly root to generic implementations.

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The model works.

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Utilization hovers around 30% to 50%

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and latency stretches beyond budget.

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The bill looks the same, the work is slower.

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Upon closer examination, the artifacts are precise.

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Provider load logs declare success with a tell,

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falling back to default kernels, or X-formers disabled.

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You’ll see tensor RT plan deserialization fail

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with incompatible engine rebuilding,

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which triggers an on-note compile.

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Engines built on one minor version of tensor RT

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won’t deserialize on another.

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The rebuild completes, but the resulting plan may omit

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fused attention or FP16 optimizations.

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The race finishes.

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Without spikes, tensor core duty cycles stay muted.

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Here’s the counter intuitive part.

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Teams interpret it runs as its optimal.

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In this environment, nothing is accidental.

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If scale.product attention isn’t active,

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if X-formers is off if QDNN reports limited workspace performance

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collapses politely.

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The simple version is that mismatched binaries force kernels

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that use more memory movement and less math density.

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PCIe becomes visible in traces.

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Tail latencies drift as concurrency rises.

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Think of the stack as a lock set.

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Driver, CUDA, Toolkit, SUDNN, ONNX runtime build flags,

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and tensor RT, one tooth out of place, the key turns halfway.

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ORT advertises a capability graph per execution provider.

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If the compiled ORT expects CUDA 12.2,

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but the node driver exposes 12.1, CUDA loads

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with restricted features or not at all.

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If tensor RT is 8.6 on the node,

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but plans were generated with 8.4, de-serialization fails

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and regenerates with conservative tactics.

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The system prefers correctness over speed.

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Silently.

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Benchmarks prove the loss in practical terms.

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With X-formers or SDP active, diffusion attention

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drops wall clock time measurably.

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Research consistently shows 2X-5X speedups in the attention

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path depending on resolution and batch size.

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Disable them through version drift,

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and you forfeit those multipliers.

254
00:10:09,040 –> 00:10:12,840
Token merging, tome, stacks with these gains.

255
00:10:12,840 –> 00:10:14,560
Without the fused kernels, the benefit

256
00:10:14,560 –> 00:10:17,400
gets throttled by memory bandwidth and unoptimized layouts.

257
00:10:17,400 –> 00:10:20,480
The gap compounds under 10 to 24 by 10 to 24

258
00:10:20,480 –> 00:10:21,840
and higher concurrency.

259
00:10:21,840 –> 00:10:24,800
To trace the artifacts, start with capability enumeration.

260
00:10:24,800 –> 00:10:27,160
Add startup, print the exact provider list,

261
00:10:27,160 –> 00:10:28,680
and their reported features.

262
00:10:28,680 –> 00:10:32,240
Tensor RT version, FB16 and INT8 availability,

263
00:10:32,240 –> 00:10:35,000
maximum workspace, CUDNN convolution

264
00:10:35,000 –> 00:10:38,400
heuristics, NCCL presence for multi-GPU.

265
00:10:38,400 –> 00:10:41,480
For ON and X runtime, dump the EP priorities.

266
00:10:41,480 –> 00:10:45,600
Tensort, CUDA, then CPU, verify which actually binds

267
00:10:45,600 –> 00:10:46,600
to your graph nodes.

268
00:10:46,600 –> 00:10:49,360
Lock weather attention nodes are assigned to Tensor RT

269
00:10:49,360 –> 00:10:52,680
with fused kernels or to generic CUDA kernels.

270
00:10:52,680 –> 00:10:54,240
Next interrogate the environment.

271
00:10:54,240 –> 00:10:55,320
Query the driver.

272
00:10:55,320 –> 00:10:58,560
NVIDIA SMI shows the kernel module and CUDA compatibility.

273
00:10:58,560 –> 00:11:01,160
Read lip versions in the container, lip CUDAAT,

274
00:11:01,160 –> 00:11:03,880
lip CUBLAS, lip CUDNN, lipn-vin-fair.

275
00:11:03,880 –> 00:11:06,440
If the image carries CUDA 12.3 libraries,

276
00:11:06,440 –> 00:11:08,880
but the host driver supports up to 12.2,

277
00:11:08,880 –> 00:11:11,480
runtime compatibility mode may load with constraints.

