Before moving an AI model to another GPU, NPU, TPU, or other inference accelerator, verify the entire destination stack—not just whether the operating system can see a device. Freeze a working baseline, identify the exact hardware and software combination, audit every model operation and data type, determine whether artifacts can be reused, prove output quality, benchmark realistic traffic, and test deployment and rollback.
There is no universal compatibility guarantee. Support depends on the model architecture, operators, custom kernels, precision, compiler or inference engine, drivers, runtime, serving layer, and the particular accelerator. The checks below turn a risky replacement into a controlled engineering experiment.
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Start with a migration gate, not a device purchase
Use these gates in order. A failure at an earlier gate makes later performance numbers misleading.
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- Baseline frozen: model, tokenizer or preprocessing, artifact hashes, input shapes, output expectations, precision, traffic profile, and current latency and throughput are recorded.
- Destination named: accelerator model and form factor, host, operating system, driver, runtime, framework build, compiler or engine, and serving software are identified by version.
- Coverage confirmed: architecture, operators, custom extensions, dtypes, quantization, dynamic shapes, and preprocessing are supported or have an intentional fallback plan.
- Artifact path known: you know whether the checkpoint, exported graph, serialized engine, or compiled binary can be loaded directly or must be converted and rebuilt.
- Correctness passed: representative and edge-case inputs meet application-specific numerical and functional tolerances.
- Workload benchmarked: latency percentiles, throughput, memory, warm-up, batching, and concurrency are measured under equivalent conditions.
- Operations accepted: startup, health checks, monitoring, scaling, failure recovery, capacity, and rollback work on the target stack.
Do not route full production traffic to the new accelerator until all seven gates pass.
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1. Freeze a known-good baseline
Capture the model and input contract
Record the exact model architecture and revision, checkpoint or exported-artifact hash, tokenizer, feature extraction code, preprocessing and postprocessing versions, and any custom layers. Document every input tensor’s name, shape, layout, data type, normalization, and realistic value distribution. Include variable-length or dynamic-shape behavior rather than testing only one convenient shape.
Write down what constitutes an acceptable result: class labels, scores, generated text, embeddings, detection boxes, or other outputs. Define numerical tolerances per output rather than relying on a vague visual comparison. Save a set of ordinary production examples, boundary values, malformed requests, long or short sequences, and any regulated or safety-critical cases. Keep their expected outputs from the current deployment.
Record the serving baseline
Measure the current system with the same observability you will use after migration. Capture request latency percentiles, throughput, queue time, batch size, concurrency, memory consumption, startup and warm-up time, and error rate. For generative models, record time to first token and inter-token latency in addition to end-to-end request time. Note autoscaling settings, number of model instances, worker processes, and any CPU preprocessing that is part of the request path.
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|---|---|
| Model artifact | Revision, hash, export format, and conversion options |
| Inputs | Shapes, distributions, sequence lengths, layouts, and preprocessing version |
| Outputs | Reference results, tolerance rules, and postprocessing behavior |
| Performance | Latency percentiles, throughput, batch, concurrency, warm-up, and memory |
| Environment | Hardware, driver, runtime, framework, engine, serving configuration, and instance count |
2. Identify the complete destination stack
Name the target precisely. “A newer GPU” or “an accelerator-enabled container” is not enough for a reproducible migration. Record the accelerator model, memory size, interconnect or form factor, host CPU and system memory, operating system, driver, device runtime, framework build, compiler or inference engine version, serving layer, container image, and orchestration settings.
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Check the target platform’s current support matrix for that exact combination. Framework support for one accelerator does not imply support for another, even when both expose a similar programming API. PyTorch’s torch.accelerator.is_available documentation defines runtime availability as follows: Check if the current accelerator is available at runtime: it was built, all the required drivers are available and at least one device is visible.
A visible device therefore proves only an initial prerequisite, not that your model can execute efficiently.
Operating-system and device prerequisites can also be backend-specific. For example, PyTorch’s MPS documentation lists its own OS and hardware requirements and notes operation or data-type limitations. Treat those requirements as examples of why the destination backend’s documentation must be checked directly; do not generalize MPS behavior to another accelerator.
Make a version-locked inventory
| Layer | Questions to answer |
|---|---|
| Hardware | Which exact accelerator, memory capacity, host, interconnect, and deployment form factor? |
| System | Which operating-system release, kernel, container runtime, and permissions? |
| Device software | Which driver, firmware, device runtime, and supported compute capability? |
| Framework | Which framework build and accelerator backend were installed? |
| Compiler or engine | Which compiler, graph optimizer, inference engine, flags, and precision settings? |
| Serving | Which server version, model repository format, workers, batching, and health checks? |
3. Audit model, operator, and feature coverage
Inventory the executable graph
List the model’s layers and operators after export, not only those in the original training code. Include custom C++ or CUDA-style extensions, fused kernels, plugins, control flow, sparse or mixture-of-experts routing, dynamic dimensions, random-number operations, and any collective communication required across devices. Check preprocessing and postprocessing too: tokenization, image decoding, resizing, normalization, non-maximum suppression, sampling, and decoding can remain on the CPU or fail independently of the neural-network graph.
