Developer boosts C LLM inference speed by 25x, hitting DRAM limits

By PulseAugur Editorial · [1 sources] · 2026-06-25 00:37

A developer details the process of optimizing a C-based LLM inference engine, Project Zero, to achieve significantly faster performance on CPUs. Initially running BitNet b1.58 at 1.4 tokens/second, the project evolved over nine months to reach 36.25 tokens/second on a Xeon processor, nearing the DRAM bandwidth ceiling. The optimization journey involved removing ML frameworks, leveraging specific CPU instructions like AVX-512 and VNNI, and addressing hardware bottlenecks such as memory bandwidth and thermal throttling. AI

IMPACT Demonstrates significant potential for CPU-based LLM inference, reducing reliance on GPUs and specialized hardware.

RANK_REASON Detailed technical post about optimizing LLM inference on CPUs, focusing on performance tuning and hardware limitations. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

infra
other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Developer boosts C LLM inference speed by 25x, hitting DRAM limits

COVERAGE [1]

dev.to — LLM tag TIER_1 English(EN) · Shifu · 2026-06-25 00:37

From 1.4 tok/s to 36 tok/s: What Building a Zero-Dependency C LLM Engine Taught Me About DRAM Ceilings

<h1> From 1.4 tok/s to 36 tok/s: What Building a Zero-Dependency C LLM Engine Taught Me About DRAM Ceilings </h1> <p>I started Project Zero with a single question: how fast can you run BitNet b1.58 inference on a CPU if you write everything in C and skip every ML framework?</p> <…

COVERAGE [1]

From 1.4 tok/s to 36 tok/s: What Building a Zero-Dependency C LLM Engine Taught Me About DRAM Ceilings

RELATED ENTITIES

RELATED TOPICS