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1. Describe the workload before choosing hardware
Training and inference stress infrastructure differently, and even two inference services can have opposite requirements. Write a workload brief that turns business targets into measurable engineering constraints. Microsoft’s Azure compute guidance says VM size should align with model complexity, data size and cost; the same principle applies to on-premises servers and other clouds. Start by recording:
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- Workload type: pretraining, continued pretraining, fine-tuning, batch inference, or interactive/online serving.
- Model architecture and parameter count, context-window length, expected prompt and output lengths, and whether retrieval-augmented generation is used.
- Numerical format: full precision, mixed precision or quantization, including any quality constraints that limit compression.
- Peak and sustained throughput, maximum acceptable first-token and end-to-end latency, concurrency, and burst pattern.
- Available accelerator memory, host RAM, local storage and object-storage bandwidth.
- Data location, sovereignty, encryption, retention, isolation and audit requirements.
- Availability target, maintenance window, disaster-recovery expectation and budget model.
- Frameworks, drivers, operating system, container runtime, Kubernetes or scheduler version and support lifecycle.
Pretraining and large distributed training
Pretraining usually runs for long periods at high utilization. The limiting factors are aggregate accelerator memory, memory bandwidth, accelerator-to-accelerator links, host-to-host networking and checkpoint storage. Sequence length, batch size and the parallelism strategy can increase memory and communication requirements sharply. Include RDMA-capable networking and a high-speed accelerator fabric when the model is split across devices or hosts. A system that has impressive peak compute but weak communication can deliver poor useful-work throughput.
Fine-tuning and customization
Fine-tuning may fit on far less hardware than pretraining, especially with parameter-efficient methods or quantization, but the data pipeline, evaluation jobs and experiment concurrency still matter. If teams need managed runs, experiment tracking, model registration, versioning and governance, Azure Machine Learning is positioned for custom training and lifecycle operations. For managed foundation-model customization and deployment, Microsoft Foundry emphasizes reduced infrastructure work with integrated safety and governance controls. Select those services only when their supported models, regions, data controls and integration points match your requirements.
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Batch inference
Batch jobs optimize cost per item and can often tolerate queueing. Measure records or tokens processed per hour, completion deadline, retry behavior and accelerator utilization. A smaller fleet scheduled during off-peak periods may beat a low-latency design, while CPU inference can be appropriate for small models when the target latency allows it.
Interactive and online serving
Online APIs are governed by tail latency, concurrency, autoscaling behavior and availability. KV-cache memory can exceed the model-weight footprint for long contexts, so size for the worst supported prompt and output lengths rather than average requests. Keep capacity for failover and rolling upgrades; a cluster that meets average throughput but exhausts memory during a traffic spike will violate its service target.
2. Size memory, then choose topology
First determine whether the complete model and runtime state fit on one accelerator. Include model weights, activations, KV cache, framework overhead, communication buffers and headroom for fragmentation. If they fit, start with the simplest single-accelerator or single-host deployment. Simpler topology usually means fewer failure domains, easier debugging and less communication overhead.
A worked memory example
Google Cloud’s GKE inference guidance gives an illustrative workload requiring 260 GB of total accelerator memory: 200 GB for the model, 50 GB for KV cache and 10 GB of overhead. This is a worked example, not a typical enterprise-model statistic. A single 24 GB device cannot satisfy it; even a device with the exact aggregate capacity may need additional headroom and a topology that supports efficient sharding. Treat the numbers as a sizing method, then substitute your measured model and context profile.
Topology decision table
| Requirement | Starting topology | What to validate |
|---|---|---|
| Model and runtime fit on one accelerator | One accelerator | Memory headroom, latency, sustained utilization and failure recovery |
| Model needs several accelerators in one server | Single host with a high-bandwidth accelerator interconnect | Peer-to-peer bandwidth, topology-aware framework behavior, host PCIe layout and aggregate memory |
| Model or throughput target exceeds one host | Multi-host cluster | RDMA or equivalent network, collective-communication performance, sharding strategy, congestion and restart time |
| Small model with modest latency | CPU or small accelerator | Per-request latency, concurrency, cost per item and quality at the selected precision |
Google Cloud groups accelerator choices into small, single-host large and multi-host large inference profiles and ties parallel strategies to topology. Its current examples include L4 and G2 for small inference and retrieval-augmented generation, A100, H100 and B200 for single-host large-model inference, and H200, B200/GB200 and TPU families for selected multi-host profiles. These are examples within Google’s environment, not universal winners; confirm current machine availability and supported configurations before basing a design on a named SKU.