278
00:11:11,480 –> 00:11:13,680
If the image expects 10 to RT 8.6 headers,

279
00:11:13,680 –> 00:11:17,280
but the node plug in delivers 8.4, API calls will degrade

280
00:11:17,280 –> 00:11:18,840
or no-obsert in optimizations.

281
00:11:18,840 –> 00:11:21,200
The remediation is a build matrix, not a wish.

282
00:11:21,200 –> 00:11:23,920
Pin exact versions in a single source of truth.

283
00:11:23,920 –> 00:11:27,120
Base image with driver compatibility, CUDA minor,

284
00:11:27,120 –> 00:11:30,800
CUDN, ORT build hash, and 10 to RT version.

285
00:11:30,800 –> 00:11:32,760
Bake inference images against that matrix

286
00:11:32,760 –> 00:11:35,040
and reject nodes that don’t match via node labels

287
00:11:35,040 –> 00:11:35,920
and admission checks.

288
00:11:35,920 –> 00:11:37,720
Pre-built and cache 10 to RT engines

289
00:11:37,720 –> 00:11:39,240
for each model variant and resolution

290
00:11:39,240 –> 00:11:41,760
on the exact 10 to RT version you deploy.

291
00:11:41,760 –> 00:11:44,720
Treat plan files as artifacts tied to the matrix.

292
00:11:44,720 –> 00:11:47,160
Never rely on on-load engine building in production.

293
00:11:47,160 –> 00:11:50,120
It masks drift and inflates cold starts.

294
00:11:50,120 –> 00:11:52,320
To make it stick, add CI smoke tests

295
00:11:52,320 –> 00:11:54,000
that assert kernel capabilities.

296
00:11:54,000 –> 00:11:55,840
Spin the container in an isolated runner

297
00:11:55,840 –> 00:11:57,720
with the target driver and verify.

298
00:11:57,720 –> 00:12:00,680
10 to RT loads, FP16 kernels used,

299
00:12:00,680 –> 00:12:05,480
attention nodes fused IO binding active, X-formers or SDP acknowledged,

300
00:12:05,480 –> 00:12:08,240
runner deterministic prompt set and fail the build

301
00:12:08,240 –> 00:12:10,440
if latency exceeds the baseline window

302
00:12:10,440 –> 00:12:13,120
or if logs contain any falling back language.

303
00:12:13,120 –> 00:12:16,240
Store the capability snapshot alongside the image digest,

304
00:12:16,240 –> 00:12:20,000
so rollbacks recover both code and performance characteristics.

305
00:12:20,000 –> 00:12:22,840
In the end, the evidence says version drift is not a bug you see.

306
00:12:22,840 –> 00:12:24,240
It’s a speed you pay.

307
00:12:24,240 –> 00:12:25,080
The system will run.

308
00:12:25,080 –> 00:12:26,920
The clocks will testify.

309
00:12:26,920 –> 00:12:29,760
Evidence file, C, container.

310
00:12:29,760 –> 00:12:32,800
Misconfiguration, efficiency erosion by design.

311
00:12:32,800 –> 00:12:34,640
Even when versions align,

312
00:12:34,640 –> 00:12:38,000
the container can sabotage efficiency from within.

313
00:12:38,000 –> 00:12:40,480
The evidence suggests a slow bleed, image bloat,

314
00:12:40,480 –> 00:12:42,680
missing GPU plumbing and allocator behavior

315
00:12:42,680 –> 00:12:44,160
that distorts latency under load.

316
00:12:44,160 –> 00:12:46,600
Nothing crashes, everything degrades.

317
00:12:46,600 –> 00:12:49,200
Why this matters is simple.

318
00:12:49,200 –> 00:12:51,680
Containers frame the runtime reality.

319
00:12:51,680 –> 00:12:54,120
If the image is obese and cold starts drag,

320
00:12:54,120 –> 00:12:56,440
replicas arrive late to the incident.

321
00:12:56,440 –> 00:12:58,840
If GPU devices aren’t mounted or the runtime

322
00:12:58,840 –> 00:13:02,200
lacks the right flags, execution providers misbehave.

