Aditya Chatterjee, Anand Joseph X M, Sanjay Singh Chauhan: One Megakernel is Not Enough: Predicting Where to Cut for Streaming Inference https://arxiv.org/abs/2610.35336 https://arxiv.org/pdf/2610.35336 https://arxiv.org/html/2610.35336
October 27, 2026 at 6:43 AM
Aditya Chatterjee, Anand Joseph X M, Sanjay Singh Chauhan: One Megakernel is Not Enough: Predicting Where to Cut for Streaming Inference https://arxiv.org/abs/2610.35336 https://arxiv.org/pdf/2610.35336 https://arxiv.org/html/2610.35336
A nine-month-old AI lab just raised a $1B Series A at a $6B valuation. Flapping Airplanes. The seed closed nine months ago at $1.5B. That is a 4x markup in three quarters.
The pitch is not a bigger model. It is a megakernel framework that overlaps memory-bound inference and compute-bound trainin...
The pitch is not a bigger model. It is a megakernel framework that overlaps memory-bound inference and compute-bound trainin...
October 24, 2026 at 3:50 PM
A nine-month-old AI lab just raised a $1B Series A at a $6B valuation. Flapping Airplanes. The seed closed nine months ago at $1.5B. That is a 4x markup in three quarters.
The pitch is not a bigger model. It is a megakernel framework that overlaps memory-bound inference and compute-bound trainin...
The pitch is not a bigger model. It is a megakernel framework that overlaps memory-bound inference and compute-bound trainin...
This tweet appeared under this Techmeme headline:
@flappyairplanes:
We've developed the world's most complete megakernel framework. It can perform entire training runs, serve inference, play video games, and even do all three simultaneously in a single kernel.
@flappyairplanes:
We've developed the world's most complete megakernel framework. It can perform entire training runs, serve inference, play video games, and even do all three simultaneously in a single kernel.
October 21, 2026 at 5:48 PM
This tweet appeared under this Techmeme headline:
@flappyairplanes:
We've developed the world's most complete megakernel framework. It can perform entire training runs, serve inference, play video games, and even do all three simultaneously in a single kernel.
@flappyairplanes:
We've developed the world's most complete megakernel framework. It can perform entire training runs, serve inference, play video games, and even do all three simultaneously in a single kernel.
Flapping Airplanes raised a $1 billion Series A to build a megakernel framework for training large models with less data.
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briefly.co
October 21, 2026 at 9:13 PM
Flapping Airplanes raised a $1 billion Series A to build a megakernel framework for training large models with less data.
Flapping Airplanes raised a $1 billion Series A to build a megakernel framework for training large models with less data.
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briefly.co
October 21, 2026 at 9:16 PM
Flapping Airplanes raised a $1 billion Series A to build a megakernel framework for training large models with less data.
Flapping Airplanes raised a $1 billion Series A to build a megakernel framework for training large models with less data.
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briefly.co
October 21, 2026 at 9:15 PM
Flapping Airplanes raised a $1 billion Series A to build a megakernel framework for training large models with less data.
I have some pi nanos and an environmental board to hook up, on my long queue of projects. it'll be managed by the swarm when it's ready. just one more megakernel and I will get to it I swear 😁
October 19, 2026 at 6:44 PM
I have some pi nanos and an environmental board to hook up, on my long queue of projects. it'll be managed by the swarm when it's ready. just one more megakernel and I will get to it I swear 😁
🚨NVIDIA just smashed 423 tok/s/user on a 2.8T parameter model, 1.68x faster with the same hardware!
Who needs sleep when you’ve got a 12k LoC Megakernel and a 10x engineer? 🚀 https://gigcitygeek.com/241623
Who needs sleep when you’ve got a 12k LoC Megakernel and a 10x engineer? 🚀 https://gigcitygeek.com/241623
October 19, 2026 at 11:59 PM
🚨NVIDIA just smashed 423 tok/s/user on a 2.8T parameter model, 1.68x faster with the same hardware!