When a cluster is unavoidable
Use tensor, pipeline, sequence or data parallelism only when the workload requires it or when measured throughput justifies the added complexity. Model sharding creates synchronization traffic, so accelerator count alone does not predict performance. Test the framework’s behavior on the exact host layout and network, including startup, checkpoint, scale-out and failure-recovery paths.
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3. Separate training networking from inference networking
Distributed training repeatedly exchanges gradients and parameters. High-speed GPU-to-GPU links inside a host and RDMA-capable host networking between servers can materially affect time to train. Microsoft’s Azure recommendations favor ND-family VMs for training and call for RDMA and GPU interconnect support; NC-family VMs connected with Ethernet are presented as an alternative in that Azure context.
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Inference has a different trade-off. A replicated online service can keep each request on one host, and Microsoft notes that inference generally does not need InfiniBand. Do not automatically pay for training-oriented fabric on an inference fleet. If inference shards a single model across hosts, however, measure token latency and network overhead; the “inference does not need InfiniBand” rule is not a license to ignore communication for multi-host serving.
Network checks for distributed jobs
- Confirm the supported RDMA technology, rail count, bandwidth and topology.
- Measure collective operations with the same container, driver and framework versions used in production.
- Check oversubscription, noisy neighbors, placement guarantees and cross-zone charges in cloud environments.
- Include checkpoint storage throughput and recovery time; a fast training step is not useful if checkpoints stall or cannot be restored.
4. Choose an operating model your team can support
Infrastructure is an operating commitment, not only a hardware purchase. Microsoft summarizes the decision clearly: “Platform decisions are trade-offs, not absolutes, and should be guided by workload demands, scale requirements, and the maturity of the teams supporting them.” The statement appears in Microsoft Learn’s platform-selection guidance, updated 2026-09-18.
Managed model platforms
A managed foundation-model service can reduce cluster, driver and scheduler work and may provide integrated safety, governance and deployment controls. It is a strong fit when the organization uses supported foundation models and values speed over low-level control. Check model availability, fine-tuning method, region, private-network options, data-use terms and export paths before committing.
Managed machine-learning platform
For custom models, a managed machine-learning service can provide jobs, experiment tracking, pipelines, model registry, versioning and managed compute. Azure Machine Learning is Microsoft’s example in this category. Confirm that it supports your framework, accelerator shape, networking and identity model, and price the control-plane and data-transfer charges as well as compute.
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Teams with established schedulers may prefer a cluster service that preserves queueing, topology and custom image control. Azure CycleCloud is described for Slurm, PBS Professional, IBM Spectrum LSF, Grid Engine and HTCondor environments. This path suits organizations that already operate HPC practices, but it requires ownership of images, drivers, quotas, monitoring, upgrades and capacity planning.
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Kubernetes and portability
AKS and partner Kubernetes solutions can standardize containerized workloads across Azure, on-premises systems and other clouds. Portability is valuable when data residency, procurement or capacity changes, but Kubernetes transfers more responsibility to your team: GPU operators, device plugins, scheduling, autoscaling, upgrades, observability and incident response. Define which layer is managed and which is yours.
On-premises and hybrid
On-premises systems can provide predictable locality, isolation and amortization for steady utilization, but procurement lead times, power, cooling, spare parts and operations become your responsibility. Hybrid designs can keep sensitive data local while bursting elastic jobs to a cloud; test data movement, identity, network egress, model packaging and reproducibility before relying on burst capacity.