323
00:13:02,200 –> 00:13:04,080
If memory arenas horde allocations,

324
00:13:04,080 –> 00:13:06,640
VRM churn triggers paging and tail spikes,

325
00:13:06,640 –> 00:13:08,000
the model looks fine.

326
00:13:08,000 –> 00:13:10,800
The container quietly taxes every request.

327
00:13:10,800 –> 00:13:14,000
Upon closer examination, artifacts accumulate at build time.

328
00:13:14,000 –> 00:13:17,640
Images exceed two gigabytes loaded with compilers, headers and test assets

329
00:13:17,640 –> 00:13:19,440
because there’s no multi-stage build.

330
00:13:19,440 –> 00:13:21,560
A missing Docker ignore invites notebooks,

331
00:13:21,560 –> 00:13:24,440
caches and experimental weights into production layers.

332
00:13:24,440 –> 00:13:27,480
Each deployment pulls gigabytes across the wire,

333
00:13:27,480 –> 00:13:31,120
scaled across node pools and cold start becomes policy.

334
00:13:31,120 –> 00:13:32,000
Not an outlier.

335
00:13:32,000 –> 00:13:33,240
The evidence isn’t mysterious.

336
00:13:33,240 –> 00:13:35,320
Docker history tells the story in layers.

337
00:13:35,320 –> 00:13:37,640
Runtime reveals the second tier of erosion.

338
00:13:37,640 –> 00:13:40,960
Without explicit GPU flags, no, GPUs in Docker

339
00:13:40,960 –> 00:13:43,720
or missing device plug-in configuration in Kubernetes,

340
00:13:43,720 –> 00:13:47,480
the process sees no hash dev on video devices.

341
00:13:47,480 –> 00:13:48,840
And video container toolkit,

342
00:13:48,840 –> 00:13:51,360
misconfigurations hide libcuda.

343
00:13:51,360 –> 00:13:53,760
So in friends, so the execution provider loads

344
00:13:53,760 –> 00:13:55,600
with constraints or not at all.

345
00:13:55,600 –> 00:13:58,920
MIG policies aren’t enforced, so workloads fight over memory slices

346
00:13:58,920 –> 00:14:00,600
in ways schedulers don’t understand.

347
00:14:00,600 –> 00:14:02,680
Logs remain polite, performance bleeds out.

348
00:14:02,680 –> 00:14:04,280
Memory behavior is the third tier.

349
00:14:04,280 –> 00:14:05,760
By default, Onyx runtime,

350
00:14:05,760 –> 00:14:08,440
scooter memory arena caches allocations aggressively,

351
00:14:08,440 –> 00:14:10,760
under concurrency that looks like stability,

352
00:14:10,760 –> 00:14:14,160
until the arena over reserves and starves new requests.

353
00:14:14,160 –> 00:14:15,560
Pinned memory isn’t set,

354
00:14:15,560 –> 00:14:18,440
so host device transfers happen through pageable buffers,

355
00:14:18,440 –> 00:14:20,240
turning PCIe into a bottleneck.

356
00:14:20,240 –> 00:14:21,840
IO isn’t bound to device,

357
00:14:21,840 –> 00:14:24,040
so tensors bounce between CPU and GPU,

358
00:14:24,040 –> 00:14:26,160
creating invisible latency taxes.

359
00:14:26,160 –> 00:14:28,720
Tail behavior worsens first, then the median follows.

360
00:14:28,720 –> 00:14:31,280
Here’s what the symptom set looks like in the wild.

361
00:14:31,280 –> 00:14:34,320
Effective throughput lags despite moderate GPU utilization.

362
00:14:34,320 –> 00:14:36,400
Latency under light load is acceptable,

363
00:14:36,400 –> 00:14:38,400
but P95 wobbles under concurrency,

364
00:14:38,400 –> 00:14:40,000
then spikes unpredictably.

365
00:14:40,000 –> 00:14:42,880
Occasional, OOM kills reset replicas at peak traffic,

366
00:14:42,880 –> 00:14:45,200
creating herd behavior, restarts cascade,

367
00:14:45,200 –> 00:14:47,600
auto scaling thrashes and queues rebuild.

