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Plan GPU capacity in four passes: define the workload and service targets, calculate the memory floor, select accelerators that clear that floor, then benchmark with realistic traffic and add headroom. GPU count follows from those measurements—not from parameter count or advertised FLOPS alone. Training generally needs much more memory than serving because gradients, optimizer state, and saved activations must remain available; serving demand is dominated by weights, runtime overhead, and the KV cache at your target context and concurrency.
1. Define the workload before choosing a GPU
A useful capacity plan starts with a written workload specification. The same model may need one GPU for low-volume batch inference, several replicated GPUs for an interactive API, and a model-parallel cluster for training. Record these values before comparing hardware:
- Workload type: pre-training, fine-tuning, batch inference, or interactive serving.
- Model: parameter count, architecture, number of layers and KV heads, tokenizer, and expected maximum context length.
- Precision: FP32, FP16, BF16, 8-bit, 4-bit, or another quantization scheme for weights and runtime state.
- Training targets: dataset size, sequence length, global and micro-batch sizes, checkpoint interval, and desired time to completion.
- Serving targets: requests per second, peak concurrent requests, time to first token (TTFT), inter-token latency (ITL), end-to-end latency, and output-token rate.
- Availability: whether a single-GPU outage is acceptable, required recovery time, and maintenance policy.
- Constraints: capital or hourly budget, cloud region and quota, power and rack limits, and the growth horizon.
Separate average demand from the peak you must survive. A service that averages 20 concurrent requests but reaches 80 during a launch should be sized and tested against the 80-request case, or it should explicitly queue or shed excess traffic. Define whether latency targets apply to every request, the 95th percentile, or another percentile; otherwise a benchmark cannot produce a pass/fail decision.
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2. Calculate the memory floor
Inference memory equation
For serving, use:
Required accelerator memory = model weights + runtime overhead + activations + (KV cache per batch × batch size)
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Google Cloud’s engineering guidance defines these terms and notes that a typical server overhead allowance is about 1–2 GB. The allowance is not a universal constant: frameworks, kernels, allocators, CUDA libraries, graph capture, and monitoring agents can change it. Measure the actual backend after loading the model.
Model weights are only the first term. AWS describes KV-cache demand as scaling approximately with input context, generated output length, concurrent requests, the number of KV heads and layers, and head dimension. Long prompts, long answers, or more simultaneous sequences can therefore exhaust memory even when the quantized weights fit comfortably.
Weight-only estimates
Google Cloud gives the following estimates using parameter count multiplied by bytes per weight. They exclude runtime overhead, activations, and KV cache.
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|---|---|---|---|
| 7 billion parameters | Approximately 14 GB | Approximately 7 GB | Approximately 3.5 GB |
| 70 billion parameters | Approximately 140 GB | Approximately 70 GB | Approximately 35 GB |
These figures are planning estimates, not the amount of VRAM a production server can use. A 7B model at 4-bit may fit on a single card by weight size yet fail when context length, batching, or the backend’s workspace is included. Leave allocator and operational headroom rather than targeting 100% reported memory.
Training memory
Training stores weights, gradients, optimizer state, and saved activations. Mixed precision reduces some terms but does not remove them. AWS documentation states that even a relatively small 10-billion-parameter deep-learning model can require at least 200 GB during training, exceeding one common GPU and motivating model parallelism. The exact requirement depends on optimizer, precision, sequence length, micro-batch size, activation checkpointing, and parallelism strategy.
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Training batch size has a direct memory cost: larger batches retain more activations. If a run is close to the limit, reduce micro-batch size, enable activation checkpointing, use gradient accumulation, or select a larger-memory accelerator. These changes can affect throughput and convergence, so validate them in a short representative run rather than assuming a memory-only fix is free.
Headroom policy
Document a minimum free-memory target for your framework and workload. Reserve capacity for model-loading peaks, temporary buffers during graph compilation, tokenizer or preprocessing transfers, rolling upgrades, and a larger-than-usual prompt. A card that passes only when every request has the median context is not a reliable production design.
3. Turn the memory floor into a GPU count
One large GPU
Choose one accelerator when the complete model state fits with headroom and its measured throughput meets the target. A single device avoids cross-device communication, simplifies deployment, and usually gives the lowest latency for a single sequence. It also creates a single failure domain and may leave expensive memory idle when demand is low.
Several smaller GPUs
Use multiple devices when they provide better availability, a lower effective cost, or enough aggregate capacity for independent replicas. Replication loads a full model on each GPU; it improves concurrent throughput but does not make a model that fails the per-GPU memory floor fit. Compare the cost of two or more smaller replicas with one larger card at the same measured latency and utilization.