5. Verify the complete hardware and software configuration
Never infer support from a GPU model name alone. NVIDIA’s AI Enterprise support matrix requires a supported GPU, a qualified or certified system (with listed platform exceptions) and supported infrastructure-management software for on-premises configurations. Validate the combination of accelerator, server, BIOS, firmware, network adapter, operating system, hypervisor, container runtime, Kubernetes distribution, drivers and AI software release.
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NVIDIA’s cloud deployment overview, updated 2026-07-31, lists AWS, Google Cloud, Azure, OCI, Alibaba Cloud and Tencent Cloud. Its deployment table distinguishes standard instances, NVIDIA images, managed Kubernetes and marketplace OpenShift; image contents and license inclusion vary by method. For every candidate, record whether the license is included, requires bring-your-own licensing, or is billed separately, and identify support entitlement and renewal terms.
Compatibility gate before purchase
- Freeze the intended framework, model-serving or training stack and release versions.
- Check the vendor support matrix for the exact GPU, server or VM, operating system and orchestration distribution.
- Verify driver, CUDA or equivalent runtime, firmware and container-image compatibility.
- Confirm network, storage and identity integrations, including private connectivity and registry access.
- Obtain written confirmation of regional availability, support lifecycle and licensing for the quoted configuration.
6. Benchmark the real job and calculate full cost
Peak accelerator specifications are not a procurement decision. Run the intended model, precision, context length, batch or concurrency and software stack. For training, include a representative data-loader, checkpoint and evaluation cycle. For serving, replay realistic request lengths and arrival patterns, including warm-up and autoscaling.
Metrics to capture
- Training: samples or tokens per second, time to target quality, scaling efficiency, checkpoint duration and recovery time.
- Online inference: time to first token, inter-token latency, end-to-end latency at p50, p95 and p99, requests or tokens per second, error rate and cold-start time.
- Batch inference: items or tokens per hour, completion deadline, retry rate and cost per completed item.
- All workloads: accelerator memory and utilization, host CPU and RAM, network throughput, storage wait, useful-work efficiency, uptime and thermal or throttling events.
Google recommends monitoring hardware utilization and goodput for accelerator environments. Goodput means completed useful work that meets the service target, not merely busy devices. Track it alongside utilization so a highly occupied cluster that misses latency or spends time retrying is not mistaken for an efficient one.
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Total-cost model
Calculate more than hourly accelerator price. Include reserved or committed-use terms, storage, snapshots, data transfer, inter-zone traffic, managed-service fees, software licenses, support, orchestration, observability, power and cooling, facilities, spare capacity, engineering labor and migration or exit costs. Cloud prices and capacity vary by region and date; obtain a current quote at procurement time. The available primary documentation does not establish a neutral, apples-to-apples benchmark or a universal cost winner, so use your own measured goodput and invoice data.
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Use the same workload evidence for every candidate. Weight each criterion according to business risk rather than allowing a single specification to dominate.
| Criterion | Questions for the shortlist | Evidence to require |
|---|---|---|
| Memory fit | Does aggregate accelerator and host memory cover weights, cache, activations and headroom? | Measured peak memory and failure behavior at maximum context or batch |
| Topology | Can the model stay on one host, and if not, is the fabric appropriate? | Peer-to-peer and collective-communication tests |
| Performance | Does it meet throughput, latency, concurrency and training-time targets? | Representative benchmark with p95/p99 and goodput |
| Operations | Can the team patch, monitor, scale and recover it? | Runbooks, staffing estimate and incident objectives |
| Compatibility | Are GPU, drivers, OS, framework and orchestrator supported together? | Versioned support-matrix evidence and vendor confirmation |
| Commercials | What are recurring, one-time, transfer and exit costs? | Current regional quote, license terms and support lifecycle |
| Governance | Can data, identity, audit and availability requirements be met? | Architecture review, control mapping and recovery test |
| Portability | How hard is it to move models, images, data and workflows? | Export procedure, alternative target and tested restore |
8. A practical selection procedure
- Write service objectives. Set quality, throughput, latency, concurrency, availability and completion deadlines for each workload class.
- Build a memory and topology model. Calculate weights, KV cache, activations, buffers and headroom; determine whether one accelerator, one host or multiple hosts are required.