Who needs sleep when you’ve got a 12k LoC Megakernel and a 10x engineer? 🚀 https://gigcitygeek.com/241623
Who needs sleep when you’ve got a 12k LoC Megakernel and a 10x engineer? 🚀 https://gigcitygeek.com/241623
Hyper Optimizing Kimi K3 in vLLM https://vllm.ai/blog/2026-10-16-kimi-k3-megakernel (https://news.ycombinator.com/item?id=50140784)
Hyper Optimizing Kimi K3 in vLLM: Moving Fast and Responsibly
How agent-assisted CuTe DSL optimizations accelerate Kimi K3 decode on NVIDIA GB300, and how scoped changes, correctness tests, and reproducible benchmarks help
vllm.ai
October 18, 2026 at 10:20 PM
Hyper Optimizing Kimi K3 in vLLM https://vllm.ai/blog/2026-10-16-kimi-k3-megakernel (https://news.ycombinator.com/item?id=50140784)
1. We used Argon to translate an internal version of our quantum error correction decoder, AlphaQubit, into a single, highly optimized hardware “megakernel,” nearly doubling inference throughput. Speeding up decoding is crucial, as QEC only works if the decoder keeps up with the quantum hardware.
October 13, 2026 at 4:48 PM
1. We used Argon to translate an internal version of our quantum error correction decoder, AlphaQubit, into a single, highly optimized hardware “megakernel,” nearly doubling inference throughput. Speeding up decoding is crucial, as QEC only works if the decoder keeps up with the quantum hardware.
UPDATE: Qwen 3.8 27B 140 tok/s on single RTX 3090 Megakernel: KL divergence 0.0009 vs llama.cpp
The Qwen3.8-27B CUDA megakernel achieves ~140 tokens per second on a single RTX 3090, delivering 1.4–1.9× the speed of llama.cpp. Accuracy remains hig...
The Qwen3.8-27B CUDA megakernel achieves ~140 tokens per second on a single RTX 3090, delivering 1.4–1.9× the speed of llama.cpp. Accuracy remains hig...
UPDATE: Qwen 3.8 27B 140 tok/s on single RTX 3090 Megakernel: KL divergence 0.00
The Qwen3.8-27B CUDA megakernel achieves ~140 tokens per second on a single RTX 3090, delivering 1.4–1.9× the speed of llama.cpp. Accuracy remains high, with a KL divergence of only 0.0009 relative to
techyon.pages.dev
October 11, 2026 at 9:35 PM
UPDATE: Qwen 3.8 27B 140 tok/s on single RTX 3090 Megakernel: KL divergence 0.0009 vs llama.cpp
The Qwen3.8-27B CUDA megakernel achieves ~140 tokens per second on a single RTX 3090, delivering 1.4–1.9× the speed of llama.cpp. Accuracy remains hig...
The Qwen3.8-27B CUDA megakernel achieves ~140 tokens per second on a single RTX 3090, delivering 1.4–1.9× the speed of llama.cpp. Accuracy remains hig...
🤖 Qwen3.8-27B on a single 3090: 140 tok/s on code with a custom megakernel
I've been using Claude Opus 5.5 to speed up Qwen3.8-27B on my PC (rtx 3090), it wrote a CUDA megakernel ...
https://tinyurl.com/2y94h8hj #AINews #MachineLearning #CrustyTLDR
I've been using Claude Opus 5.5 to speed up Qwen3.8-27B on my PC (rtx 3090), it wrote a CUDA megakernel ...
https://tinyurl.com/2y94h8hj #AINews #MachineLearning #CrustyTLDR
October 11, 2026 at 10:03 AM
🤖 Qwen3.8-27B on a single 3090: 140 tok/s on code with a custom megakernel
I've been using Claude Opus 5.5 to speed up Qwen3.8-27B on my PC (rtx 3090), it wrote a CUDA megakernel ...
https://tinyurl.com/2y94h8hj #AINews #MachineLearning #CrustyTLDR
I've been using Claude Opus 5.5 to speed up Qwen3.8-27B on my PC (rtx 3090), it wrote a CUDA megakernel ...
