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If your startup cannot obtain enough GPUs, do not start by buying whatever card is available. First separate experimentation, training and fine-tuning, production inference, and batch jobs; measure each workload’s actual memory, latency, throughput, reliability, and scheduling needs; then combine managed inference, multiple capacity sources, reservations, and interruptible capacity where each is appropriate.
This approach prevents an expensive mismatch: a GPU configuration that is excellent for distributed training may be wasteful for inference, while a low-cost interruptible instance may be unsuitable for a customer-facing endpoint.
Start by defining the demand, not the GPU model
GPU shortages become easier to manage when “we need more GPUs” is converted into separate, measurable requests. Microsoft’s startup guidance recommends treating AI compute as distinct workloads and setting explicit inference targets for latency, throughput, reliability, region, and cost per request. See Microsoft’s workload compute-path guidance (updated September 2, 2026).
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- Experimentation and evaluation: interactive notebooks, prompt tests, ablations, and model evaluations. These usually benefit from flexible scheduling and short queue times rather than guaranteed 24-hour availability.
- Training and fine-tuning: distributed or single-node jobs whose duration, checkpoint frequency, interconnect needs, and restart cost must be known before selecting capacity.
- Production inference: online requests with defined latency, throughput, uptime, data-residency, and cost-per-request targets. Separate urgent customer traffic from asynchronous work.
- Batch processing: embeddings, offline scoring, synthetic-data generation, and other jobs that can often wait, run in waves, or resume after interruption.
Build a demand worksheet
For each workload, record the following before contacting a provider. Use measured values where possible and clearly mark estimates that still need a benchmark.
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| Record | Why it changes the capacity decision |
|---|---|
| Model, precision, context length, and peak memory | Determines whether the job fits on one accelerator, needs sharding, or requires a particular memory capacity. |
| Number and type of accelerators | Distributed training can depend on GPU-to-GPU interconnect and topology; inference may need only a fraction of a device. |
| Expected utilization and concurrency | Low utilization can make a dedicated server more expensive than managed or shared inference. |
| Latency, throughput, and uptime targets | These distinguish a customer-facing service from a queue-based batch job. |
| Acceptable queue time and delivery date | Lets you decide whether a reservation is justified or a flexible queue is acceptable. |
| Data location and residency requirements | Eliminates regions or services that cannot legally or operationally handle the data. |
| Checkpoint and interruption behavior | Determines whether Spot or other interruptible capacity can be used safely. |
Keep production demand separate from experiments and schedule non-production work when practical. A single blended forecast hides which jobs truly require guaranteed capacity.
Reduce or reshape GPU demand before adding capacity
Move suitable serving to a managed path
Not every inference workload needs a startup-operated GPU server. A managed model API can be appropriate for standard models or variable traffic; a managed open-model deployment can be preferable when you need control over weights or runtime without owning the underlying machines. Microsoft’s guidance also describes managed inference GPU paths, but identifies some Managed Compute in Foundry capabilities as preview and not recommended for production while in preview. Check the current service status, supported regions, quota, and commercial terms before committing.
Managed services trade infrastructure control for faster operations. Confirm model and framework compatibility, maximum context or payload limits, observability, scaling behavior, data handling, and the provider’s availability commitments. Benchmark a representative request mix rather than assuming a managed endpoint will be cheaper or faster.
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The accelerator and software stack that best serves a low-latency model may be a poor fit for distributed training. For training and fine-tuning, evaluate checkpoint time, restart cost, interconnect, storage throughput, and whether the framework supports the candidate accelerator. For inference, measure tokens or requests per second at the required concurrency and tail latency. Keep those results in separate cost models.
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Make flexible jobs interruption-tolerant
Batch jobs and some experiments can use smaller or alternative compatible accelerators if the code can checkpoint and resume. Schedule them into available windows, limit concurrent experiments, and make artifacts reproducible so a preempted run does not lose an entire day. These are options to benchmark, not guaranteed performance or savings.
Use more than one route to obtain capacity
NVIDIA Compute MatchMake
NVIDIA Compute MatchMake connects eligible AI companies with NVIDIA Cloud Partners. You can browse by GPU platform, region, and dates, submit a capacity request, and proceed through matching and contracting. The marketplace currently lists platforms including H100, H200, GB200, GB300, B200, B300, RTX 6000 Pro, and Vera Rubin NVL72.
A request is not a guarantee of inventory, acceptance, price, or delivery date. Filter by the region and dates you actually need, describe the workload and quantity accurately, and ask partners to propose compatible alternatives instead of insisting on an exact SKU that your benchmark does not require.
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Check your existing cloud account
If you already use a major cloud, inspect account-level GPU quotas, regional instance availability, managed machine-learning capacity, reservation products, and the provider’s support route. A GPU type shown on a public page may still be unavailable to your account or region. Ask support to confirm quota increases, expected fulfillment, networking limits, storage throughput, and any contract or minimum-spend requirement in writing.
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Consider a second provider for a defined workload
A second provider can reduce single-region risk, but portability has a cost. Validate container images, driver and CUDA compatibility, networking, object-storage transfer, secrets, monitoring, and data egress before moving a production job. Keep a small, repeatable benchmark suite so a proposed alternative can be accepted or rejected quickly.
