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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesScaling AI inference starts with the workload, not the model name or a preferred GPU. Measure prompt and response lengths, concurrency, traffic peaks and latency goals; check memory fit; then benchmark representative traffic before choosing a serving architecture, tuning optimizations or configuring autoscaling.
Start by describing the workload
Two deployments serving the same model can need very different infrastructure. AWS Prescriptive Guidance identifies prompt length, response length, concurrency and latency objectives as factors that change sizing. Before comparing hardware or hosting options, record the shape of the work the service must handle:
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- Model: architecture, parameter size and serving precision.
- Requests: distributions of input and completion token lengths, not just averages.
- Demand: concurrent requests or requests per second or minute, including peak periods and bursts.
- Service expectations: latency objectives, availability expectations and recovery goals.
Interactive chat, long-context document processing and offline or throughput-oriented jobs can place very different demands on the same model. A single average request rate will not capture those differences.
Define service objectives before sizing capacity
Set measurable targets for time to first token (TTFT), time per output token (TPOT) or inter-token latency, end-to-end request latency and acceptable queue delay. Also define acceptable error rates and availability expectations. These measures make it possible to tell whether a change improves the experience or merely moves work through the system differently.
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AWS’s sizing guidance offers example targets of TTFT under one second, end-to-end latency under five seconds and queueing delay under 500 milliseconds. They are examples from AWS, not industry standards or recommended targets for every application. Set targets from your users’ needs and validate them against the complete application path.
Check memory fit before optimizing throughput
A running inference service needs memory for model weights, the key-value (KV) cache and runtime overhead. The KV cache holds intermediate attention state for active requests; its footprint changes with context length and concurrency. That makes long-context workloads materially different from short prompts, even when the model is unchanged.
AWS’s sizing guide estimates about 70 GB for the weights of a 70-billion-parameter model at FP8/INT8, while its NVIDIA L40S example has 48 GB of HBM. Those source examples illustrate that the specified weights alone exceed that accelerator’s stated memory capacity; they are not a deployment recommendation, current hardware comparison or complete memory budget. AWS notes that the weight estimate excludes the additional memory needed to run the service.
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The same guide illustrates how concurrency and context affect Mistral-7B KV-cache estimates:
| Context length | One request | Four concurrent requests |
|---|---|---|
| 1,000 tokens | 0.12 GB | 0.49 GB |
| 16,000 tokens | 1.95 GB | 7.81 GB |
These are AWS-published illustrative estimates; the page’s publication year is not stated. Treat them as a reminder to model cache demand for your context lengths and concurrency, not as guaranteed usage for a particular runtime. Establish whether the weights, expected cache and runtime overhead fit before comparing accelerator throughput.
Benchmark the traffic users will actually send
Requests per second alone can misrepresent LLM capacity when prompt and completion lengths vary. Google Cloud guidance cautions that request rate may not measure LLM throughput reliably under variable context lengths. Benchmark with realistic input and output distributions and concurrency rather than relying on a single request-rate target.
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Capture at least:
- TTFT and TPOT or inter-token latency;
- end-to-end request latency and queue delay;
- input-token and output-token throughput;
- error rates; and
- the exact model, accelerator hardware, serving software and configuration used.
Test representative traffic patterns, including peaks and bursts, and compare results against the SLOs. A benchmark is useful for capacity planning only when its workload and deployment details are recorded well enough to reproduce it.
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Choose a serving approach around operating needs
Managed services and self-managed clusters are alternatives with different trade-offs, not a universal best-to-worst ranking. AWS describes serverless, managed and self-managed paths; Google Cloud documents both GKE deployment and managed Model Garden options. Evaluate the responsibilities and constraints that matter to your organization:
| Decision area | Managed service | Self-managed serving |
|---|---|---|
| Control and customization | Assess whether the service supports the model, configuration and controls you require. | Assess whether the additional control is worth the work of operating and maintaining the serving stack. |
| Operational staffing | Determine which infrastructure tasks the provider handles and which remain with your team. | Plan for the people and processes needed to operate the cluster and serving system. |
| Capacity and availability | Verify capacity flexibility, availability behavior and recovery options for the specific service. | Design and validate capacity management, availability and recovery for your environment. |
| Security and responsible AI | Check that provider capabilities and configuration meet your requirements. | Define and operate the controls required for your environment and application. |
| Total cost | Estimate cost for the target traffic and the service configuration you expect to use. | Include the cost of infrastructure as well as the operational effort required to run it. |
Feature availability, capacity options and prices depend on the service, region and time; verify them for the deployment under consideration. The guidance does not establish a universally superior hosting model.
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Test optimization choices against quality and service goals
Quantization, tensor parallelism and memory optimization can change the memory footprint, latency, throughput, output quality and operating complexity. Their effects depend on the model and workload, so assess candidates using the same representative benchmark and SLOs rather than assuming a technique will improve every dimension.
- Establish a baseline for the chosen model, hardware and serving configuration.
- Apply one optimization at a time where practical, recording configuration and software versions.
- Measure memory use, TTFT, token-generation timing, end-to-end latency, throughput and errors.
- Check output quality for the application as well as performance and cost before adopting the change.
Autoscale on signals tied to the SLO
Autoscaling thresholds should be validated under load because request sizes and hardware affect how quickly a queue or batch grows and how much work each instance can handle. The signal should reflect the objective you need to protect, not simply be the easiest metric to collect.
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- Batch size: The same GKE guidance points to batch-size scaling when queue-based scaling cannot meet tighter latency requirements.
This is platform-specific guidance for GKE, not a rule for every inference system. Load-test thresholds with representative request lengths and concurrency, then verify that scaling behavior keeps latency within the target while handling demand changes.
Include production readiness in the scaling plan
Capacity is only one part of a production inference service. Before rollout, review availability and capacity recovery, observability for latency, throughput and errors, security controls, and responsible AI requirements. Recheck the service objectives and operating assumptions as traffic or application behavior changes; a configuration sized for one request mix may not suit a later one.
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