Alternatives
Products that do what I built a toy TPU that can do inference and training on the XOR problem does
We wanted to do something very challenging to prove to ourselves that we can do anything we put our mind to. The reasoning for why we chose to build a toy TPU specifically is fairly simple: - Building a chip for ML workloads seemed cool - There was no well-documented open source repo for an ML accelerator that performed both inference and training None of us have real professional experience in hardware design, which, in a way, made the TPU even more appealing since we weren't able to estimate exactly how difficult it would be. As we worked on the initial stages of this project, we…
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Tom from Tensil here - happy to answer questions! We developed Tensil to bring custom ML accelerators to people who don't have the resources of companies like Google, Facebook and Tesla. Currently, we're focused on supporting convolutional neural network inference on edge FPGA (field programmable gate array) platforms, but we aim to support all model architectures on a wide variety of fabrics for both training and inference. Tensil is different from other ML accelerators in that it is open source and really easy to use. For example, you can generate a custom accelerator with one command: $…
2022 · tensil.ai
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We wrote our inference engine on Rust, it is faster than llama cpp in all of the use cases. Your feedback is very welcomed. Written from scratch with idea that you can add support of any kernel and platform.
2025 · github.com
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2018 · actcast.io
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A 3.16M-parameter INT4 transformer running entirely in the on-chip memory of a Xilinx Kria KV260. Zero DRAM in the token loop, 59,965 tok/s on the fabric, bit-exact. Chat with it live.
27d ago · mikeayles.com
- 7RX
Hi all, long time lurker, first time poster. I want to share with you all something we've been working on for a while at Lambda: the Razer x Lambda Tensorbook: https://www.youtube.com/watch?v=wMh6Dhq7P_Q But before I tell you about it, I want to make this all about me, because I built this for me. See, while I'm genuinely interested in hearing from the community what you think as this is the culmination of a lot of effort from a lot of people across so many different fields (seriously, the number of folks across manufacturing, engineering, design, logistics, and marketing who…
2022
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Hey HN! We're building an open-source CMS designed to help creators with every part of the content production pipeline. We're showing our tiny first step: A tool designed to take in a Twitter username and produce an "identity card" based on it. We expect to use an approach similar to [Constitutional AI] with an explicit focus on repeatability, testability, and verification of an "identity card." We think this approach could be used to create finetuning examples for training changes, or serve as inference time insight for LLMs, or most likely a combination of the two. The tooling we're…
2025 · contentfoundry.com
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Everything that would be here is in the README. I hope this gets big, it has tons of potential.
2013 · github.com
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Hi HN, I'm Paul from Tensordyne. We build AI inference systems and chips on logarithmic math. We've put together an interactive Token Economics Calculator to help make apples-to-apples comparisons of inference hardware across vendors: We're interested in how closely it lines up with the community's view of the market. Why we built this Investors and customers kept asking how our system compares to others (NVIDIA and a growing list of startups). Plenty of publicly available data exists, but it's scattered and inconsistent. News articles, provider sites, Artificial Analysis, MLCommons, and now…
Nov 2025 · tensordyne.ai
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Most inference UIs that I've come across pretty much just give us a chat-like interface to toy around with models in a single visual conversation thread. Given the fact that we are limited to seeing only one output at a time, it's kind of hard to compare outputs from different models, adjustments made to the prompting, and sampler settings. But even when keeping the generation parameters the same (e.g., to test for reliability in the output) and just going for multiple passes, there is no easy way to have a side-by-side comparison to keep track of the outputs from the multiple "rounds". I…
2024 · github.com
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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…
13d ago · lpulite.com
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I've been tired with the current options on the market for awhile and decided to do something about it after the running into the disaster that is MLOps at my last two startups and having to manage a multiple operation platforms both for my fellow ML engineers, the general application CI/CD and orchestration layers while simultaneously building the application itself. Its still extremely early for the product but its functioning and is well on its way. I'd love feedback on the approach and peoples thoughts on the problem space. Personally my irritations have been in the poor tooling,…
2025 · egdaemon.com
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Aug 2026 · github.com
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I wanted to build an inference provider for proprietary AI models, but I did not have a huge GPU farm. I started experimenting with Serverless AI inference, but found out that coldstarts were huge. I went deep into the research and put together an engine that loads large models from SSD to VRAM up to ten times faster than alternatives. It works with vLLM, and transformers, and more coming soon. With this project you can hot-swap entire large models (32B) on demand. Its great for: Serverless AI Inference Robotics On Prem deployments Local Agents And Its open source. Let me know if anyone…
Nov 2025 · github.com
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High performance storage engine for efficient LLM inference and GPU Training.
