But the KV cache is created for each transformer... layer. By sending each layer’s KV cache after it’s computed, we overlap communication with computation. We stream the KV cache and hide the network delay. We achieve a 4x speedup in prefill & 3x in decode, with 0 network delay.show more

🎥 Video generation is hitting the memory wall. As... videos get longer, the KV cache quietly explodes — and long-horizon consistency starts to break. We built Quant VideoGen: a training-free KV cache compression method for auto-regressive video diffusion. Instead of storing every KV in high precision, QVG exploits video’s spatiotemporal redundancy with semantic-aware smoothing + progressive residual quantization. 🚀 Up to 7× KV memory reduction ⚡ <4% overhead ✅ Strong long-video quality 🕹️ Deploy HYWorldPlay on your own RTX 5090 locally KV compression is becoming a core scaling primitive — not just for LLMs, but for video generation too. Paper: Code: (1/5)show more

KV cache yada turboquant yada yada compression something something... $DRAM $MU $SNDKshow more

🚀 Self-speculation brings 6.75x real speedup for LLM generation... with SGLang inference! Same model drafts future tokens in Diffusion mode → then verifies them in AR (causal) mode. One model and one KV cache. Just different attention masks. Thanks to perfect alignment, we get 2× longer acceptance lengths than MTP techniques (Eagle-3, MTP, dFlash). We run 2 forward passes… but the 2× higher acceptance means we break even - and with zero overhead from extra drafter, KV cache, or LM head that comes with MTP - those are not free. Last week we released Nemotron-Labs-Diffusion + Tri-mode LLMs! We did continued pre-training on Ministral-3 models by switching attention patterns (block causal bidirectional). Result: one model that runs AR mode, Diffusion mode, and Self-Speculation. Diffusion mode already shows high benchmark accuracy - excited to see what happens when someone beats left-to-right acceptance! 🔥 Github: Paper: SGLang inference: Try the models on HF:show more

Epic is working on a way for you to... quickly clear the games cache manually! Once you clear the cache it does point out that some games will take longer to load after cache has been cleared as it needs to download/cache again.show more

Robots can now reconstruct 3D scenes in real time... from a single RGB camera. [📍 Projects page + paper] No depth sensor. No retraining. 30 FPS. Researchers at the Imperial College London introduced KV-Tracker, a training-free method that makes heavy models like π³ and Depth Anything 3 fast enough for real-time tracking. The idea is simple. These models use global self-attention, which is powerful but computationally expensive. KV-Tracker caches the key and value pairs from selected keyframes and reuses them for new frames. That cache becomes an implicit scene representation. Result: • Up to 30 FPS • 10 to 15x speedup • Accurate 6-DoF tracking on benchmarks like TUM RGB-D and 7-Scenes • Works with monocular RGB only It also supports object-level tracking with masks and allows saving the KV-cache for later reuse. For robotics, this reduces hardware constraints and moves real-time 3D perception closer to practical deployment. Credit to Marwan Taher (Marwan Taher) at Imperial’s Dyson Robotics Lab and many others who contributed to this! 📍 Save projects page + paper for later: Video: ——- if it matters in AI or Robotics you'll read it here first:show more

My Saturday project: make HEY load faster in Ladybird... 👋🚀 Got it down from ~1 s to ~400 ms with two changes: - Our HTTP cache now supports heuristic freshness lifetime (per RFCs 9110 &amp; 9111) - We delay rendering until we have all CSS imports (no more FOUC!)show more

🌐 The #Streamr Network 1.0 mainnet is GO for... launch in 48 hours! 🚀 After 6+ years in research &amp; development, get ready for a fully decentralized, feature-complete data broadcasting network, powered by its users. We hope you’re as excited as we are! 🧡show more

Sorry for the delay of pictures and videos from... #Itzulia2024, but we had to deal with an unauthorised interview 😂show more

We’re preparing to ship thousands of humanoid robots Here,... we are showing 4x humanoid robots, each powered by its own Helix neural networkshow more

One million particles created using GitHub Spark, using the... KV data to configure and remember the properties #GitHubUniverse #WebGLshow more

#WATCH | Delhi | After attending the dinner hosted... by Rajya Sabha LoP Mallikarjun Kharge for the INDIA bloc leaders, Shiv Sena (UBT) MP Priyanka Chaturvedi says, "We socialised with each other keeping politics aside, and talked about family matters. We met each other in a good atmosphere. The unity of the INDIA Alliance is in front of you all."show more

🔥 Mariupol, attack on PS 220 kV Azovskaya, there's... a blackout in the city.show more

In a separate incident, the IDF says the paratroopers... located a cache of Hezbollah weapons.show more

the flashback on the new Cache

#VishwakSen's birthday celebrations on the sets of #Funky, joined... by Anudeep KV and #KayaduLohar.show more

FMPONE COOKED WITH CACHE HOLYYY

We are excited to partner with Cysic to add... compute capacity to the Nexus network. The strategic partnership will connect Cysic with the growing Nexus network to drive more efficient and more secure computation. Additionally, we’ll explore collaborating on hardware improvements for zero-knowledge systems.show more

cache-cache à paris

Last night's Russian Geran-2 drone attack focused mainly on... Ukraine's energy infrastructure. As a result, the following targets were hit: 154/35/27.5/10 kV substation in Kavuny, Mykolaiv Oblast (47.91593, 31.27147). 330/110/35/10 kV substation in Nizhyn, Chernihiv Oblast (51.03075, 31.95625). 110/35/10 kV substation in Berezivka, Mykolaiv Oblast (47.21555, 30.91465). 110/27.5/10 kV railway substation in Zhmerynka, Vinnytsia Oblast. Other railway electrical infrastructure in Kirovohrad Oblast.show more

Today, we’re excited to announce a partnership with Mind... Network. Both Nesa and Mind Network specialize in decentralized security, and we share a mission to bring this tech to crypto AI. Together we will explore a close technological collaboration, sharing components of our stacks with one another. Mind Network will also be using Nesa for its AI inference. Mind Network is the first FHE Restaking Layer for AI, focused on enhancing security at the consensus and validator levels of AI networks. Nesa is the Layer-1 blockchain for AI, specializing in private inference and building infrastructure to make it easy for any application, protocol, and smart contract to fuse with AI. Look out for our AMA together and other activations soon.show more