Zack Li

Introducing 𝙻𝙵𝙼𝟹: our new generation of Liquid Foundation Models. With a new tri-hybrid architecture and new multimodal encoders, the LFM3 family is the ideal model suite for specialized intelligence. > 6 model instances, across text, audio, vision, and omni modalities, generative and decision models. > 𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱: text, 256k context window. > 𝙻𝙵𝙼𝟹-𝚅𝙻-𝟸𝙱: vision-language model with video-input support for spatial understanding > 𝙻𝙵𝙼𝟹-𝙰𝚞𝚍𝚒𝚘-𝟸𝙱: tool calls, voice chat, reasoning in 18 languages. > 𝙻𝙵𝙼𝟹-𝙾𝚖𝚗𝚒-𝟸𝙱: an omni model combines vision, audio, and language. > 𝚍𝟷-𝟸𝙱 and 𝚍𝟷-𝙾𝚖𝚗𝚒-𝟸𝙱: decision models with typed outputs and calibrated probabilities in a single pass. Omni adds audio input. > fine-tune on 1 GPU. > a new tri-hybrid architecture > architecture optimized for 14 processor classes across GPUs, CPUs, and NPUs. > 𝙻𝙵𝙼𝟹-𝚅𝙻-𝟸𝙱 is a beast. It performs competitively with Gemma 4 12B (6x larger), and Gemini 3.5 Flash Lite, and GPT-5.6 Luna, with 1.9B parameters. > 𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱 is #1 on the Pareto frontier of device intelligence 𝚙𝚒𝚙𝚎𝚝𝚝𝚎.𝚕𝚒𝚚𝚞𝚒𝚍.𝚊𝚒 Every modality for specialized intelligence. Pick the one that fits your use case, customize it, and run it on-device. 1/ 🧵

A hand-typed analysis for optimizing LLM inference with agent fleets. There has been a lot of recent news about that - but many are kinda vague, so I think a more technical post is in order. To explain why this may claim credibility: I drew conclusion from our own DeepSeek 4.1 Flash optimization, which reached a per-step time of 2.41 ms, and a generation speed of up to 2300 tok/s (with Dspark) and reliably around 1800 tok/s for agent tasks. Agents ran ~230 optimization proposals and 94 of them were verified to be useful. My 2 cents from those runs below: 1. Lots of gains beyond "kernel optimization". More than 50% of the optimizations were shipped with no new kernel but configs / graphs / launch order and other strategies. Kernels have been a well beaten path but there is a lot to be gained in the other parts of the end to end system. 2. MegaKernel might be a lie. On non-trivially large models, megakernel is trying to take to big a bite. Most of our experiment showed that glueing small ops into existing kernels, or fusing multiple small kernels into a medium one, is getting the biggest gain. This may only apply to NVIDIA GPUs where the underlying driver is very optimized, though, as @vllm recently showed good results with megakerkenl on TPU. 3. KernelBench and real serving are different problems. Many kernels measured positive in isolated runs but turned out negative when combined in end-to-end. I've always wanted a good simulation like Accel-Sim, but so far simulation don't have a good accuracy when modeling complicated latency patterns with both compute and memory bounds. 4. Verification becomes *really* important if you don't want to be glued to your desk. If you can define "what is correct", like numerical tests, end-to-end quality evaluations, then agents can run for a long time. We didn't interfere with the run other than (a) adding GPU machines, (b) ensure token limit aren't hit, and (c) increasing credit card spending limit. 5. Don't worry, it'll converge - I think the community has consensus that once you set up the goal, agents will eventually converge. Convergence speed is gonna be key though, and 4(c) above is really real. 6. At low concurrency settings, low level assembly writing such as SASS edits have less effect, mainly because waiting for memory (and cache?) becomes the main bottleneck. 7. The hot paths are kinda solved, although much more is to be explored. I think the biggest gain in the next couple months will come from optimizing the wider distributed serving stack, in addition to the single serving engine itself. This would probably involve a lot of system and SRE insights: building proper telemetry, building proper A/B testing without disrupting production traffic, kinda similar to the good old recommendation systems do. 8. Agents doing end to end optimization is a necessity. In our case agents diligently went through 230 experiments, each of which might take a person a week (or at least a couple days with manual experiments + claude code). The sheer volume and speed definitely allows us to explore all possible optimizations otherwise not possible due to human bandwidth. My last thought is a bit contradictory: so far, it seems that agents are "good engineers", in the sense that they can massively carry out known insights. In a way, they are "interpolating" what we already know, but just way faster. I haven't seen agents design awesome algorithms like PagedAttention or Winograd, yet. But who knows, extrapolating from our existing knowledge base may well be very possible in the upcoming months. And man, I like typing on a keyboard, although it might look much worse than a well trained AI writer.

I'm happy to share that Mojo and MAX are now fully open - for development, extension, and commercial use! This includes the full Mojo programming language, but also the MAX graph compiler, MLRT runtime internals, and hardware abstraction logic. Get your agents going!👇

Spent years watching models ace a single image and completely whiff on anything that moves or changes angle. Today we’re fixing that. Try Glimpse now, our first visual thinking model with competitive frontier performance compared to Astra on key public visual reasoning benchmarks.