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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →There is no reliable cores-per-model formula for AI inference. Estimate CPU capacity by benchmarking your model and serving stack with representative inputs, outputs, and concurrency; use the sustained throughput that meets your latency objectives to size for peak demand, then add capacity for bursts, failures, and growth.
Start with the workload, not a core count
The same model can require very different infrastructure depending on its precision, prompt and response lengths, concurrency, traffic pattern, and latency targets. Before comparing CPU configurations, record the workload you actually need to serve.
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- Model and software: model family and architecture or parameter scale, inference runtime and version, and the intended CPU hardware.
- Precision: precision or quantization, plus any quality constraint that limits which formats are acceptable.
- Request shape: average and peak input-token and output-token lengths, and any relevant context-window or batch settings.
- Demand: peak request rate and concurrent requests, including how long bursts last and whether requests arrive unevenly.
- Service objectives: p50, p95, or p99 request latency as applicable; time to first token (TTFT); output-token latency; and the maximum acceptable queue delay.
- Operations: traffic seasonality, availability target, failure tolerance, and expected growth.
AWS’s inference right-sizing guidance likewise recommends characterizing model, token lengths, concurrency or request rate, latency goals, traffic patterns, and recovery needs before selecting infrastructure.
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Choose metrics that describe capacity and user experience
For a generative model endpoint, measure latency and work performed together. Track TTFT, output-token latency (also called time per output token or inter-token latency), end-to-end request latency, input and output tokens per second, concurrency, and errors or timeouts. Google Cloud’s GKE inference documentation describes inference latency and throughput metrics.
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Requests per second is useful when the request distribution is fixed. It can mislead when prompt or response lengths vary: two systems serving the same number of requests may process very different token volumes. For non-generative models, record completed inferences per second and latency percentiles at the target batch size and concurrency.
Keep the conditions with every benchmark result: exact model artifact, input shape and request mix, batch settings, runtime and software version, CPU family, thread count, and measurement method. There is no single benchmark recipe that fits every non-LLM model; the governing principle is to measure a representative workload.
Benchmark each candidate at its SLO
- Fix the comparison conditions. Use the same model artifacts, runtime and backend, precision, input and output shapes, context window, and concurrency for each candidate CPU configuration.
- Warm up the service. Measure sustained performance after startup rather than relying on a short, single-request result.
- Find the safe service rate. Increase load and record throughput, latency percentiles, token rates, and errors. Use the capacity the system sustains while meeting the latency and error objectives—not a higher throughput reached only after violating them.
- Repeat for plausible candidates. Include relevant CPU families or configurations and any intended thread or placement settings. Change one factor at a time where practical so its effect is interpretable.
Public benchmark results can help narrow candidates, but they are not direct comparisons if workload shapes, serving frameworks, or quantization differ. AWS recommends validating candidates empirically in its right-sizing guidance and EKS CPU inference guidance.
When comparing costs, compare the cost of serving a fixed request or token volume at the required p95 or p99 latency. Cost per core or peak benchmark throughput alone does not show which candidate meets the service objective efficiently.
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Tune CPU use before scaling out
Control thread counts
Some machine-learning libraries detect all node vCPUs and create more worker threads than a container or pod is allocated. Set OpenMP, MKL, OpenBLAS, or runtime-specific thread counts at or below the allocation, then benchmark alternatives. A smaller model can perform worse when oversubscribed, so more threads are not automatically better. AWS discusses thread allocation in its EKS CPU inference guidance.
Consider memory bandwidth as well as cores
For inference, AWS recommends prioritizing memory bandwidth over core count when selecting CPU instances. Treat that as a candidate-selection heuristic, not a guarantee: benchmark the intended model, runtime, precision, and request mix on the hardware you plan to use.
Check NUMA placement and contention
On systems with multiple NUMA nodes, spreading threads across nodes can increase memory latency; sharing cores can also make throughput less predictable. Intel’s CPU pinning and NUMA guidance explains these placement considerations. Where the platform and workload support it, test pinning or topology-aware allocation and compare results under load.
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Test concurrency and batching together
Higher concurrency or batching can increase throughput, but can also add queueing and worsen tail latency. Measure the trade-off against your SLO. Do not extrapolate linearly from one request or one thread to a fully loaded node: contention and memory behavior can change as load rises.
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Turn measured capacity into a replica estimate
Let Dpeak be forecast peak demand in a unit that matches the benchmark, and let CSLO be the sustained per-node capacity that meets the chosen latency and error objectives. A starting point is:
replicas = ceil(D_peak / C_SLO)
For an identical request distribution, demand and capacity can be expressed in requests per second. For LLM traffic, input and output tokens per second are often more informative. If request shapes differ, benchmark a representative weighted mix or segment demand into meaningful workload classes; do not divide a request rate by capacity measured on a different prompt-and-output distribution.
This calculation gives a baseline, not a guarantee of linear scaling. Increase it to cover the failure tolerance, demand variability, and growth you have chosen to support. Then load-test the planned deployment at expected peak demand and during the failure scenario that matters to the service. AWS’s guidance also calls for capacity beyond the calculated minimum to account for spikes, uneven distribution, failures, and future growth.
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Scale on signs of inference saturation
CPU utilization alone may not show whether an inference service is running out of useful capacity. Consider signals that reflect demand and user impact:
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- Queue length or pending requests.
- Active or concurrent requests.
- p95 or p99 request latency and TTFT.
- Per-node input and output token throughput.
- Errors and timeouts.
Use autoscaling to respond to changing load, but keep a warm baseline that can meet the SLO while new instances start and models load. Define what happens if demand exceeds the safe envelope, such as queuing within a bounded delay or shedding load. AWS discusses these scaling signals and baseline-capacity considerations in its right-sizing guidance.
Decide whether CPU remains the right tier
AWS’s EKS guidance identifies quantized 1–8B small language models, embeddings, classifiers, retrieval, orchestration, and batch or asynchronous scoring as CPU candidate workloads. It also notes that larger or latency-sensitive online models may be better suited to accelerators. These are AWS-oriented starting points, not universal cutoffs for every cloud, CPU generation, model, or runtime.
A CPU may not be suitable when the required p95 latency is very tight or sustained concurrency is high. Decide from your measured target workload rather than treating an example threshold as a guarantee. As the AWS guide puts it: “Every recommendation in this guide should be validated empirically.”
For two or more candidate configurations, compare them on the same request mix using SLO-qualified sustained throughput, p95 and p99 latency, TTFT and output-token latency, memory bandwidth and usable memory, CPU architecture and NUMA layout, cost at the target latency, availability, operational complexity, and failure recovery.
Re-run the estimate when conditions change
Capacity results belong to the model, software, hardware, and workload that produced them. Re-benchmark after changing the model or runtime version, precision or quantization, thread settings, CPU configuration, or request mix. A previously valid replica estimate may no longer meet the SLO after one of those changes.
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