A hard lesson from production: in a crypto accounting engine I worked on at Bitwave (Zealsight), we hit a scalability break where ingestion lag accumulated and downstream consumers fell behind. The fix wasn’t a single “optimization”; it was adding clearer queue/backlog visibility, tightening batching boundaries, and simplifying the data path so we could reason about throughput and failure behavior under load.
I’d like to pitch a guest post to kernel.org readers—grounded in measurement, hardware reality, and systems constraints—from my current work benchmarking local LLM inference across Apple Silicon, NVIDIA, and AMD (ROCm), plus prior distributed-systems work.
Possible titles (choose one):
1) Performance: “From Backlog to Baseline: A Practical Playbook for Measuring and Regressing Throughput in Production Pipelines”
2) GPU: “Local LLM Inference on Linux: What Benchmarks Reveal About NVIDIA vs ROCm vs Apple Silicon Constraints”
3) Distributed systems: “Designing Backpressure You Can Debug: Queue Signals, Batch Sizes, and Failure Containment”
Writing samples: kunalganglani.com
If you’d rather not hear from me, say so and I won’t email again.
Kunal
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