Tensor or model parallelism
Use tensor or model parallelism when one GPU cannot hold the model or when a sharded configuration is required for performance. Partitioning introduces synchronization and inter-GPU traffic. The fabric, topology, and collective-communication performance can matter as much as nominal compute. Test the actual shard count: adding a GPU can reduce per-device memory while lowering useful tokens per second if communication dominates.
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MIG and other partitioning
When several isolated workloads can share one physical accelerator, NVIDIA’s Multi-Instance GPU (MIG) technology provides dedicated compute, memory, and memory-bandwidth resources with predictable latency and throughput for simultaneous workloads. Partitioning is useful for small services, development, or batch jobs, but each partition has a smaller memory ceiling and cannot absorb a model that requires the full device. Measure interference, scheduling delay, and recovery behavior with the intended mix.
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Do not assume the training accelerator must also serve the model. Google Cloud recommends A100 for fine-tuning and moderate model training, while larger-memory, high-bandwidth systems target frontier training and very large-model inference. A training fleet can be optimized for distributed compute and checkpoint throughput; a serving fleet can be optimized for memory per device, low latency, and efficient replication. Reuse a class only when its measured economics and operational requirements make sense for both jobs.
4. Compare accelerators on more than FLOPS
After an accelerator clears the memory gate, compare the dimensions that determine useful capacity:
| Dimension | What to check | Why it changes the decision |
|---|---|---|
| Memory capacity | Usable HBM or VRAM after firmware and runtime reservations | Sets the hard limit for weights, activations, and KV cache. |
| Memory bandwidth | Advertised bandwidth and measured tokens per second | Influences weight movement and decode performance, especially at small batches. |
| Tensor performance | Precision-specific Tensor Core or matrix throughput | Affects training step time and prefilling, but only when the workload is compute-bound. |
| Interconnect | NVLink or other fabric, topology, and collective bandwidth | Determines the penalty for tensor/model parallelism and multi-GPU training. |
| Price | Capital cost or region-specific hourly rate, including attached hosts | Lets you compare total cost for the measured capacity, not a bare card. |
| Availability | Quota, region, lead time, and replacement stock | A theoretically efficient design is unusable if capacity cannot be obtained or restored. |
| Software support | Driver, CUDA, framework, kernel, and backend compatibility | Unsupported versions can erase the advertised performance or block deployment. |
Google’s current catalog, accessed in 2026, lists frontier GPU systems with up to 279 GB of HBM per GPU and up to 1,800 GB/s of NVLink interconnect bandwidth. Such figures describe available hardware configurations, not guaranteed application throughput; confirm the exact SKU, host topology, region, and software stack before purchasing.
5. Size the rest of the serving system
GPU count alone does not define capacity. Include:
- CPU capacity: tokenization, request validation, batching, sampling, and postprocessing can bottleneck a lightly loaded GPU.
- Host memory: model staging, tokenizer data, request queues, and page-locked transfer buffers consume RAM.
- Model and checkpoint storage: estimate capacity for active versions, rollback copies, and checkpoints; measure read throughput and IOPS during load.
- Network: account for ingress, streamed output, service-to-service calls, and storage traffic. Sharded inference also needs inter-GPU bandwidth.
- Scheduler and quotas: cloud placement, Kubernetes limits, batch windows, and per-project quotas can cap usable GPUs below the purchased total.
- Redundancy: reserve a spare device, a second replica, or cross-zone capacity according to the recovery objective.
NVIDIA’s serving reference architecture calls for monitoring GPU utilization and memory, network-fabric health, readiness, prefill/decode saturation, KV-cache behavior, backend errors, and model-load state. Alert on trends, not just a single utilization threshold: rising KV-cache occupancy or queueing latency can signal a capacity problem while GPU utilization still appears moderate.
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6. Benchmark before committing to a fleet
Build a representative workload
Run the actual model and serving backend. Use the production tokenizer, precision, quantization kernels, batching policy, maximum context, and representative distributions of prompt and output lengths. A benchmark with one short prompt and one generated token cannot predict a long-context chat workload.
Sweep concurrency and batch size
- Start at one concurrent request and verify correctness, model-load time, and baseline latency.
- Increase batch size or concurrent sequences in controlled steps.
- Stop each step when a latency percentile, KV-cache limit, out-of-memory event, or error-rate target fails.
- Repeat around the failure boundary with several runs to separate warm-up effects from stable capacity.
- Test recovery by unloading and reloading the model, restarting a worker, and replacing a failed replica.
Record comparable measurements
| Category | Measurements to retain |
|---|---|
| Latency | TTFT, inter-token latency, end-to-end latency, and the percentile used for the SLO. |
| Throughput | Output tokens per second, requests per second, completed tokens, and sustained concurrency. |
| Reliability | Error rate, timeouts, out-of-memory events, failed model loads, and restart or recovery time. |
| Resource use | GPU utilization, allocated and free memory, KV-cache occupancy, CPU and host-memory use, and network traffic. |
| Reproducibility | Model size, precision, backend, GPU type, driver and framework versions, batch/concurrency, and prompt/output distributions. |
NVIDIA identifies these fields as serving-test measurements. Keep the raw load-test configuration with the results so a change in driver, kernel, backend, or model version can be explained rather than mistaken for random variance.
