Optimize & Deploy
15x Batch Inference Throughput
Continuous batching with priority scheduling and KV cache optimization for Mistral-7B on commodity CPU hardware.
The problem
CPU inference servers struggle to serve mixed interactive and batch traffic without either latency spikes or wasted capacity.
- An interactive request waits behind a batch job that started thirty seconds earlier.
- You over-provision for peak load because the server can't juggle both traffic types.
- Batch throughput is fine until real users show up, then it falls apart.
What NEO built
NEO built continuous batching with priority-preemption scheduling, block-based KV cache management, and grammar-constrained decoding for Mistral-7B on commodity CPUs.
The result
15x throughput
Delivered 15x throughput (18.7 req/s vs. 1.2 baseline) with sub-500ms interactive latency and 100% valid JSON output.
From the blog · 8 min
15x Throughput Improvement: Batch Inference Optimization for Mistral-7B on CPU
How NEO built a production Mistral-7B inference server with continuous batching, priority scheduling, and KV cache optimization to achieve 15.6x throughput improvement and 165ms median latency on commodity CPU hardware.
Try this in your workspace
Paste this into NEO chat to kick off the same workflow on your own data.
Optimize my CPU inference server to handle mixed interactive and batch traffic with continuous batching and KV-cache reuse, without interactive requests taking a latency hit.
Paste it in · review the plan · get the diff
Get NEO
