Building Atlas, pure Rust and CUDA/ROCm kernels on DGX Spark & Strix Halo | MLPerf Agentic Edge Taskforce | Ambassador @Alibaba_Qwen | Ambassador @DevinAI
Joined March 2026
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A break from the usual... as a @DevinAI ambassador, I co-hosted a hackathon at @GeorgiaTech, met the most cracked builders on campus 🔥
100+ students, 7 hours, and >25 shipped projects 🚀
Thank you @dabit3, @cognition, @HackerHouseCo 🐝
"I no longer think we're going to be able to just patch all the bugs." - Sam Altman
We love @vllm_project, but 40 advisories in 6 weeks. 5 rated high, 3 let an attacker run code on YOUR server.
Atlas is just Rust + CUDA/ROCm kernels, no dependencies. None of the 40 touch it :)
Attention robotics fans 🚨
VLA's like @physical_int Pi05 are coming to Atlas 🤖
Watch Atlas control its first robot on GB10 solely through the model itself and the vision feeds ⚙️
DGX Sparks have many use cases beyond text! AGX Thors aren't the only physical edge devices 🫢
Just landed TP=2 for two DGX Sparks on Qwen3.8-Flash-Next (shoutout @jmacftw!)
~2x faster tok/s vs one spark, agg decode: 193.4 tok/s at C=8 (68.6 tok/s code at C=1)
FP4-local support soon. Can't wait for qwen4!
sparkrun run @atlas/qwen3.8-flash-next-nvfp4-tp2 --cluster (x)
🤖 Made with AI
Day-0 support for Nemotron-3.5-Super with DFlash2 is live for DGX Spark and AMD Strix Halo (Windows/Linux) 🎊
💚 sparkrun run @atlas/nemotron-3.5-super-vl-120b-a12b-nvfp4-dflash2
❤️ git clone github.com/Atlas-Inf/atlas && ./scripts/serve-nemotron-strix-linux.sh
@NVIDIAAI @AMD
Presenting GLM5.3-Flash TP=2 at breakneck speeds. The BEST local model to run on 2 DGX sparks 🔥
These speeds aren't using a special quant like EXL3 (@MiaAI_lab et al.) it's with native NVFP4. Merging in soon ⏰
Shoutout @jmacftw, try it today at:
github.com/Enntity/atlas/
We think that GLM 5.3 Flash is still the best local model you can run on 256G for real near-frontier work. That hasn't changed from a month ago when we started SparkGLM. We had one goal in mind: balance quality, speed, and staggered concurrency to make the strongest version of GLM53F for 2X DGX Spark that we could.
Today we can run the NVIDIA NVFP4 weights at 65.7 / 36.3 / 91.6 tok/s (code / prose / structured) single stream decode and 2760 tok/s cold prefill on a custom fork of Atlas (@AtlasInference) with generous 4 x 512K context (1.37M tokens of KV), NVME disk prefix caching (so old long prompts stay reasonably fast), and strict JSON with speculative decoding.
We think it works pretty well. We're using it every day for our production workloads and you're welcome to use it too. You can find it at github.com/Enntity/sparkglm
@LottoLabs has a simple, easy-to-use platform for us localmaxxers 🔥
THE #1 model to run on a single DGX Spark by @Alibaba_Qwen 👀
Boots in 59s 🤯 WITH MTP support on a single spark 📈
One line:
git clone github.com/Atlas-Inf/atlas.g…
sparkrun run @atlas/qwen3.8-flash-next-nvfp4
