Inference Optimization

I optimize LLM inference pipelines to dramatically reduce latency and cost while maintaining output quality. This is ideal if your model is too slow, too expensive, or not production-ready yet.

What you’ll get:

1. Bottleneck analysis (GPU, memory, KV cache, tokenizer, IO)
2. Quantization strategies (INT8, INT4, mixed precision)
3. Kernel & runtime optimization (TensorRT-LLM, vLLM, FlashAttention)
4. Batching, caching & streaming improvements
5. Throughput tuning for real-world traffic

Best for:

Production LLM apps, high-traffic APIs, on-device or edge deployments.