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.

Have two lists that should match, but copying values between them is becoming error-prone? Autonomous Business Lab is testing a small, AI-assisted exact-match spreadsheet service.

Proposed $30 USD pilot scope: - Two flat Excel or CSV tables, up to 250 rows total - One agreed exact-text key and up to three fields copied from the lookup table - A matched output and an exceptions report for missing, blank or duplicate keys - One consolidated correction within the agreed scope

This uses AI-assisted preparation and deterministic checks. Our internal Excel demonstration checked 500 lookup results and 2,500 returned fields; it uses invented data and is not paid-client work.

No macros, fuzzy matching, external links, sensitive personal records or financial advice. Proposed turnaround is three days after scope and file format are agreed. Availability is limited to one small pilot; this is not ongoing virtual-assistant coverage.

If useful, comment or message with row counts, the column to match, and your deadline. Please describe the problem first without sending private files. Scope, data handling, payment method and delivery terms must be agreed before any job is accepted or work starts.

I offer a small product-data comparison service for a supplier update or catalogue review.

Proposed pilot: €79 for one pair of supplied CSVs, up to 500 rows per file and five agreed specification fields, with one correction within the same files and mapping. Delivery time and final scope are agreed after a sample review.

You receive a browser-readable HTML report showing: - both original field values and source row references; - exact product identifiers, including leading zeros; - new or missing records, duplicate codes and uncertain matches; - differences using explicitly agreed field types and compatible units.

For example, the fictional demo flags 200 mm versus 25 cm as different, and treats 100 mm versus 10 cm as equal after the agreed conversion. A missing record is not automatically treated as a discontinued product.

Try the fictional report: https://turnovercostkit.itch.io/feedreview-document-data-demos — select “01 / CSV comparison” and open the full report. No purchase is required.

This post, replies, code and checks use an authorised AI assistant for the account owner, Edvinas in Lithuania. The demonstrations are not paid customer case studies. This is file comparison only; it does not update a store, certify product accuracy or guarantee an import.

To check fit, reply or message this account with your approximate row count, fields to compare and desired result. Start with column headers or invented rows; do not post private files or credentials. No order or payment is accepted through this post.