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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesPlan GPU capacity in four passes: define the workload and service targets, calculate the memory floor, select a GPU and topology that pass that floor, then validate the choice with representative benchmarks and operational headroom. Parameter count alone is not enough. Training also needs gradients, optimizer state and saved activations; serving adds runtime overhead and a KV cache that expands with context length and concurrent requests.
The result should be a capacity model that states the model version and precision, sequence lengths, throughput, latency and availability targets, GPU count and type, software stack, and the assumptions behind future growth. Use separate GPU classes for training and serving when their constraints differ, and reject any accelerator that cannot hold the complete runtime state even if its advertised compute rate looks attractive.
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Start with a workload contract
Write down the workload before looking at GPU names. A training job, an offline batch, and an interactive token service stress different resources and should not automatically share one sizing rule.
| Workload | Primary targets | Memory behavior | Typical scaling decision |
|---|---|---|---|
| Pretraining or fine-tuning | Time to a completed run, training throughput, checkpoint reliability and cost | Weights plus gradients, optimizer state and saved activations; larger batches increase activation memory | Use one GPU only when the full training state fits; otherwise use model or other parallelism and a fast inter-GPU fabric |
| Batch inference | Jobs completed per hour, throughput per dollar and predictable completion time | Weights, runtime overhead, activations and KV cache for the chosen batch and context | Increase batch size until memory, latency or error limits are reached, then add replicas |
| Interactive serving | Time to first token, inter-token latency, end-to-end latency, peak concurrency and availability | KV cache changes with prompt and output lengths and the number of simultaneous sequences | Replicate independent instances when each meets latency and memory targets; shard a model when one GPU cannot hold it |
Record the variables that change the answer
- Model parameter count, architecture and version, including the number of layers, KV heads and head dimension when available.
- Weight and activation precision: FP16, BF16, 8-bit, 4-bit or another format.
- Maximum and typical input context, expected output length and the distribution of both rather than only an average.
- Target batch size for training or batch inference; peak concurrent requests for a live service.
- Required throughput, time to first token, inter-token latency, end-to-end latency and tail-latency percentile.
- Availability target, allowable recovery time, maintenance approach and whether an instance may be interrupted.
- Budget basis: hourly cloud spend, reserved capacity, or capital purchase; include storage, hosts and networking rather than GPU rental alone.
- Growth horizon, expected model upgrades and the date by which additional capacity must be available.
Keep separate profiles for normal traffic, the busiest expected period and a failure case. A GPU plan that works only at average load is not a production capacity plan.
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Calculate the memory floor
Serving equation
For inference, Google Cloud’s engineering guidance defines the required accelerator memory as:
Required accelerator memory = model weights + overhead + activations + (KV cache per batch × batch size)
Model weights are the serialized parameters. Runtime overhead covers the framework, allocator and other serving processes; Google describes about 1–2 GB as a typical server-overhead estimate, but the actual value depends on the backend and configuration. Activations are temporary tensors created during execution. The KV cache stores attention keys and values for active sequences and is multiplied by the number of sequences in the batch.
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Google’s 2024 rule of thumb is model size in bytes = number of parameters × data type in bytes. The following figures are weight estimates only; they are not safe GPU capacities for serving.
| Model | FP16/BF16 weights | 8-bit weights | 4-bit weights |
|---|---|---|---|
| 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 |
Quantization reduces weight storage, but it does not remove runtime overhead, activations or the KV cache. Leave room for the model loader, temporary buffers and the allocator’s behavior. A 7-billion-parameter model at FP16 therefore starts at about 14 GB before those additions; a 70-billion-parameter model at 4-bit starts at about 35 GB before them.
Why context and concurrency cause surprise out-of-memory errors
A request with a long prompt retains more keys and values. Generated tokens add to that cache, and every simultaneous sequence consumes its own portion. AWS describes KV-cache demand as scaling approximately with input context, output length, concurrent requests, the number of KV heads and layers, and head dimension. Two deployments with identical weights can therefore need very different memory at their target concurrency.
