Dimba generates entire sequences in parallel through iterative refinement. No token-by-token bottleneck. No quality tradeoff. Just fast, coherent AI.
Autoregressive models are hitting a wall. Sequential generation creates fundamental limits that no amount of optimization can overcome.
Token-by-token generation means real-time apps suffer. Voice assistants, coding copilots, and robotics need speed.
Existing "fast" methods sacrifice coherence for speed. Parallel generation hasn't matched transformer quality.
Quadratic attention complexity makes long-context tasks expensive. Million-token windows are out of reach.
Running large autoregressive models at scale requires massive compute resources and optimization teams.
Dimba fuses diffusion and state-space models for parallel generation with transformer-level quality.
Generate entire sequences at once. No sequential bottleneck means lower latency and better real-time performance.
Linear attention complexity (O(L) vs O(LĀ²)) enables million-token contexts without the memory blowup.
Iterative refinement in latent/embedding space. The model denoises the full sequence together, not token by token.
Denoising and sequence modeling intertwined at every step. Not a wrapper ā a unified architecture.
Four steps from noise to coherent text. Physics-inspired stability for long sequences.
Random embeddings
Mamba-2 SSM layers
Cosine diffusion steps
Discrete tokens
Novel techniques we've developed to push the boundaries of what's possible.
Cryptographic entropy sources for reproducible generation. Verifiable randomness for critical applications.
Hidden chain-of-thought in continuous embedding space. "Thinking time" without token overhead.
Optimized for on-device inference. D1-Small targets mobile and embedded deployments.
We're training D1-Base now. Follow along as we build the future of language models.