Optimize & Deploy
15x Batch Inference Throughput
Continuous batching with priority scheduling and KV cache optimization for Mistral-7B on commodity CPU hardware.
15x throughput
The 4-step NEO workflow
- 1
Describe the task
Share the model, deployment target, and SLA constraints.
- 2
Add context for NEO
Provide sample traffic, hardware, and current bottlenecks.
- 3
NEO implements & delivers
NEO optimizes serving and returns configs plus benchmark results.
- 4
Follow up or test it out
Load-test and iterate until targets are met in staging.
Ask NEO
How to run this scenario
Make "15x Batch Inference Throughput" production-ready: compression, batching, and serving tuned for your hardware and SLAs.
Approach
What NEO focuses on
- Profile inference end-to-end and set latency/memory targets
- Apply quantization, batching, and runtime optimizations iteratively
- Validate on representative traffic before promotion
Outcomes
What you get
- Serving configs that hit your latency and cost targets
- Documented tradeoffs between quality, speed, and hardware
- A repeatable path to re-optimize after model updates
Ready to try for yourself?
Open NEO in VS Code or Cursor and describe this scenario. NEO plans the work, runs experiments, and ships artifacts you can review and iterate on.