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Measure the longest supported context and the high-percentile prompt and completion lengths. If the service accepts streaming requests, count sequences that remain open while tokens are being delivered. Test cache eviction, request limits and admission control explicitly; otherwise a workload can fit during a short benchmark and fail during a burst.
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Training needs a larger memory model
Training stores more than inference weights. Gradients, optimizer state and saved activations occupy additional memory, and larger training batches generally save more activations. Checkpointing can trade compute for activation memory, but it does not make the underlying state disappear.
AWS states that even a relatively small 10-billion-parameter deep-learning model can require at least 200 GB during training. That is a lower-bound example, not a universal multiplier: optimizer choice, precision, batch size, sequence length, framework and parallelism change the result. Treat a single-GPU fit based only on weight size as invalid for training.
Use a worked floor, not a guess
For a 7B FP16 serving candidate, begin with roughly 14 GB of weights, add the documented 1–2 GB typical overhead estimate, then measure activations and KV cache at the intended context and concurrency. The resulting floor is greater than 15–16 GB and can be substantially higher under long-context traffic. For a 70B 4-bit candidate, begin near 35 GB, add overhead, activations and cache, and then verify whether the runtime supports the quantization format without allocating large conversion buffers.
For training, create a separate worksheet for weights, gradients, optimizer state, activations, communication buffers and checkpoint staging. If the total exceeds one GPU’s usable memory, move to sharding or a larger-memory accelerator rather than hoping a higher-FLOPS part will compensate.
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Choose the GPU class and count
Apply a hard memory gate first
Reject a GPU if usable memory cannot hold the complete model and runtime state at the required batch or concurrency. Only after that gate passes should you compare:
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- Memory bandwidth, which affects how quickly weights and cache data move.
- Tensor Core capability and supported numeric formats for the selected framework.
- Compute throughput at the precision you will actually run.
- Interconnect bandwidth and topology when a model or job spans GPUs.
- Measured throughput and latency at the intended batch, context and concurrency.
- Acquisition or hourly cost, availability, quota and operational complexity.
Google Cloud recommends A100-class accelerators for fine-tuning and moderate model training. Its 2026 catalog lists frontier systems reaching up to 279 GB of HBM per GPU and up to 1,800 GB/s of NVLink interconnect bandwidth. Those are catalog maxima for particular systems, not a promise that every SKU or region provides them; verify the exact instance, quota and software support.
One large GPU versus several smaller GPUs
| Choice | Advantages | Costs and risks | Use it when |
|---|---|---|---|
| One larger-memory GPU | No sharding traffic for a model that fits; simpler deployment and often lower coordination overhead | One failure removes the instance; large parts can be scarce or expensive; unused memory may be stranded | The model and peak cache fit with headroom and a single instance meets latency or throughput goals |
| Several smaller GPUs with replicas | Independent failures, horizontal throughput and flexible scaling for models that fit on each GPU | More replicas to operate; aggregate memory does not help a model that cannot fit on one device | Each replica can hold the model and meet its target, and traffic can be distributed reliably |
| Several GPUs with tensor or model parallelism | Allows a model larger than one GPU and can provide aggregate compute | Inter-GPU communication, synchronization and fabric failures can raise latency and reduce efficiency | No single device passes the memory gate, or measured parallel execution is required for the target |
Do not add memory capacities across unrelated replicas. Four 24-GB GPUs provide four 24-GB model slots, not one 96-GB slot, unless the runtime explicitly shards the model across them.
Serving topologies
Replicated instances
Load the complete model on each GPU or GPU group and distribute requests. Replication is usually the simplest way to increase throughput and isolate failures. Size the number of instances from measured throughput at the required latency, then test what happens when one instance is drained or unavailable.
Tensor or model parallelism
Split layers or tensor operations across devices when one GPU cannot hold the model. Confirm that the interconnect is fast enough for the communication pattern and that the backend supports the chosen partition. A configuration that has enough aggregate memory can still miss time-to-first-token or inter-token targets because synchronization dominates.
MIG and partitioned GPUs
NVIDIA Multi-Instance GPU (MIG) can divide a supported physical GPU into isolated instances with dedicated compute, memory and memory-bandwidth resources. NVIDIA describes predictable latency and throughput for simultaneous workloads as a design goal. Use MIG when several isolated, smaller jobs can fit within the available partitions; it is not a way to make one model larger than the parent GPU or to combine partition memory into a single pool.