- Create two or three feasible architectures. Include a simple single-host option, a scale-out option when justified, and an operating-model alternative such as managed service versus Kubernetes or scheduler.
- Pass the compatibility gate. Remove configurations that fail support, firmware, driver, operating-system, network or licensing checks.
- Run a bake-off. Use identical model versions, prompts or training data, precision, software releases and measurement windows. Capture goodput, tail latency, utilization and failure recovery.
- Price the complete lifecycle. Use current regional quotes and include people, facilities, software, transfer, support and exit costs.
- Conduct a production pilot. Exercise security controls, autoscaling, upgrades, checkpoint restore, incident response and a realistic traffic or training trace.
- Document a scale trigger. Define the utilization, queue time, latency or model-size threshold that justifies adding accelerators or moving to a different topology.
9. Common failure modes and fixes
Buying for peak FLOPS
Symptom: The system advertises high compute but misses token latency or training-time targets. Fix: Benchmark memory bandwidth, interconnect, data loading and collective operations with the real job; select on goodput.
Ignoring KV-cache growth
Symptom: Short prompts work, but long-context or concurrent requests exhaust memory. Fix: Model cache usage at the maximum supported context and concurrency, then reserve headroom or reduce context and batch targets explicitly.
Using training networking for every service
Symptom: An inference fleet pays for specialized fabric without measurable benefit. Fix: Keep replicated single-host serving on an appropriately connected network; require RDMA or equivalent only when distributed communication tests show it is needed.
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Assuming a GPU is supported everywhere
Symptom: Drivers, hypervisors or orchestration fail after delivery. Fix: Check the release-specific support matrix for the complete system and obtain written qualification before purchase.
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Choosing a platform the team cannot operate
Symptom: Capacity exists but upgrades, scheduling or incidents stall delivery. Fix: Include skills, staffing and runbook ownership in the scorecard; choose managed services when operational work is not strategic, or fund platform engineering when control is essential.
Comparing list prices instead of delivered work
Symptom: The cheapest accelerator has the highest cost per successful request or training milestone. Fix: Normalize by goodput and include software, transfer, idle reserve, support and labor.
10. Reference architectures by workload
Small internal assistant or RAG API
Begin with one small accelerator or CPU if the model and latency target fit. Google’s examples use L4/G2 for small inference and RAG; its GKE guide lists an L4 with 24 GB per GPU and an RTX PRO 6000 with 96 GB per GPU in illustrative use cases. Those are vendor-published specifications, not independent benchmarks. Validate concurrent context lengths, retrieval latency and failure recovery before expanding.
Large model on one server
Use a multi-accelerator host when aggregate memory is sufficient and the internal fabric supports the framework’s sharding mode. A100, H100 and B200 appear as Google Cloud examples for single-host large-model inference. Confirm current machine availability, memory, host topology and software support in the target region.
Multi-host serving
Choose this path when the model or throughput target cannot fit on one host. Evaluate H200, B200/GB200 or TPU-family options only as starting points for a measured design; the correct choice depends on sharding, network, framework and service objectives. Test scale-out goodput, cross-host tail latency and recovery from a failed host.
Distributed training cluster
Favor accelerator memory and count that match sequence length, batch size and parallelism, with RDMA and accelerator interconnect where communication warrants it. Compare a scheduler-led cluster, managed training platform and Kubernetes implementation against the team’s operating maturity, not only hardware price.
CPU or mixed pipeline
Keep preprocessing, retrieval, evaluation and smaller models on CPU when that is efficient. Microsoft’s Azure recommendations include memory-optimized CPU compute for smaller non-generative training and compute-optimized CPU inference. Verify latency and quality for the specific model instead of assuming a CPU is adequate—or assuming every stage needs a GPU.
Make the decision auditable
Store the workload brief, sizing assumptions, benchmark scripts, software lockfile, support-matrix checks, quotes and approval criteria with the architecture record. Revisit the decision when model context, precision, traffic, regional capacity, pricing or software releases change. This creates a defensible path from business objective to infrastructure choice and makes the next scale decision faster.
Quick Recap
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