August 2, 2026
Small file, massive comment war
Why we write our own C and C++ inference engines
Tiny app, big chaos: fans cheer the speed while critics roast the writing
TLDR: LocalAI says it rebuilt some AI tools in plain C and C++ so they install as tiny apps instead of giant software bundles, without really losing speed. Commenters split between impressed "this is actually useful" reactions and savage jokes about the blog post being harder to enjoy than the code.
LocalAI came in swinging with a simple flex: why ship a huge, messy software setup when you can ship one small file and get basically the same speed? The post claims its home-built C and C++ AI engines can match, and sometimes beat, bulkier Python-based tools while using far less storage and memory. In plain English: the team says it turned a giant 9.1 GB install into a 66 MB program, with results so close that most of the difference is basically a rounding error. That alone had performance nerds nodding along.
But the real drama was in the replies, where applause immediately collided with snark. The top drive-by roast was brutal: "Should have started with writing your own blog posts" from stephbook, a comment that instantly set the tone for the thread. Another commenter mocked the article’s writing style itself with a parody formula, basically saying the post read like a fill-in-the-blanks product pitch. Ouch.
Still, plenty of readers were very into the idea. One person bragged they saw the same kind of magic, shrinking a web-ready AI tool from 30 MB to 300 KB and making it faster too. Another said the biggest selling point wasn’t raw speed at all, but not waiting five minutes every time a rented graphics machine boots up. The split was deliciously clear: half the crowd was yelling “this is genuinely useful”, and the other half was yelling “sure, but can someone rewrite this blog post first?”
Key Points
- •LocalAI says 18 of its backends are original C or C++ ports created to avoid shipping large Python environments, CUDA-only stacks, or unsupported model implementations.
- •The article reports that vllm.cpp, a C++20 port of vLLM’s V1 architecture, installs as a 66 MiB binary versus a 9.1 GiB vLLM virtual environment.
- •In benchmarks on NVIDIA GB10 with Qwen3.6-27B in NVFP4, vllm.cpp slightly exceeded vLLM throughput at six measured points, produced token-identical output, and used less peak host memory.
- •Against llama.cpp on CPU, vllm.cpp showed faster prefill throughput while decode was reported as effectively tied; against MLX-LM on Apple M4, it was slightly ahead in prefill and slightly behind in warm total throughput.
- •The article says depth-anything.cpp, a port of ByteDance’s Depth Anything 3, ran 1.31x faster than PyTorch on CPU, used 27% of the memory, and gained much of that advantage by caching positional embeddings instead of recomputing them every forward pass.