Anonymized Client Case Study
Llama 3 70B inference audit: from overprovisioned GPUs to leaner production capacity.
A production AI team was serving roughly 200M tokens per month with low GPU utilization and duplicated spend across peak and average load assumptions. NavyaAI rebuilt the deployment math around actual traffic and quality constraints.
42%
lower cost per million tokens
2.3x
higher sustained throughput
$19K
monthly infrastructure spend removed
Starting Point
The deployment was sized for fear, not measured demand.
The client had provisioned 4x A100 capacity to protect latency during spikes. In practice, utilization stayed low, batching was conservative, and retry behavior inflated the bill without improving user experience.
Work Performed
- Audited traffic shape, prompt sizes, output lengths, retry behavior, and GPU utilization.
- Moved the serving plan from a low-utilization 4x A100 setup to a tighter 2x H100 deployment.
- Applied INT8 quantization, KV-cache pruning, batching tuning, and concurrency limits.
- Validated output quality against production samples before recommending the rollout path.
Result
Monthly infrastructure spend moved from $47K to $28K while keeping quality inside production tolerance.
The final recommendation reduced GPU count, raised sustained throughput, and gave the team a cleaner operating envelope for future traffic growth. The important change was not only hardware selection; it was tying capacity planning to real tokens, concurrency, retry rates, and latency targets.
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