Training topology
When the training state exceeds one device, model parallelism is one option, but the partition must be evaluated with the framework, batch size and communication pattern you will use. Checkpoint writes and recovery also become distributed concerns. A high-bandwidth interconnect can matter more than a modest difference in nominal compute because every step may exchange tensors or gradients.
Size the complete system, not just the accelerators
GPU utilization can look low while the service is limited elsewhere. Include these resources in the capacity sheet:
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- Host memory: model staging, pinned transfer buffers, queues, tokenizer data and multiple loaded versions during a rollout.
- Model and checkpoint storage: capacity for weights, optimizer checkpoints, replicas and rollback versions.
- Storage throughput and IOPS: startup and recovery time depend on how quickly large files can be read.
- Network bandwidth: ingress, egress, request routing, telemetry and checkpoint traffic can contend with inference.
- Inter-GPU fabric: verify topology and bandwidth rather than assuming all devices in a host communicate equally.
- Scheduler limits and quotas: a theoretically suitable GPU is unusable if the region, project or account cannot obtain it.
- Redundancy: reserve a recovery path for a failed GPU, host, zone or maintenance event.
NVIDIA’s serving reference architecture recommends monitoring GPU utilization and memory, fabric health, readiness, prefill and decode saturation, KV-cache behavior, backend errors and model-load state. Alert on these signals together; utilization by itself cannot distinguish useful work from a memory-bound or queue-bound service.
Benchmark with representative traffic
Published FLOPS are not a capacity result. Run the actual model, backend, precision and software versions on each candidate GPU.
- Freeze the test description. Record model revision, tokenizer, precision or quantization, serving framework, GPU SKU, host type, driver, CUDA or equivalent runtime, library versions and context limits.
- Create a traffic distribution. Include realistic prompt lengths, output lengths, request arrival patterns, streaming behavior and the peak concurrent-request target. Keep a separate long-context stress case.
- Warm the deployment. Load weights, complete compilation or kernel selection, and discard startup samples. Measure cold-load and recovery time separately.
- Sweep batch or concurrency. Increase one variable at a time until the latency target, KV-cache limit, memory limit or error rate fails. Test both steady state and bursts.
- Exercise failure behavior. Drain an instance, restart a worker, reload a model and remove one replica or one link where practical. Record queue growth and recovery time.
- Repeat for every candidate. Keep software and traffic constant so GPU differences are meaningful. Do not compare one GPU with a tuned backend against another with default settings.
- Capture the complete result. Save raw samples and percentile calculations, not only an average.
Metrics to record
| Metric | Why it determines capacity |
|---|---|
| Time to first token | Shows prompt ingestion and prefill responsiveness for interactive users |
| Inter-token latency | Shows decode smoothness after the first token |
| End-to-end request latency | Captures queueing, prefill, decode and postprocessing together |
| Output tokens per second | Allows capacity to be expressed in useful generated work |
| Throughput and achieved concurrency | Shows how many requests or tokens the deployment handles at the target SLA |
| Error rate and rejected requests | Identifies overload, cache exhaustion and backend instability |
| GPU utilization and memory use | Reveals compute saturation, memory pressure and headroom |
| Model size, precision, backend, GPU and software versions | Makes the result reproducible after upgrades or migrations |
Use tail latency, not only the mean. A configuration that averages 200 milliseconds but produces unacceptable high-percentile delays under peak concurrency does not meet an interactive target.
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For a replicated service, let Q be required throughput and q be measured throughput per instance while meeting latency and memory limits. The minimum instance count is ceil(Q ÷ q). Apply the same calculation to peak concurrent requests when concurrency, rather than tokens per second, is the binding limit.
Then add explicit capacity for failure, maintenance, rollout overlap and the growth horizon. No universal percentage applies because the right margin depends on the availability objective and demand volatility. State the assumption, for example, that the service must continue after losing one instance, and test that reduced fleet directly.
