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Gemma 2B runs on 5KB x86-64 assembly engine

By PulseAugur Editorial · [1 sources] ·

A developer has created a highly optimized inference engine for the Gemma 2B language model, written entirely in x86-64 assembly. This engine boasts a minimal 5.2 KB binary footprint and achieves approximately 4.6 tokens per second on a quad-core i5 CPU using FP16 precision. The project, named PULSAR-ASM, aims to explore the bare-metal requirements for running modern transformers and serve as a reference for resource-constrained microcontrollers, eschewing traditional runtimes like C/C++ or PyTorch. AI

IMPACT Demonstrates extreme optimization potential for LLMs on resource-constrained hardware.

RANK_REASON Developer-created optimization of an existing model for minimal footprint. [lever_c_demoted from research: ic=1 ai=1.0]

Read on r/LocalLLaMA →

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

Gemma 2B runs on 5KB x86-64 assembly engine

How we ranked this

Signal score
12 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
Developer-created optimization of an existing model for minimal footprint. [lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
infra, model release
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

Full methodology in our editorial standards.

COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/tom_tsai28 ·

    [Discussion] A 5KB pure x86-64 assembly engine for Gemma-2B (FP16, 4.6 tok/s on CPU)

    <!-- SC_OFF --><div class="md"><p>Hi everyone,</p> <p>Sharing a personal project exploring the minimal bare-metal footprint required to run an autoregressive LLM.</p> <p>Instead of relying on large runtimes or compiler abstractions, I wrote an inference engine for Gemma-2B entire…

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