I ran a 7B Mixture-of-Experts LLM in a language I built — token-identical to fp32
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I've been building machin (MFL), a machine-first language, and using it to write an LLM inference engine with zero dependencies — no PyTorch, no llama.cpp, no BLAS, no Python at runtime. Just a static binary. The wall A dense 1B model got me ~20 tok/s on a laptop CPU. That's the ceiling, and it's fundamental : decode speed is set by bytes moved per token , and on weak hardware you're pinned to the memory bus. I built and measured every trick — speculative decoding, int4, contextual sparsity,…
1Key Takeaways
- I've been building machin (MFL), a machine-first language, and using it to write an LLM inference engine with zero dependencies — no PyTorch, no llama.cpp, no BLAS, no Python at runtime.
- The wall A dense 1B model got me ~20 tok/s on a laptop CPU.
- That's the ceiling, and it's fundamental : decode speed is set by bytes moved per token , and on weak hardware you're pinned to the memory bus.
- I built and measured every trick — speculative decoding, int4, contextual sparsity,….
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that i've been building machin (MFL), a machine-first language, and using it to write an LLM inference engine with zero dependencies — no PyTorch, no llama.cpp, no BLAS, no Python at runtime.
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