I built a lite LPU that can do inference on Karpathy's MicroGPT
Details
- External ID
- 49423735
- Source
- HN
- Company
- —
- Product
- I built a lite LPU that can do inference on Karpathy's MicroGPT
- Website domain
- lpulite.com
- Launched
- Aug. 24, 2026
- Cohort
- —
- Upvotes
- 18
- Upvotes percentile
- 0.7318548387096774
- Tags
- —
- Fetched at
- Sept. 10, 2026, 5:32 a.m.
- Updated at
- Sept. 10, 2026, 5:32 a.m.
Description
We had no guide or course that teaches chip design at our university. We had taken a digital logic course, but were disappointed with the fact that the most complex project we did was building a full adder in Quartus using logic blocks, not even in RTL!5 Therefore, we decided to challenge ourselves to dive deep into machine learning (ML) hardware and learn as much as we could on our own. We wanted to prove that basic math (like y = mx + b) and basic logic circuits are enough to help anyone understand how modern AI hardware works.Our goal was to design our own version of the LPU from scratch and run a simple Transformer-style model on it, proving that with minimal Machine Learning and computer design knowledge, it’s totally possible. We were also driven by a simple question: What makes the LPU architecture so compelling that even Nvidia licensed it?Keep in mind, this article is not intended to serve as a tutorial for “how to build an LPU from scratch,” and our architecture is not a 1:1 LPU. It serves as an educational resource for how someone with minimal hardware experience can approach this field, and our journey in building what we think an LPU would look like.
Enrichment
- Theme
- lightweight and on-device AI runtimes
- Vertical
- Horizontal
- Function
- Model & infra
- Audience
- Developer
- AI stance
- AI-native
- Project type
- Hobby / open-source project
- Normalized one-liner
- lightweight inference accelerator
- Manually corrected
- False
Could you build this?
No Designing custom silicon architectures or FPGA-based processors to execute neural networks requires specialized digital logic, RTL engineering, and hardware compiler expertise.
What it would actually take: Requires writing register-transfer level hardware descriptions (SystemVerilog/Verilog or Chisel), building cycle-accurate simulators, synthesizing against FPGA toolchains, and designing an instruction set architecture (ISA). It also demands a custom deterministic compiler to schedule tensor operations and attention passes directly onto hardware functional units.
Discussion
3 comments analyzed.
Competitors
Other products that read as similar to this one — 151 launches clear the similarity bar, closest 8 shown.
Attention rank: #52 of 152 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 299 days after the earliest competitor.
- Lamb Labs: Custom Chips for AI Inference · yc · 2026-08-03 · 64 upvotes · similarity 0.47
- Standard Machines. Teaching AI to Design Advanced Chips. · yc · 2026-08-24 · 22 upvotes · similarity 0.44
- I made a game where you build a CPU from logic gates · hn · 2026-07-30 · 102 upvotes · similarity 0.43
- ZeroGPU · ph · 2026-06-09 · 308 upvotes · similarity 0.43
- MicroGPT in 243 Lines · hn · 2026-02-13 · 10 upvotes · similarity 0.43
- I built GPT from scratch to understand how it works · hn · 2026-01-14 · 7 upvotes · similarity 0.41
- SHDL · hn · 2026-01-28 · 48 upvotes · similarity 0.41
- Symbolic Circuit Distillation: prove program to LLM circuit equivalence · hn · 2026-01-06 · 16 upvotes · similarity 0.41
Other launches for this product
- No other launches for this product.
Same idea, different domain
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