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Optimize & Deploy

GGUF quantization, batch inference, edge deployment, GPU monitoring, and CPU-optimized serving for production workloads.

The problem

Models that work in the lab rarely fit edge devices or tight SLAs without quantization, batching, and serving tweaks. Each step is easy to get wrong.

  • The model that ran fine on your laptop won't fit the deployment target at all.
  • You're overpaying for GPUs because nobody's watching spot prices across providers.
  • Interactive requests wait behind batch jobs because the server can't juggle both.

How NEO helps

NEO automates compression, batching, and deployment checks so inference meets latency and memory targets on the hardware you actually run, from laptops to GPUs.

  • Tracks GPU pricing and availability across 5 clouds and alerts on price drops
  • Builds continuous-batching inference servers that hit real throughput gains on commodity hardware
  • Streams TTS output as soon as a chunk is ready, no GPU required

Scenarios (3)

15x Batch Inference Throughput
15x throughput

Optimize & Deploy

15x Batch Inference Throughput

Continuous batching with priority scheduling and KV cache optimization for Mistral-7B on commodity CPU hardware.

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CPU-Based Voice Assistant
1.25s time-to-first-audio

Optimize & Deploy

CPU-Based Voice Assistant

1.25s time-to-first-audio using sub-sentence streaming and multi-threaded TTS, no GPU required.

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GPU Scout
5 clouds, 7 GPU types tracked

Optimize & Deploy

GPU Scout

GPU Scout, a tool that monitors GPU availability and pricing across AWS, GCP, Azure, Lambda Labs, and vast.ai, alerting when spot instances drop in price or become available.

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Run this workflow in your workspace

Install NEO in VS Code or Cursor and ship training, evals, and deployment from chat.

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eval_pipeline.py
  def run_eval(model, tasks):-     results = model.predict(tasks)+     results = batch_eval(model, tasks)+     log_regression(results)      return score(results)