Your MacBook can now host a coding agent at 300+ tps! I gave DeepSeek V4.1 Flash a job: write a specialized inference engine for LiquidAI's LFM2.5-24B-A2B on Apple's M5 Max. For $21 of API, it built Molten: 300+ tps, about 1.5x faster decode than mlx-lm and llama.cpp. Why specialized? General frameworks like mlx-lm and llama.cpp have to run any model on any hardware. A specialized engine serves one model on one chip at batch size 1, so there is far more room to optimize. Molten is native Rust + Metal: one ~6 MB binary, no Python, PyTorch or MLX. It comes with a chat CLI and an OpenAI-compatible server with streaming and tool calls, so you can point your own coding agent at it. It needs an M5-series Mac with 24 GB+ (sorry, M1-M4 friends). The numbers (M5 Max, 128 GB) • 305 tps at 256 tokens of context, vs 190 for mlx-lm and 199 for llama.cpp • Still 248 tps at 16K, where coding agents actually live (mlx-lm: 165) • Faster time to first token: 1.76 s vs 2.17 s for a 16K prompt • Same weights and math as mlx-lm How it was built The agent (DeepSeek V4.1 Flash in the Pi harness) ran in a recursive self-improvement (RSI) loop. Each session starts from the best engine so far, plus the feedback from every earlier round. A fixed evaluator rebuilds each candidate from source, checks its tokens against mlx-lm, times and profiles it. Only correct and faster engines survive. • Hour 3: first correct engine, 0.56x mlx-lm • Day 2.5: passes mlx-lm • Day 5.7: 1.28x end to end, 1.53x decode 78 agent sessions, about $21 (1.36B tokens, 98.6% of them cache hits). What the RSI loop found useful for speeding up: • Fuse chains of small ops into single kernels: norm + router + top-4 expert pick, expert FFN + combine, Q/K/V projections + KV-cache write, Q/K norm + RoPE + attention, LM head + argmax. • Keep the decode loop on the GPU. Tokens never round-trip to the CPU, and the GPU stays 99% busy. • Hard-code every shape, so the 4-bit kernels stream weights at ~75% of the memory bus. • Target the hardware: prefill runs on the M5 GPU's Neural Accelerators through Metal 4 tensor ops, with MoE routing and expert GEMMs fused and kept on the GPU. (It's not Rust, by the way. llama.cpp is native C++ too. It's the specialization.) TBH, I don't fully understand all of it yet lol. The agents wrote this in under 6 days, and it's going to take me longer than that to read it. As the human researcher, I'm now officially the slowest-improving component of my own RSI loop🤣, so I'll spend the next few weeks learning from my agents. Where I think this goes: local inference will matter more and more, and a specialized engine can beat a general one. As RSI matures, the cost of building one keeps falling toward zero. I think every model, every chip, and maybe every workload will eventually get its own inference engine. The idea is inspired by @perplexity_ai 's Lily. Thanks to @liquidai for the great model, and Apple's MLX, whose kernels Molten adapted from. Repo here: github.com/zhangshuibai/molt…

🚀 Excited to introduce StreamRender-H3 — bringing real-time interactive worlds to life with diffusion models! Built on Sol-Engine, we post-trained @MiniMax_AI H3 Ref2VA into a real-time streaming neural renderer. 🎮 Code drives the world. A game engine handles physics, object states, and player interactions, while H3 generates the visuals. ⚡ Real-time streaming: 2 denoising steps, 24 FPS, and ~273 ms per chunk on 8×GB200 GPUs. 🎨 One world, endless appearances: Change the reference image and prompt to render the same interactive world in entirely different styles. 🔥 Powered by streaming post-training, distillation, a fast causal H3 VAE, and full-stack inference acceleration. Check out our demo and code! 👇 demo: nvlabs.github.io/Sana/Sol-En… code: github.com/NVlabs/Sana/tree/…

🚀 Introducing SoL-Pi from NVIDIA. ⚡️ RSI for Efficiency. Efficiency for Swarm Intelligence. Before scaling to thousands of parallel agents, can we first make each agent waste fewer tokens? SoL-Pi targets the harness layer for Agent ! 1/8