https://tinyurl.com/2y94h8hj #AINews #MachineLearning #CrustyTLDR
📰 Reddit r/LocalLLaMA
Qwen3.8-27B on a single 3090: 140 tok/s on code with a custom megakernel
https://www.reddit.com/r/LocalLLaMA/comments/1x2erdj/qwen3827b_on_a_single_3090_140_toks_on_code_with#IA##AI##ML#ML
Qwen3.8-27B on a single 3090: 140 tok/s on code with a custom megakernel
https://www.reddit.com/r/LocalLLaMA/comments/1x2erdj/qwen3827b_on_a_single_3090_140_toks_on_code_with#IA##AI##ML#ML
October 10, 2026 at 3:00 PM
📰 Reddit r/LocalLLaMA
Qwen3.8-27B on a single 3090: 140 tok/s on code with a custom megakernel
https://www.reddit.com/r/LocalLLaMA/comments/1x2erdj/qwen3827b_on_a_single_3090_140_toks_on_code_with#IA##AI##ML#ML
Qwen3.8-27B on a single 3090: 140 tok/s on code with a custom megakernel
https://www.reddit.com/r/LocalLLaMA/comments/1x2erdj/qwen3827b_on_a_single_3090_140_toks_on_code_with#IA##AI##ML#ML
3090 1枚でQwen3.8-27Bがコード生成140 tok/sらしい。カスタムmegakernelでここまで出るのか。モデルを変えなくても推論側の最適化でまだ伸びる、というのが効く話で、ローカルで補完やエージェントを回す人には体感が変わる速度帯。
https://www.reddit.com/r/LocalLLaMA/comments/1x2erdj/qwen3827b_on_a_single_3090_140_toks_on_code_with/
https://www.reddit.com/r/LocalLLaMA/comments/1x2erdj/qwen3827b_on_a_single_3090_140_toks_on_code_with/
October 11, 2026 at 12:11 AM
3090 1枚でQwen3.8-27Bがコード生成140 tok/sらしい。カスタムmegakernelでここまで出るのか。モデルを変えなくても推論側の最適化でまだ伸びる、というのが効く話で、ローカルで補完やエージェントを回す人には体感が変わる速度帯。
https://www.reddit.com/r/LocalLLaMA/comments/1x2erdj/qwen3827b_on_a_single_3090_140_toks_on_code_with/
https://www.reddit.com/r/LocalLLaMA/comments/1x2erdj/qwen3827b_on_a_single_3090_140_toks_on_code_with/
Imma do a dual card megakernel for them, but this presents the chance to do a unikernel megakernel 😅
October 7, 2026 at 11:20 PM
Imma do a dual card megakernel for them, but this presents the chance to do a unikernel megakernel 😅
Nobody has run these mega-kernel claims against a public MoE at scale. Google's numbers came from its own stack; Inspur's super-operators are unbenchmarked outside vendor hardware. Effortless efficiency rarely survives the first production trace.
Silicon Valley throws out a “mega-kernel”, Chinese team unveils “super-operators”: the efficiency war for trillion-parameter models begins
Inspur and Inferact have taken different routes—tightly coupled domestic-chip supernodes and a TPU megakernel—to optimise Kimi K3’s 2.8-trillion-parameter inference, signalling a shift from model algorithms to system-level efficiency in AI compute.
aichina.news
October 6, 2026 at 10:48 AM
Nobody has run these mega-kernel claims against a public MoE at scale. Google's numbers came from its own stack; Inspur's super-operators are unbenchmarked outside vendor hardware. Effortless efficiency rarely survives the first production trace.
