A 6M-token movable window on a single 46GB GPU
Abstract page for arXiv paper 2607.23806: A Frozen 12B Beats Frontier Models on Verified Work: 100% Accuracy, 0 Tokens, Bit-Exact, Forever
A 6M-token movable window on a single 46GB GPU launched on July 28, 2026 with 7 votes, #2,664 of 24,623 launches that month and more than 75% of that year's launches.
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I've built a locally running Perplexity clone2024 · github.com · ▲669The video demo runs a 7b Model on a normal gaming GPU. I think it already works quite well (accounting for the limited hardware power). :)
- ATA tiny LLM running at 21,000 tok/s on a $250 FPGA (Live Demo)Aug 2026 · mikeayles.com · ▲79
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.
Run Qwen3-Next-80B on 8GB GPU at 1tok/2s throughput2025 · github.com · ▲123Contribute to Mega4alik/ollm development by creating an account on GitHub.
Fine-tune an 8B model on a 4 GB laptop GPUAug 2026 · github.com · ▲139Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU. - MakazhanAlpamys/Soup
Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPUJun 2026 · apeg.dev · ▲10I wanted to know how fast a 26B mixture-of-experts model could run on a desktop CPU with no GPU. Got ~40 tok/s single-stream (lossless) and ~124 batched. The surprising part was the byte budget: for this model you compress the output head (32% of per-token bytes), not the experts (16%). The writeup has the bandwidth roofline and the dead-ends; the repo has the reproducible recipe. Happy to answer questions. Repo: https://github.com/arun-prasath2005/gemma4-cpu-moe
Shoehorn, a library to quantize an LLM to fit your Mac's VRAMAug 2026 · github.com · ▲6I made this after seeing someone posit the idea online yesterday over lunch then spent some time refining it. So far it's pretty impressive IMO! Right now I am running Qwen3-30B-A3B on my 24gb unified memory m4 MacBook Pro at 50 tok/sec and this should definitely not be working for such a large model on my middling hardware. Things are detailed in the README to get up and running and DESIGN.md has details on all the choices and such made along the way.
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