368
00:14:47,600 –> 00:14:49,120
Operators chase the wrong cause,

369
00:14:49,120 –> 00:14:50,280
believing the model is heavy,

370
00:14:50,280 –> 00:14:52,320
while the container’s policies cause the collapse.

371
00:14:52,320 –> 00:14:54,240
Think of container hygiene as evidence handling.

372
00:14:54,240 –> 00:14:56,080
Multi-stage builds remove fingerprints,

373
00:14:56,080 –> 00:14:59,160
compilers and dev tools never enter the runtime.

374
00:14:59,160 –> 00:15:01,760
A distroless or slim base image narrows the surface

375
00:15:01,760 –> 00:15:03,080
and shrinks pull time.

376
00:15:03,080 –> 00:15:06,200
Docker slim or dive audits confirm what survived the build.

377
00:15:06,200 –> 00:15:09,080
A Docker ignore prevents accidental bulk.

378
00:15:09,080 –> 00:15:11,000
The result is forensic clandiness.

379
00:15:11,000 –> 00:15:13,160
What runs is only what should run.

380
00:15:13,160 –> 00:15:16,840
GPU plumbing needs explicit statements in Docker, declare,

381
00:15:16,840 –> 00:15:19,160
GPUs all or the exact mix lies,

382
00:15:19,160 –> 00:15:21,080
in Kubernetes request Nvidia.

383
00:15:21,080 –> 00:15:23,800
Com GPU resources and ensure the device plug-in

384
00:15:23,800 –> 00:15:25,560
matches your driver branch.

385
00:15:25,560 –> 00:15:27,400
At startup, assert device presence

386
00:15:27,400 –> 00:15:29,840
and driver compatibility in VDISME and LDconfig

387
00:15:29,840 –> 00:15:31,920
aren’t for decoration, they’re admission checks.

388
00:15:31,920 –> 00:15:33,640
If anything is missing or mismatched,

389
00:15:33,640 –> 00:15:35,240
log it as a violation and exit,

390
00:15:35,240 –> 00:15:38,080
then tune on extra time and tensor RT with intent.

391
00:15:38,080 –> 00:15:39,800
Lock the execution provider order

392
00:15:39,800 –> 00:15:41,880
to GPU paths only in production.

393
00:15:41,880 –> 00:15:44,840
Consider disabling or retuning the CUDA memory arena

394
00:15:44,840 –> 00:15:46,920
when it holds beyond your working set.

395
00:15:46,920 –> 00:15:50,960
Limit growth or set pre-allocation to predictable bounds.

396
00:15:50,960 –> 00:15:54,400
Enable FP16 by default when accuracy guard rails allow it.

397
00:15:54,400 –> 00:15:56,960
Gate INTA behind an accuracy test.

398
00:15:56,960 –> 00:15:58,640
Bind IO to device memory,

399
00:15:58,640 –> 00:16:00,760
so inputs arrive where computation lives

400
00:16:00,760 –> 00:16:02,360
and choose streams deliberately

401
00:16:02,360 –> 00:16:04,120
to prevent head-of-line blocking.

402
00:16:04,120 –> 00:16:06,840
Make it stick with two gates, health and performance.

403
00:16:06,840 –> 00:16:10,080
Health is not just a 200, it’s a verified capability snapshot,

404
00:16:10,080 –> 00:16:12,560
providers loaded, fused kernels present.

405
00:16:12,560 –> 00:16:15,640
Tensor cores acknowledged, IO bound.

406
00:16:15,640 –> 00:16:17,520
Performance is a baseline prompt set

407
00:16:17,520 –> 00:16:19,680
that runs warm at startup with a latency window

408
00:16:19,680 –> 00:16:21,120
and utilization floor.

409
00:16:21,120 –> 00:16:22,720
If the container can’t achieve both,

410
00:16:22,720 –> 00:16:24,360
it’s not admitted to service.

411
00:16:24,360 –> 00:16:26,080
Attach pull-size budgets to CI,

412
00:16:26,080 –> 00:16:28,600
so images that exceed thresholds fail the build.

413
00:16:28,600 –> 00:16:30,760
Keep a diff of image contents per digest,

414
00:16:30,760 –> 00:16:33,400
so rollbacks restore code and hygiene.