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For training, count the GPUs needed by the measured step time and memory fit at the chosen global batch and sequence length. A larger fleet is not automatically faster if communication or input storage becomes the bottleneck. Compare completed samples or tokens per hour, checkpoint duration and cost per training run.
Compare options on the same scorecard
| Question | Evidence to require |
|---|---|
| Does it fit? | Measured peak memory with weights, overhead, activations and KV cache, plus stated headroom |
| Does it meet the SLA? | Time-to-first-token, inter-token, end-to-end and tail latency at target concurrency |
| Does it scale? | Throughput when adding replicas or shards, including fabric and scheduler limits |
| What does it cost? | Hourly or capital GPU cost plus host, storage, network, software and idle capacity |
| Can you obtain it? | Region, quota, lead time, maintenance policy and replacement path |
| Can you operate it? | Deployment complexity, monitoring, rollback and recovery after a failed device |
A smaller GPU can be the cheaper answer when replication meets the target efficiently. A cheaper GPU is not viable when its memory leaves no room for cache growth or when sharding overhead breaks the latency budget.
Troubleshoot common sizing failures
| Observed symptom | Likely cause | Corrective action |
|---|---|---|
| Model loads, then fails during a long request | KV cache or activations were omitted from the estimate | Measure peak context, output length and concurrency; cap admission or choose more memory |
| Training runs out of memory when batch size increases | Saved activations grow with the batch | Reduce microbatch size, use accumulation or activation-saving techniques, or move to a larger/sharded configuration |
| GPU utilization is high but token latency misses | Concurrency or decode work is saturating the device | Benchmark lower concurrency, add replicas, or select a GPU with more relevant bandwidth and compute |
| Several GPUs have enough aggregate memory but the model is slow | Inter-GPU communication or synchronization dominates | Check fabric topology and sharding efficiency; compare with a larger single-memory GPU |
| Throughput collapses during bursts | Queue, host CPU, network or KV-cache limits were not tested | Run burst tests and monitor each layer, then set admission and autoscaling limits |
| Capacity cannot be launched | Quota, region availability or scheduler limits | Verify allocation before committing to the design and maintain an alternate SKU or region |
| Rollouts cause outages | No capacity for overlapping old and new model versions | Include staging, model-load time and rollback replicas in the headroom plan |
Account for changing hardware and prices
GPU generations, cloud SKUs, prices, quotas, regions, marketplace inventory and contractual terms change quickly. Recheck those details immediately before purchasing or reserving capacity; the technical equations remain useful, but a quoted instance may no longer exist or may expose different memory and interconnect characteristics. The published figures above reflect guidance available in 2026, including Google Cloud’s catalog page accessed in 2026 and AWS SageMaker documentation accessed in 2026.
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When comparing cloud and owned hardware, use the same measured workload and include utilization between jobs, storage, power or host charges, support, deployment labor and replacement time. A capital purchase that appears inexpensive per GPU can be costly if the model uses only a fraction of its memory or if demand is seasonal.
Capacity-planning checklist
- Workload type, model revision and growth horizon are written down.
- Parameter count, precision, context and output distributions are documented.
- Training includes gradients, optimizer state and saved activations.
- Serving includes runtime overhead, activations and KV cache at peak concurrency.
- A candidate GPU passes the memory gate with measured headroom.
- Bandwidth, Tensor Core or equivalent precision support, compute, interconnect and cost are compared.
- CPU, host memory, storage, network, fabric, quotas and redundancy are included.
- Benchmarks use the real backend, software versions and representative traffic.
- Time-to-first-token, inter-token latency, end-to-end latency, throughput, errors, utilization and memory are recorded.
- Failure, rollout, recovery and demand-growth cases have been tested.
- Volatile SKU, price and quota assumptions have a dated recheck.
The practical decision rule
Choose the least complex GPU configuration that holds the complete workload state, meets measured latency and throughput targets at peak demand, and still has an explicit path for failure and growth. Memory is the gate; bandwidth, compute and interconnect determine how efficiently the workload runs after it fits. Only a benchmark using the intended model, backend, traffic and software can turn that plan into a defensible GPU count.
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