20h ago · theopenlake.com
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I’ve spent the last few months building a deep learning engine completely from scratch in Python (using only math and random). What started as a basic linear algebra calculator project grew into a symbolic tensor system with autodiff, custom matrix ops, attention mechanisms, LayerNorm, GELU, and even a text generation demo trained on the Brown corpus. I'm still an undergrad, so my main goal is to deeply understand how deep learning actually works under the hood - gradients, attention, backpropagation, optimizers - by building it step-by-step with full visibility into everything, and without…
2025 · github.com
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Hey HN, I’m a dev and a dad to a 10-year-old. I built this because I caught my daughter using ChatGPT to do her history homework. She wasn't learning; she was just acting as a "middleware" between the AI and the paper. The Backstory: I realized the problem isn't the AI—it's the zero-friction answers. Most "AI for kids" apps are just "parrots"—they mimic intelligence by repeating patterns. What’s Different: Qurio is a "Bicycle" for the mind. It treats the child like a future "Architect" rather than a "Junior Executor." Technically, it wraps an LLM in a strict "Socratic Loop." It detects…
Feb 2026 · thinkqurio.com
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Hi HN friends, we're Nima (nimabanai) and Craig (cbschind) from Assemble Labs (https://assemblelabs.co) building the hardware context layer for AI to help you write better firmware faster. We’ve built an MCP server that plugs into any AI tool you’re using (Cursor, Claude Code, Gemini, etc.) and brings complete hardware context (schematics, datasheets, etc.) to your existing environment (new app fatigue is real...) with accuracy and in real time. Our goal is to make writing and debugging firmware on custom hardware faster and easier. Try out the free beta release:…
Oct 2025
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You know that old TI calculator you used in high school, then put in a box and forgot about? Have you ever wished you had an operating system for your calculator with preemptive multitasking, dynamic memory management, a tree filesystem conforming to the FHS, and all the comforts of Unix? Well, good news: that's totally a thing that exists. I've been working on my kernel for about three and a half years now and I'm looking for new contributors to help out. It's written entirely in z80 assembly, and it's both challenging and fun to work on. There's an IRC channel for contributors or people…
2014
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I've been working on training this small vision language model for the last month - excited to release the first prototype today! It is based on SigLIP (image encoder), Phi-1.5 (text model) and trained using the LLaVa-1.5 training dataset. It runs reasonably fast on CPU with ~8GB of RAM in full 32-bit precision. There's plenty of room to speed it up and reduce memory consumption by quantizing the model. I posted a video of it running on my M2 Macbook Air (on CPU not MPS, so performance should be comparable on other hardware) on Twitter to demonstrate inference speed:…
2023 · github.com
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I’m 15 and self-taught. I'm learning ML from scratch because I want to really understand how things work. I’m not into frameworks. I prefer math, logic, and C++. I implemented a basic MLP that supports different activation and loss functions. It was trained via mini-batch gradient descent. I wrote it from scratch, using no external libraries except Eigen (for linear algebra). I learned how a Neural Network learns (all the math) -- how the forward pass works, and how learning via backpropagation works. How to convert all that math into code. I’ll write a blog soon explaining how MLPs work in…
2025 · github.com
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Hi HN, I built OpenGraviton, an open-source AI inference engine designed to push the limits of running extremely large models on consumer hardware. The system combines several techniques to drastically reduce memory and compute requirements: • 1.58-bit ternary quantization ({-1, 0, +1}) for ~10x compression • dynamic sparsity with Top-K pruning and MoE routing • mmap-based layer streaming to load weights directly from NVMe SSDs • speculative decoding to improve generation throughput These allow models far larger than system RAM to run locally. In early benchmarks, OpenGraviton reduced…
Mar 2026 · opengraviton.github.io
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