415
00:16:33,400 –> 00:16:35,000
The evidence suggests that containers

416
00:16:35,000 –> 00:16:36,560
don’t just deploy software,

417
00:16:36,560 –> 00:16:38,400
they encode behavior under stress.

418
00:16:38,400 –> 00:16:41,360
When they’re noisy, heavy or vague about the GPU,

419
00:16:41,360 –> 00:16:43,200
they turn acceleration into ceremony.

420
00:16:43,200 –> 00:16:44,920
When their lean explicit and assertive,

421
00:16:44,920 –> 00:16:47,000
they preserve the fast path you paid for.

422
00:16:47,000 –> 00:16:48,680
In this environment, nothing is accidental.

423
00:16:48,680 –> 00:16:51,000
The container either helps the GPU do its work

424
00:16:51,000 –> 00:16:52,320
or gets in the way.

425
00:16:52,320 –> 00:16:55,800
Forensics lab, metrics that convict, latency, throughput,

426
00:16:55,800 –> 00:16:57,960
utilization, evidence beats opinion.

427
00:16:57,960 –> 00:17:01,160
So we fix the prompt set, lock the seeds and run warm.

428
00:17:01,160 –> 00:17:05,920
Same schedulers, same steps, 2025 for 5.0.5 5.0.12,

429
00:17:05,920 –> 00:17:10,080
expanded to 50 for 10.24 by 10.24 to expose strain.

430
00:17:10,080 –> 00:17:13,000
Concurrency is held at 16 with a sweep to 32.

431
00:17:13,000 –> 00:17:15,040
Batch size starts at 1, rising carefully

432
00:17:15,040 –> 00:17:16,760
until VRAM boundaries speak.

433
00:17:16,760 –> 00:17:19,720
No extrapolation, no excuses, just clocks and counters.

434
00:17:19,720 –> 00:17:21,520
Latency testifies first.

435
00:17:21,520 –> 00:17:23,640
In the degraded state, the CPU fallback

436
00:17:23,640 –> 00:17:26,360
or generic attention path, P50 at 5.12,

437
00:17:26,360 –> 00:17:28,640
I5.12 stretches into double digits.

438
00:17:28,640 –> 00:17:30,320
P95 tells the real story.

439
00:17:30,320 –> 00:17:33,440
It wanders to 20, 40 seconds, unpredictably,

440
00:17:33,440 –> 00:17:36,000
because queues compound small inefficiencies.

441
00:17:36,000 –> 00:17:38,880
At 10.24 by 10.24 with 50 steps,

442
00:17:38,880 –> 00:17:40,920
P95 becomes a breach on arrival.

443
00:17:40,920 –> 00:17:44,560
After a mediation, GPU EP locked, fused attention active,

444
00:17:44,560 –> 00:17:48,200
I/O bound to device, P50 returns to the low single digits

445
00:17:48,200 –> 00:17:50,920
and P95 compresses inside budget.

446
00:17:50,920 –> 00:17:53,400
The range shrinks, the system becomes predictable.

447
00:17:53,400 –> 00:17:55,080
Throughput corroborates.

448
00:17:55,080 –> 00:17:58,480
At concurrency, 16, the degraded path yields a trickle.

449
00:17:58,480 –> 00:18:00,920
Images per minute barely climb with replicas

450
00:18:00,920 –> 00:18:02,640
because each instant stalls itself,

451
00:18:02,640 –> 00:18:05,560
scaling to 32 multiplies contention, not output.

452
00:18:05,560 –> 00:18:07,280
Post-fix the relationship straightens.

453
00:18:07,280 –> 00:18:09,240
Images per minute rise nearly nearly

454
00:18:09,240 –> 00:18:11,560
until the 10-so-core duty cycle saturates

455
00:18:11,560 –> 00:18:13,480
or VRAM caps the batch.

456
00:18:13,480 –> 00:18:15,040
The slope difference is the conviction.

457
00:18:15,040 –> 00:18:18,160
Work actually crosses the finish line faster, not just louder.

458
00:18:18,160 –> 00:18:19,840
Utilization closes the case.

459
00:18:19,840 –> 00:18:23,120
Before GPU duty cycle idles between percent and 50%

460
00:18:23,120 –> 00:18:27,120
with long flat valleys, CPU user time holds a suspicious plateau.