doing a megakernel on the strix halo was the right call; switching between system1/system2 is seamless and latency is about as good as the hardware can manage
October 5, 2026 at 1:12 PM
doing a megakernel on the strix halo was the right call; switching between system1/system2 is seamless and latency is about as good as the hardware can manage
interim step: have the language model do a turn riffing on a prompt, that goes to the image model right next to it
will integrate into the megakernel overnight so it's almost like a system 1 image api
will integrate into the megakernel overnight so it's almost like a system 1 image api
October 3, 2026 at 9:12 PM
interim step: have the language model do a turn riffing on a prompt, that goes to the image model right next to it
will integrate into the megakernel overnight so it's almost like a system 1 image api
will integrate into the megakernel overnight so it's almost like a system 1 image api
ooh neat, I've been poking at similar ideas recently in my qwen megakernel for strix halo. current idea is to add a bunch of apis for working with these context hunks and wire an agent up to it. already in it for the custom decision apis, might as well grow some more bespoke caching protocols too
October 2, 2026 at 11:45 AM
ooh neat, I've been poking at similar ideas recently in my qwen megakernel for strix halo. current idea is to add a bunch of apis for working with these context hunks and wire an agent up to it. already in it for the custom decision apis, might as well grow some more bespoke caching protocols too
A breakthrough in AI training has arrived with Mixture-of-Kittens, a system optimized for Nvidia NVL72. The new megakernel design achieves up to 2.37x throughput over existing baselines, significantly enhancing large-scale language model training performance. https://arxiv.org/abs/2609.36070
Mixture-of-Kittens: MoE Megakernel for NVL72s
ArXiv link for Mixture-of-Kittens: MoE Megakernel for NVL72s
arxiv.org
September 30, 2026 at 8:10 PM
A breakthrough in AI training has arrived with Mixture-of-Kittens, a system optimized for Nvidia NVL72. The new megakernel design achieves up to 2.37x throughput over existing baselines, significantly enhancing large-scale language model training performance. https://arxiv.org/abs/2609.36070
Stuart H. Sul, Nash Brown, Henry Wildermuth, William Lin, Federico Cassano, Christopher R\'e: Mixture-of-Kittens: MoE Megakernel for NVL72s https://arxiv.org/abs/2609.36070 https://arxiv.org/pdf/2609.36070 https://arxiv.org/html/2609.36070
September 30, 2026 at 6:40 AM
Stuart H. Sul, Nash Brown, Henry Wildermuth, William Lin, Federico Cassano, Christopher R\'e: Mixture-of-Kittens: MoE Megakernel for NVL72s https://arxiv.org/abs/2609.36070 https://arxiv.org/pdf/2609.36070 https://arxiv.org/html/2609.36070
building a megakernel was fun! it's about the same speed as llama.cpp for tokens, while the system 1 reads are about 2x faster, landing around 150ms
all that said I should probably whip this whole thing into a shape I can upstream into llama.cpp and vllm 🤔
all that said I should probably whip this whole thing into a shape I can upstream into llama.cpp and vllm 🤔
September 29, 2026 at 12:55 PM
building a megakernel was fun! it's about the same speed as llama.cpp for tokens, while the system 1 reads are about 2x faster, landing around 150ms
all that said I should probably whip this whole thing into a shape I can upstream into llama.cpp and vllm 🤔
all that said I should probably whip this whole thing into a shape I can upstream into llama.cpp and vllm 🤔
Weave introduces a fine-grained SM scheduling system for Mixture-of-Experts inference, achieving a notable 2.89× MoE-layer speedup and reducing GPU resource waste through computation-communication overlap, redefining efficiency in large-scale AI models. https://arxiv.org/abs/2609.21483
Weave: Fine-Grained Dynamic SM Scheduling in an MoE Megakernel for Compute-Communication Overlap
ArXiv link for Weave: Fine-Grained Dynamic SM Scheduling in an MoE Megakernel for Compute-Communication Overlap
arxiv.org
September 29, 2026 at 10:30 AM
Weave introduces a fine-grained SM scheduling system for Mixture-of-Experts inference, achieving a notable 2.89× MoE-layer speedup and reducing GPU resource waste through computation-communication overlap, redefining efficiency in large-scale AI models. https://arxiv.org/abs/2609.21483
oh interesting, I have a megakernel that just got expert routing last night, maybe I'll try this
September 28, 2026 at 11:09 AM
oh interesting, I have a megakernel that just got expert routing last night, maybe I'll try this
left a goal running to build a qwen-next megakernel for strix halo, with the system 1 path baked in, so we'll see what 20% of my subscription remaining gets done tonight
September 27, 2026 at 12:18 AM
left a goal running to build a qwen-next megakernel for strix halo, with the system 1 path baked in, so we'll see what 20% of my subscription remaining gets done tonight