Why we write our own C and C++ inference engines

That framing landed unevenly. The biggest pushback was that the post oversold performance. People dug into the vLLM versus vLLM.cpp numbers and found gains that were mostly within noise except for the single-stream case. They also called out at least one benchmark as a quantized-versus-float comparison, which says more about data size than engine quality. In other words, the article sells “write your own engine” as a speed play, then backs it with examples that often read like bug fixes, caching obvious recomputation, or changing precision. Where the piece did connect was operations. Several readers with hands-on experience said the Python stack is the real tax. Large environments, brittle CI, runtime code generation, awkward installs, and poor fit for embedded or reproducible deployments are enough on their own to justify native ports, even when throughput is only flat or modestly better. That became the practical conclusion. If a C or C++ runtime gives near-parity while collapsing a multi-gigabyte dependency graph into a small binary, many teams will take that trade. A second thread sat on top of all this and nearly overwhelmed the technical discussion. Many readers thought the prose itself looked LLM-generated and found it irritating, over-styled, and less trustworthy. A few pushed back that AI-assisted writing is not disqualifying, especially for non-native English speakers, and that the substance should stand or fall on the measurements. Even then, the writing issue bled back into the technical judgment. People were more willing to dismiss the post because the rhetorical polish felt stronger than the evidence.