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Verify support for every precision used in the path, including input and output types, accumulation type, mixed-precision rules, calibration format, and quantization scheme. A backend may accept a model in one format but emulate an operation in another, silently insert conversions, or reject a quantized layer. Confirm whether per-tensor and per-channel scales, activation calibration, weight-only quantization, sparsity, or low-bit formats are supported by the chosen engine.
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Understand fallback behavior
For each unsupported operation, determine whether compilation fails, execution falls back to the CPU, a slower backend kernel is selected, or a custom implementation is required. ExecuTorch’s backend overview emphasizes that backends have their own hardware requirements and operator coverage, and that unsupported operators may run on the CPU. CPU fallback can preserve functional correctness while adding transfers and synchronization that destroy latency or throughput targets. Measure fallback cost rather than treating it as a harmless compatibility detail.
Test representative shapes and multi-device behavior
Compile and run the shortest and longest realistic sequences, smallest and largest image or audio shapes, empty or padded batches, and unusual but valid dimensions. If the service shards a model, uses tensor or pipeline parallelism, or relies on collectives, verify communication libraries, topology, and failure behavior on the target host. A single-device smoke test cannot validate a distributed serving path.
4. Decide whether artifacts can be reused
Separate the source checkpoint, exported graph, optimized graph, serialized inference engine, and serving repository. Ask the destination runtime which of these it consumes. Some migrations can load the original checkpoint; others require an export to an intermediate format, backend-specific conversion, compilation, calibration, or plugin replacement.
- Attempt a documented load: use the destination runtime’s supported import path and record the exact command, flags, and warnings.
- Export when required: produce a deterministic graph with fixed tool versions, opset or schema version, dynamic-shape profiles, and external data files captured.
- Convert and compile: save compiler, engine, plugin, calibration, and optimization settings in source control or a reproducible build specification.
- Inspect the result: confirm that all nodes are assigned to the intended accelerator and identify inserted casts, transfers, or CPU partitions.
- Package the runtime: keep the engine, runtime libraries, driver requirements, and model repository together so another host can reproduce the load.
Serialized engines are often tied to runtime and hardware capabilities. NVIDIA TensorRT’s compatibility documentation describes version and GPU checks during engine deserialization; a mismatch can prevent an engine from loading. Compatibility options may broaden where an engine can run but can reduce performance in some circumstances. Treat a successfully built engine as a release artifact with an explicit compatibility scope, not as a universally portable file.
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5. Prove correctness before optimizing speed
Run the reference corpus through the old and new paths and compare outputs by application-appropriate rules. Exact equality is reasonable for some deterministic integer operations but not for many floating-point or generative workloads. Define maximum absolute and relative error, cosine similarity, top-k agreement, ranking stability, detection overlap, or task-level accuracy before testing.
Include cases that expose conversion bugs
- Normal production inputs across the full shape and length distribution.
- Minimum and maximum dimensions, padding patterns, empty fields, and long sequences.
- Values near activation, clipping, normalization, and quantization boundaries.
- Out-of-range, malformed, or adversarial inputs handled by the service contract.
- Deterministic seeds and sampling settings where reproducibility is expected.
Repeat the comparison after every material change: export, compiler version, engine rebuild, precision reduction, quantization calibration, plugin replacement, or backend substitution. A model that loads and produces plausible output has not yet demonstrated parity. NVIDIA’s documented production workflow treats post-conversion accuracy validation as a distinct step before deployment.
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Run the same model revision, input corpus, output limits, preprocessing, postprocessing, batch policy, and traffic pattern against the old and new stacks. Warm up both systems, then report steady-state and cold-start behavior separately. Keep clocks, power settings, host contention, and data-transfer paths as consistent as practical.
| Measure | Why it matters |
|---|---|
| p50, p95, and p99 latency | Shows typical and tail user experience; averages hide queueing and synchronization problems. |
| Throughput | Requests or tokens per second at a stated batch and concurrency. |
| Memory | Peak device and host usage, fragmentation, and headroom for real requests. |
| Startup and warm-up | Determines rollout time, autoscaling responsiveness, and recovery behavior. |
| Batch and concurrency scaling | Reveals whether gains hold at the service’s actual traffic level. |
| Errors and fallbacks | Exposes timeouts, out-of-memory events, retries, and CPU partitions. |
Vary batch size, concurrent model instances, sequence or image shape, precision, and request concurrency as separate experiment dimensions. NVIDIA’s Triton workflow specifically calls for testing batch size and concurrent model instances, while its LLM deployment guidance identifies architecture compatibility, quantization format, and performance characteristics as backend-selection factors. Those are variables to measure, not guarantees that a result transfers to another model or vendor.