Match the capacity contract to the job
A useful distinction is guaranteed capacity for a known critical window versus lower-cost capacity that may disappear. AWS documents several options in its GPU capacity and cost guidance (published June 23, 2025).
| Capacity approach | Use it when | Important limitation |
|---|---|---|
| Availability Zone On-Demand Capacity Reservation | A production or launch window needs capacity in a specific availability zone. | Availability, quota, region, and account terms must be confirmed; a reservation does not solve an incompatible model or software stack. |
| Capacity Blocks for ML | You have a defined short training or batch window. | AWS describes blocks of 1 to 14 days; current regional availability and booking terms are required. |
| Spot instances | Experiments, batch processing, or training that can checkpoint and restart. | Unused capacity can be interrupted. The workload must save progress frequently and tolerate replacement capacity. |
| Managed training or inference | You want the provider to operate much of the provisioning and serving workflow. | Supported frameworks, regions, quotas, endpoint behavior, and economics still require validation. |
Do not interpret a published option as proof that GPUs are immediately available in your account. Confirm live inventory, reservation windows, and cancellation or interruption behavior before promising a customer a date.
Evaluate alternatives beyond conventional GPUs
AWS describes managed training and inference plus its Trainium and Inferentia accelerators. Microsoft describes managed API inference, open-model inference, managed inference GPUs, private-preview training options, and a Dapple partnership for very large-scale training or GPU batch. Availability, preview status, framework support, and contract terms vary.
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Run a representative benchmark before switching hardware. Include model load time, steady-state throughput, p50 and tail latency, maximum batch or sequence length, startup time, failure recovery, and engineering effort to port kernels or operators. Compare cost per useful training run or cost per successful request, including storage, data movement, idle reservations, and operations—not merely the hourly accelerator rate. Vendor-reported performance or savings figures are conditional examples, not industry-wide results.
Choose a path with a decision matrix
Score each candidate against the same questions rather than choosing by GPU name alone.
| Decision axis | Questions to answer |
|---|---|
| Delivery confidence | When can usable capacity be provisioned, and what evidence supports that date? |
| Technical fit | Does memory, interconnect, driver, framework, and model architecture support the workload? |
| Operations | Who handles provisioning, patching, autoscaling, monitoring, incident response, and replacement? |
| Service behavior | What latency, throughput, uptime, queueing, and scaling behavior is documented? |
| Location and compliance | Can data remain in the required region and under the required contractual controls? |
| Economics | What is the cost per useful run or request after transfer, storage, idle time, and engineering work? |
| Portability | How difficult is it to move the container, weights, data, and observability to another provider? |
| Failure handling | Can the job checkpoint, retry, drain traffic, and recover from preemption or an unavailable zone? |
Microsoft specifically advises checking service status, supported regions, quotas, and commercial terms before commitment. Treat preview services and private-preview training options as experiments until their production status and support commitments are confirmed.
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Share GPUs only when isolation and latency allow it
GPU sharing can increase utilization, but it is not a universal shortage remedy. NVIDIA’s vGPU inference-mode documentation distinguishes three patterns where supported: time-sliced vGPU, MIG-backed vGPU, and time-sliced MIG-backed vGPU.
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Time-sliced vGPU
Time slicing lets multiple tenants take turns on a GPU and can improve density. It also makes performance depend on neighbors and scheduling. A busy shared device can be a poor fit for a strict per-VM latency service-level objective.
MIG-backed vGPU
Multi-Instance GPU-backed virtual machines use hardware-partitioned instances for stronger isolation and more predictable bandwidth between tenants. This is the documented choice when tenant isolation or predictable performance matters more than maximum flexible density.
Verify the exact configuration
Support depends on the specific GPU, hypervisor, guest operating system, driver, and scheduling mode. Confirm those combinations before designing a multi-tenant service, and load-test noisy-neighbor behavior at your target concurrency.
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A practical 30-day response plan
Days 1–3: establish the baseline
- List every experiment, training run, inference endpoint, and batch pipeline.
- Record model memory, accelerator count, utilization, data region, queue tolerance, latency and throughput targets, uptime requirement, and checkpoint capability.
- Mark which workloads are customer-facing and which can wait or be interrupted.
Days 4–10: reduce unnecessary demand
- Test whether managed API or managed open-model inference meets the production target.
- Cap experiment concurrency and schedule non-production work outside critical windows.
- Add checkpointing, retry, and resumable artifacts to training and batch pipelines.
- Benchmark at least one compatible alternative accelerator or smaller configuration.
Days 11–20: solicit capacity in parallel
- Submit an accurate request through NVIDIA Compute MatchMake if NVIDIA-partner capacity fits the workload.
- Ask your current cloud provider to verify quotas, regional inventory, managed ML capacity, and reservation options.
- Request a second-provider quote for the same benchmark and delivery window.
- Get pricing, region, support level, preview status, interruption policy, and contract terms documented.
Days 21–30: prove the fallback
- Run the representative benchmark on the preferred and fallback paths.
- Measure useful-run or successful-request cost, tail latency, throughput, startup time, and recovery after interruption.
- Deploy a small non-critical workload on the fallback path and rehearse moving traffic or restarting a checkpoint.
- Reserve only the capacity supported by measured demand and a clearly defined business window.
Common failure modes and fixes
- Choosing a famous GPU without a workload specification: replace the SKU-first request with memory, interconnect, latency, throughput, and region requirements.
- Putting experiments in the production queue: isolate accounts, projects, quotas, or schedulers and reserve guaranteed capacity only for customer traffic.
- Using Spot for a job that cannot restart: add frequent checkpoints and idempotent retries, or move the critical window to reserved capacity.
- Assuming managed inference removes all compatibility work: verify model format, operators, tokenizer behavior, scaling limits, observability, and data controls.
- Sharing a GPU under a strict latency promise: use hardware-isolated MIG-backed instances where supported, or dedicate capacity and test tail latency.
- Believing a marketplace request guarantees delivery: treat matching as a lead, then confirm partner acceptance, inventory, dates, price, and contract.
- Ignoring egress and idle time: include transfer, storage, reserved-but-unused hours, and engineering work in the cost per useful outcome.
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