I embedded 685M public texts in 32 minutes (on 8x A100, Rust, TensorRT)
Quick note on how it works and how I've done my batch embedding engine IgniteMS. The whole thing runs as one process using Rust, reading input, tokenizing, packing batches, keeping the queue full. TensorRT handles inference. Python is only as a wrapper. I built it this way because when you use more than couple of GPUs, the GPUs stop being the problem. CPU cannot feed them fast enough. One A100 can go through batches faster than Python can tokenize and feed, so the GPU just sits there idle waiting for work. Most of my time went into optimizing this. At 8 GPUs that was basically the entire…
I embedded 685M public texts in 32 minutes (on 8x A100, Rust, TensorRT) launched on June 4, 2026 with 7 votes, #1,808 of 22,647 launches that month and more than 75% of that year's launches.
What it does
Fast self-hosted embedding engine for search, RAG, and reindexing workloads on NVIDIA GPUs. Built in Rust + TensorRT for teams that care about scale, cost, and control. - Artain-AI/ignite-ms
256,000 msg/s on 8x A100. Up to 3.6x faster than Hugging Face TEI on same hardware. 357,893 msg/s sustained in production with workload-specific tuning. IgniteMS is a batch text embedding engine. Rust, native TensorRT, no Python at runtime. It turns large volume of texts into embeddings. Use it for workloads where millions of texts need embeddings quickly: vector DB reindexing, search rebuilds after model swaps, corpus-scale processing. p4d.24xlarge (8x A100 80GB), 1M MSMARCO passages, TensorRT 11 mixedfrom github.com
In the maker’s words, at launch
Quick note on how it works and how I've done my batch embedding engine IgniteMS. The whole thing runs as one process using Rust, reading input, tokenizing, packing batches, keeping the queue full. TensorRT handles inference. Python is only as a wrapper. I built it this way because when you use more than couple of…
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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
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