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Scaling AI inference starts with the workload, not the model name. Measure prompt and response lengths, concurrency, traffic peaks and latency needs; check that weights, KV cache and runtime overhead fit; then benchmark representative traffic before choosing hardware, serving architecture or autoscaling rules.
What determines inference capacity?
Two deployments serving the same model can require significantly different infrastructure depending on prompt length, response length, concurrency and latency objectives, according to AWS Prescriptive Guidance. A model label alone therefore cannot tell you how many accelerators to provision or what throughput to expect.
Start by describing the traffic the service must handle:
- Model and precision: record the model architecture, size and numerical precision used in deployment.
- Request shape: measure the distributions of input and output token lengths. A short chat exchange and a long document-processing request create different memory and latency demands.
- Load: capture concurrent requests or requests per second or minute, including peaks and bursts.
- Service expectations: define latency, availability and recovery objectives before choosing a serving setup.
Use distributions and peak behavior rather than relying only on averages. A capacity plan based on typical short prompts may fail when long contexts arrive together.
Which service objectives should you set?
Set measurable service-level objectives (SLOs) for the user experience and the queue, not just an aggregate requests-per-second target. For a language model, track:
- Time to first token (TTFT): how long a user waits before generation begins.
- Token-generation timing: time per output token (TPOT) or inter-token latency, which captures how quickly the response continues after it starts.
- End-to-end latency: elapsed time for the complete request.
- Queueing delay: time waiting for capacity before service begins.
AWS’s sizing guide gives example thresholds of TTFT under one second, end-to-end latency under five seconds and queueing delay under 500 milliseconds. These are examples, not industry standards; the guide’s publication year is not stated. Set targets that fit your application and validate them against real request patterns.
Will the model and its active requests fit in memory?
Check memory fit before comparing accelerator throughput. The budget includes model weights, the key-value (KV) cache used to retain attention state for active requests, and runtime overhead. Weight size is only one part of the total required by a running service.
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AWS’s sizing guide estimates that weights for a 70-billion-parameter model at FP8/INT8 require about 70 GB. The same guide uses an NVIDIA L40S with 48 GB of HBM as an example, illustrating that those weights alone exceed the memory of one such accelerator. These are source examples, not a current hardware comparison or a deployment prescription; the guide does not state its publication year.
Account for context and concurrency
KV-cache use grows with context length and the number of simultaneous requests. In AWS’s Mistral-7B illustration, a 1,000-token context uses 0.12 GB for one request and 0.49 GB for four concurrent requests. At a 16,000-token context, the illustration gives 1.95 GB for one request and 7.81 GB for four concurrent requests. These are examples from AWS’s sizing guide, not guarantees for other models or configurations.
Use the target model, context lengths and concurrency to estimate the combined footprint, then leave room for runtime needs. If the configuration does not fit, investigate placement or memory-optimization options before treating a throughput benchmark as meaningful.
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How should you benchmark the service?
Benchmark with representative prompt and completion lengths, concurrency and traffic patterns. Google Cloud cautions that requests per second can be an unreliable measure of LLM throughput when context lengths vary: a request with a long prompt or response can consume far more work than a short one.
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Record the exact model, hardware and software versions used in each run, and measure:
- TTFT, TPOT or inter-token latency, and end-to-end latency against the SLOs.
- Input-token and output-token throughput, alongside request rate.
- Queueing delay and error rate at expected and peak load.
Use the results to identify where the service stops meeting its objectives, rather than optimizing a single headline throughput number. Repeat the benchmark after changes to the model, precision, serving configuration or hardware: the new setup may shift both performance and memory use.
Which serving architecture fits your operating model?
Managed services and self-managed serving trade control and operating effort differently. AWS describes serverless, managed and self-managed paths; Google Cloud documents both managed Model Garden options and deployment on GKE. Neither source identifies a universally best choice.
| Decision factor | Managed service | Self-managed serving |
|---|---|---|
| Control and customization | Assess whether the service supports the model and configuration you need; AWS and Google Cloud document managed paths. | Assess whether the additional control and customization justify operating the serving environment yourself. |
| Operational responsibility | Evaluate which deployment and capacity tasks the service handles and which remain with your team. | Plan for the team and processes needed to operate and maintain the serving environment. |
| Capacity and availability | Check whether capacity flexibility and the availability approach meet your traffic and recovery needs. | Plan how capacity will be provisioned, monitored and recovered when demand or availability changes. |
| Security and cost | Assess the service against security requirements and total cost at your target traffic. | Assess the controls and ongoing operating costs of the environment against the same requirements. |
Make the decision against your measured workload, staffing, security requirements and total cost at target traffic. Current prices and feature availability were not established in the cited guidance, so verify them directly for the region and service you plan to use.
Which optimization levers are worth testing?
Optimization is workload-dependent. AWS and Google Cloud guidance discuss options such as quantization, tensor parallelism and memory optimization; each can change more than one aspect of the deployment.
- Quantization: test whether reducing numerical precision improves memory fit or performance without unacceptable changes in model quality.
- Tensor parallelism: test how distributing model work across accelerators affects latency, throughput and deployment complexity.
- Memory optimization: assess whether it improves the fit for weights and active-request state under the context and concurrency you expect.
Test candidates individually before combining them so their effects are understandable. For each configuration, compare quality, memory use, latency, throughput, cost and operational complexity against the baseline using the same representative workload.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should inference autoscaling respond to load?
Choose scaling signals that reflect the service objective, then tune thresholds under load. A signal that works for one traffic mix or hardware configuration may not work for another.
Queue size for throughput and cost objectives
Queue size can be a useful signal when the system can maintain its latency target as work accumulates. Google Cloud’s GKE guidance suggests it as a starting point when queue-based scaling meets latency goals.
Batch size for tighter latency needs
For more latency-sensitive traffic, Google Cloud’s GKE guidance suggests batch-size-based scaling when queue-size scaling cannot meet the tighter latency requirement. This is platform-specific guidance, not a universal rule for inference systems.
Load-test the candidate signal and thresholds with realistic request sizes and concurrency. Check whether scaling keeps TTFT, end-to-end latency and queueing delay within their targets during both expected traffic and peaks.
What belongs in a production-readiness review?
Before moving beyond a pilot, review the service as an operational system, not just a successful model endpoint. AWS and Google Cloud guidance highlight production concerns that include availability, security and responsible AI.
Quick Recap
- Reliability: verify availability expectations, error monitoring and recovery goals.
- Capacity recovery: establish how the service will respond when demand changes or available capacity is disrupted.
- Observability: retain the latency, token-throughput, queue and error measurements needed to detect when SLOs are at risk.
- Security: check the serving path and operational controls against organizational requirements.
- Responsible AI: include the relevant review and safeguards for the model’s intended use.
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