461
00:18:27,120 –> 00:18:29,880
PCIe counters show chatter from pageable transfers.

462
00:18:29,880 –> 00:18:34,040
After, the duty cycle stabilizes into a high, consistent band

463
00:18:34,040 –> 00:18:36,240
with visible tenser core engagement.

464
00:18:36,240 –> 00:18:39,160
CPU returns to orchestration and light pre-processing.

465
00:18:39,160 –> 00:18:42,520
PCIe spikes compressed because PINT memory and I/O binding

466
00:18:42,520 –> 00:18:44,560
eliminated the unnecessary traffic.

467
00:18:44,560 –> 00:18:47,000
Nothing else explains that shift except real acceleration.

468
00:18:47,000 –> 00:18:48,280
We add a stress cross check.

469
00:18:48,280 –> 00:18:51,040
Under 512, 512, steps 2025, the fixed path

470
00:18:51,040 –> 00:18:54,000
sustains concurrency 16 without tail spikes.

471
00:18:54,000 –> 00:18:56,840
Push to 32 and the system degrades gracefully.

472
00:18:56,840 –> 00:19:00,160
P95 expands predictably not chaoticly.

473
00:19:00,160 –> 00:19:04,920
Under 1024 by 1024 at 50 steps, the difference is magnify.

474
00:19:04,920 –> 00:19:06,920
The degraded path buckles into timeouts.

475
00:19:06,920 –> 00:19:09,200
The hardened path holds serviceable pifty

476
00:19:09,200 –> 00:19:13,040
and an acceptable P95 with batch 1, 2 until V-RAM boundaries 1.

477
00:19:13,040 –> 00:19:14,920
This is where arenas and streams matter.

478
00:19:14,920 –> 00:19:17,560
After tuning, head of line blocking recedes.

479
00:19:17,560 –> 00:19:19,440
The cost angle is simple arithmetic.

480
00:19:19,440 –> 00:19:22,200
Requests per GPU hour climb 2 to 5x

481
00:19:22,200 –> 00:19:25,920
when fused attention, FB16 and I/O binding are verified.

482
00:19:25,920 –> 00:19:28,920
Effective cost per 1,000 images falls accordingly.

483
00:19:28,920 –> 00:19:31,680
Cold start penalties shrink because images are slim

484
00:19:31,680 –> 00:19:33,120
and engines are pre-built.

485
00:19:33,120 –> 00:19:35,760
Notes stop paying compile tags on first touch.

486
00:19:35,760 –> 00:19:37,160
The ledger agrees with the logs.

487
00:19:37,160 –> 00:19:39,000
Verification prevents lucky runs.

488
00:19:39,000 –> 00:19:42,320
Repeat on a second node pool with a different minor driver version.

489
00:19:42,320 –> 00:19:46,720
The hardened image refuses to start on a mismatched node by design.

490
00:19:46,720 –> 00:19:49,280
So results are comparable, not confounded.

491
00:19:49,280 –> 00:19:51,160
Cross-check capability snapshots.

492
00:19:51,160 –> 00:19:55,200
Same provider order, same tensor RT, same kernel assertions.

493
00:19:55,200 –> 00:19:57,800
Re-run the prompt set and recapture the distributions

494
00:19:57,800 –> 00:20:00,480
when the histograms overlap confidence rises

495
00:20:00,480 –> 00:20:03,120
when they don’t the capability diff explains why.

496
00:20:03,120 –> 00:20:04,840
In the end, the numbers don’t argue.

497
00:20:04,840 –> 00:20:05,680
They convict.

498
00:20:05,680 –> 00:20:07,600
Latency compresses throughput scales,

499
00:20:07,600 –> 00:20:10,640
utilization stabilizes, the pathology isn’t hypothetical.

500
00:20:10,640 –> 00:20:12,600
It’s measurable before and after

501
00:20:12,600 –> 00:20:15,200
and it leaves fingerprints on every graph.

502
00:20:15,200 –> 00:20:16,720
The remedy protocol.

503
00:20:16,720 –> 00:20:18,640
A repeatable hardening checklist.

504
00:20:18,640 –> 00:20:20,360
Admission control comes first.