7. Choose between the two common serving designs
Replicated single-GPU instances
Replication is appropriate when one device holds the model and meets the latency target. Route requests across independent instances, scale the replica count to peak concurrency, and keep at least the redundancy required by your availability objective. This design avoids inter-GPU synchronization and makes failures local, but each replica consumes a full copy of the weights.
One sharded multi-GPU instance
Sharding is required when the model state does not fit on one device or when a tested parallel configuration delivers better throughput. It concentrates more failure impact in one instance and makes topology, collective communication, and coordinated upgrades part of the operational burden. Compare tail latency and recovery time, not only aggregate tokens per second.
Decision test
- If the complete state does not fit on one device, reject replication and test sharding or a larger-memory accelerator.
- If it fits, benchmark one replica and a replicated pair at the target concurrency.
- Choose the design with the lowest total cost that meets latency, throughput, availability, and recovery targets with documented headroom.
8. Training-capacity planning workflow
- Estimate state memory: include weights, gradients, optimizer state, and saved activations at the planned precision and sequence length.
- Set the per-GPU micro-batch: choose the largest value that fits with headroom; use gradient accumulation to reach the desired global batch when necessary.
- Select parallelism: data parallelism for replicated model states, tensor/model parallelism when a state exceeds one device, and pipeline approaches when the model and communication pattern justify them.
- Measure scaling: run a fixed training step on one, two, and more GPUs. Record examples or tokens per second, step time, communication fraction, and scaling efficiency.
- Include checkpoint and evaluation traffic: storage throughput and network contention can pause workers even when GPU utilization is high.
- Calculate completion time: divide the required training tokens or steps by sustained measured throughput, then add evaluation, checkpoint, restart, and queue time.
- Add failure capacity: decide whether a failed GPU causes a restart, elastic shrink, or immediate replacement, and budget the hardware or cloud quota for that policy.
For fine-tuning, a smaller fleet may be sufficient if sequence length, batch size, and optimizer state are controlled. Do not transfer a fine-tuning result directly to pre-training: the activation and optimizer requirements can be materially different.
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9. Avoid common sizing failures
“The weights fit, so the model fits”
Weights omit overhead, activations, and KV cache. Recalculate at maximum context and peak concurrent sequences, then confirm with a warmed benchmark.
“More FLOPS always means more serving capacity”
Decode can be limited by memory bandwidth, synchronization, or queueing. Compare measured TTFT, inter-token latency, and tokens per second at the intended traffic mix.
“Aggregate VRAM is enough”
Four cards with 24 GB each do not provide a transparent 96-GB pool. The model must be replicated or explicitly sharded, and the sharding method must fit each device and its communication topology.
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Average demand hides bursts. Test the declared peak, define queue limits, and specify whether excess requests wait, are rejected, or spill to another region.
“A cloud listing is a capacity guarantee”
GPU generations, prices, quotas, regions, and marketplace inventory change quickly. Verify the exact instance, hourly or capital cost, driver image, and quota availability immediately before commitment. The technical principles here were researched on October 16, 2026, with a 30-day freshness window for volatile infrastructure details.
“A single benchmark number is enough”
Record software versions, prompt and output distributions, batch policy, and failure behavior. Without those controls, two apparently different GPU results may simply be different workloads.
10. A practical capacity worksheet
Use one row per candidate design and fill every field from a test or documented constraint:
| Field | Candidate A | Candidate B |
|---|---|---|
| GPU model and usable memory | Measure or document | Measure or document |
| Model precision and complete memory footprint | Weights + overhead + activations + KV cache | Weights + overhead + activations + KV cache |
| Topology and parallelism | Replication, tensor/model parallel, or MIG | Replication, tensor/model parallel, or MIG |
| Peak concurrency passed | Load-test result | Load-test result |
| TTFT, inter-token, and tail latency | Load-test result | Load-test result |
| Sustained throughput | Tokens/second and requests/second | Tokens/second and requests/second |
| Total cost | GPU, host, storage, network, and redundancy | GPU, host, storage, network, and redundancy |
| Quota and availability | Region, lead time, replacement plan | Region, lead time, replacement plan |
| Recovery behavior | Measured restart or failover time | Measured restart or failover time |
Reject any candidate that fails the hard memory or service-level requirement. Among the survivors, select the design with the best measured cost per required throughput while preserving the planned headroom and failure policy. Re-run the benchmark whenever the model, precision, backend, driver, GPU SKU, context limit, or traffic distribution changes.
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