For language-model services, report time to first token, inter-token latency, generated-token throughput, prompt length, output limit, and any continuous-batching policy. For non-generative inference, report request latency, effective throughput, and the distribution of input shapes. Attach the complete configuration to every result so a speedup can be reproduced rather than merely quoted.
7. Verify memory fit and production behavior
Capacity and lifecycle
Load the largest supported artifact and realistic concurrent requests, including activation memory, caches, workspace allocations, communication buffers, and fragmentation. Test repeated unload and reload cycles, rolling updates, and autoscaler scale-out. A model that fits in a one-request test may fail when several instances initialize simultaneously.
Health, observability, and failures
Exercise readiness and liveness checks while the engine compiles or warms up. Confirm that metrics distinguish queue time, preprocessing, device execution, postprocessing, memory pressure, fallback execution, and transport errors. Force device errors, host restarts, invalid requests, timeout cancellation, and partial replica loss. Verify that alerts fire and that the service returns a controlled error instead of hanging or retrying indefinitely.
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Use the same image-signing, secret-management, network, and filesystem controls as production. Pin driver and runtime packages, retain build logs, and record model and engine hashes. Validate that the target host can pull the required artifacts without an undocumented manual step.
8. Compare two target accelerators with one decision matrix
When evaluating more than one destination, keep the workload and acceptance criteria fixed. Score each target on the dimensions below rather than selecting on peak theoretical compute.
| Decision axis | Evidence to collect |
|---|---|
| Model and operator coverage | Unsupported nodes, custom-kernel work, CPU partitions, and dynamic-shape limits |
| Artifact portability | Conversion steps, build time, compatibility scope, and reproducibility |
| Correctness | Error and task-level results at each precision and quantization setting |
| Service performance | Tail latency, throughput, batching, concurrency, and generative-token metrics |
| Capacity | Peak memory, instance density, startup, warm-up, and scaling headroom |
| Operations | Monitoring, upgrades, incident recovery, deployment complexity, and rollback time |
| Supportability | Documented framework, driver, compiler, and serving combinations |
Choose the target that meets the service’s correctness and SLO requirements with an operationally repeatable path. A higher benchmark number is not an improvement if conversion is fragile, memory headroom disappears, or rollback cannot be completed within the incident budget.
9. Run a staged cutover and keep rollback real
- Build a parallel environment: deploy the target driver, runtime, engine, model artifact, and serving configuration without replacing the known-good stack.
- Run offline parity tests: compare the saved reference corpus, edge cases, and all required precisions.
- Load-test the service: reproduce batch, concurrency, shape, and traffic distributions, including warm-up and restart tests.
- Shadow or canary: send mirrored or limited traffic while keeping the old path authoritative. Compare outputs, tail latency, errors, memory, and capacity.
- Define acceptance thresholds: use the service’s own numerical tolerances, latency SLOs, throughput requirement, error budget, and recovery objective. Official accelerator documents do not provide one vendor-neutral pass/fail threshold for every workload.
- Promote gradually: increase traffic only while dashboards and alerts remain healthy.
- Rollback deliberately: retain the old image, engine, drivers, model repository, and routing configuration; rehearse switching back and verify that the old capacity is still available.
Common migration failures and their fixes
- Device is visible but the backend is unavailable: install a framework build compiled with the required backend, align drivers and runtime versions, and verify that at least one device is visible to the process.
- Engine will not deserialize: rebuild for the target runtime or hardware capability, and check documented compatibility settings before shipping the artifact.
- Conversion succeeds but latency worsens: inspect CPU fallback, inserted data transfers, synchronization, and unsupported dynamic shapes; then measure a supported precision or replace the problematic kernel.
- Accuracy drops after quantization: review calibration data and scales, isolate sensitive layers, test a higher-precision path, and rerun task-level validation.
- Single-request tests pass but production fails: reproduce concurrent initialization, realistic batch sizes, long inputs, memory fragmentation, and autoscaling behavior.
- Benchmark cannot be reproduced: attach model hash, tokenizer, input distribution, accelerator, driver, runtime, engine, precision, batch, concurrency, and serving settings to the result.
- Rollback is unavailable during an incident: preserve the previous artifacts and capacity, test the routing switch, and set a measured rollback time before cutover.
Final pre-migration checklist
Approve the move only when the answer to each item is documented:
Quick Recap
- Is the exact destination hardware and software stack version-locked?
- Are every critical operator, custom kernel, dtype, quantization mode, and preprocessing step supported?
- Is CPU fallback identified, measured, and acceptable?
- Is the artifact reuse, conversion, or rebuild process reproducible?
- Do representative and edge-case outputs meet defined tolerances?
- Do realistic latency percentiles, throughput, memory, startup, and concurrency meet the service SLO?
- Have monitoring, scaling, failure recovery, security controls, and rollback been exercised?
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