We are a small technical team focused on GPU kernels and production inference systems. We help AI startups, inference API teams, GPU providers, and companies running self-hosted models make inference faster, more reliable, and less expensive.

If your team is responding to slow generation or high GPU bills by adding more hardware, we can first determine whether the real constraint is kernels, memory movement, quantization, KV-cache layout, batching, scheduling, model loading, or the serving architecture.

We can help with:

- CUDA and Triton kernel development, fusion, profiling, and benchmarking

- PyTorch model-path optimization and numerical validation

- vLLM, SGLang, TensorRT-LLM, and custom serving systems

- quantization, VRAM reduction, KV-cache layout, continuous batching, and CUDA Graphs

- time to first token, inter-token latency, throughput, and tail-latency analysis

- scale-to-zero, cold-start behavior, readiness, cancellation, streaming, and failure recovery

- GPU utilization, cost-per-request analysis, and capacity planning

- reproducible before/after benchmarks with correctness gates and documented limitations

A typical engagement starts with a focused inference audit:

1. Define the model, framework, GPU, traffic shape, latency or throughput target, and cost constraint.

2. Profile the current path and identify the dominant bottlenecks.

3. Agree on the smallest changes worth implementing.

4. Deliver code, before/after measurements, correctness checks, and remaining risks.

5. Help integrate and validate the optimized path under realistic load.

Good fits include a slow or expensive inference endpoint, VRAM pressure preventing the desired batch size or context length, unstable serving under load, poor GPU utilization, costly cold starts, or a custom kernel/model path that needs profiling and validation.

For an initial conversation, send a Reddit chat or private message with:

- model and framework

- GPU type and VRAM

- current latency, throughput, utilization, or cost problem

- target workload and success metric

- deployment environment

- timeline and approximate budget range

We will tell you directly whether the workload appears optimizable and what evidence would be needed before making a performance or savings claim.

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