505
00:20:20,360 –> 00:20:24,560
Refused to start if tensor RT or CUDA execution providers aren’t present.

506
00:20:24,560 –> 00:20:28,400
Enumerate providers verify device count, print capability snapshots,

507
00:20:28,400 –> 00:20:31,600
QDNFB16, INT8 workspace,

508
00:20:31,600 –> 00:20:33,360
if anything’s missing exit non-zero.

509
00:20:33,360 –> 00:20:35,480
Availability follows integrity.

510
00:20:35,480 –> 00:20:37,200
Version pinning is the spine.

511
00:20:37,200 –> 00:20:39,720
Maintain a single matrix driver branch CUDA minor,

512
00:20:39,720 –> 00:20:42,760
CUDA and own an X runtime build hash tensor RT,

513
00:20:42,760 –> 00:20:45,240
build inference images against that matrix,

514
00:20:45,240 –> 00:20:48,760
label nodes to match gate admission on equality,

515
00:20:48,760 –> 00:20:51,720
pre-built tensor RT engines per model and resolution.

516
00:20:51,720 –> 00:20:54,520
Plans are artifacts, not runtime guesses.

517
00:20:54,520 –> 00:20:56,280
Container hygiene preserves truth.

518
00:20:56,280 –> 00:20:57,960
Use multistage builds.

519
00:20:57,960 –> 00:21:01,880
Keep the runtime distrollers or slim, include a strict Docker ignore.

520
00:21:01,880 –> 00:21:04,360
Verify with Docker history and a slimming audit.

521
00:21:04,360 –> 00:21:07,720
Set pull size budgets in CI, fail images that bloat.

522
00:21:07,720 –> 00:21:10,200
Sign images and diff contents per digest.

523
00:21:10,200 –> 00:21:13,720
Configure GPU first, log-ep order to tensor RT CUDA,

524
00:21:13,720 –> 00:21:17,960
disable CPU provider in production, assert dev and video presence

525
00:21:17,960 –> 00:21:20,280
and Nvidia container toolkit version.

526
00:21:20,280 –> 00:21:24,280
Requestnvd.com GPU explicitly or mixlices and verify its start up.

527
00:21:24,280 –> 00:21:25,640
Tune the runtime.

528
00:21:25,640 –> 00:21:27,800
Enable FP16 by default.

529
00:21:27,800 –> 00:21:30,440
Gate INT8 with accuracy checks.

530
00:21:30,440 –> 00:21:33,160
Bind IO directly to device memory,

531
00:21:33,160 –> 00:21:37,480
enable PINNED memory, choose streams to avoid head-of-line blocking.

532
00:21:37,480 –> 00:21:41,720
Retune or disable the ORT CUDA arena when it horts beyond the working set.

533
00:21:41,720 –> 00:21:45,320
Set performance as low as code, start up self-test with deterministic prompts,

534
00:21:45,320 –> 00:21:47,400
latency window and utilization floor.

535
00:21:47,400 –> 00:21:49,080
Fail on falling backlogs.

536
00:21:49,080 –> 00:21:52,360
Add observability, per request GPU matrix,

537
00:21:52,360 –> 00:21:56,200
degraded path alerts, canary prompts and diffable baselines.

538
00:21:56,200 –> 00:22:00,520
Roll out blue green with performance gates and automatic rollback on breach.

539
00:22:00,520 –> 00:22:05,000
With the protocol enforced, acceleration is provable, not assumed.

540
00:22:05,000 –> 00:22:06,440
The lesson is clinical.

541
00:22:06,440 –> 00:22:10,440
Silent CPU fallback, version drift and container bloat aren’t bugs.

542
00:22:10,440 –> 00:22:12,680
They are predictable failure patterns you can block.

543
00:22:12,680 –> 00:22:16,120
Run the protocol, instrument GPU utilization, refuse degraded paths,

544
00:22:16,120 –> 00:22:18,200
PINN version’s treat engines as artifacts.

545
00:22:18,200 –> 00:22:22,360
If you want the deeper dive into ONNX runtime and tensor RT memory behavior

546
00:22:22,360 –> 00:22:27,640
and when to disable arenas, watch the next case in this series and subscribe for the lab notes.