POWERED BY LATENT GRAPH ATTENTION
The end of
quadratic attention.
Dense-matched quality. 10× the speed. First-ever 5M-token context.
The world's first sub-quadratic attention architecture — dense-matched quality token-for-token, at up to 10× the speed, out to the first-ever 5M-token window.
1:1
Quality vs. dense quadratic attention — token-for-token
10×
Faster inference at 512K context, same hardware
5M
Token context window, served at production cost
Meet Antilattice. Built to connect the pairs that matter and drop the rest.
Trace how isolated tokens resolve into coherent structure — attention whose cost grows with length, not with its square.
01 / Latent Graph Attention
An architectural breakthrough.
262,144
pairs computed / block
1×
relative speed
ROUTED ATTENTION MASK
LEARNED TOKEN GRAPH
Every pair, every time.
Standard attention compares each token with every other token. Double the context, quadruple the work.
The model learns what matters.
Latent Graph Attention learns a token graph — which relationships actually carry meaning.
Compute flows only along edges.
Attention is routed through those edges alone. 10× faster at 512K. 100× faster at 5M.
02 / Measured on our stack
10× throughput at 512K. Still serving at 5M.
Internal benchmarking on a vLLM-based serving stack, normalized to short-context throughput. The dense baseline becomes memory-infeasible past roughly 512K–1M tokens on the same hardware; Antilattice keeps scaling.
03
Built for
The workloads that outgrow dense attention
Every pair collapses to the routed edges that matter Agentic systems
Agents that accumulate tool calls, memory, and multi-turn state across hundreds of thousands of tokens — without truncating history.
U/01
Long-horizon agents
Enterprise
Reason across an entire contract set, filing archive, or knowledge base in a single context window instead of chunked retrieval.
U/02
Document intelligence at full scale
Developer tooling
Give coding agents the entire repository as context — dependencies, tests, and history included.
U/03
Whole-repository code reasoning
Infrastructure
Serve long context at a per-token cost that doesn't compound with usage, so long context becomes the default.
U/04
Cost-sensitive scale
04 / Antilattice on-device
Antilattice on-device.
1B fp16 · 256K CONTEXT · 4GB RAM
The same Latent Graph Attention, sized for the device in your pocket. A 1B fp16 model holds 256K tokens of context inside a 4GB memory budget — dense fp16 KV alone would need 51.5GB.
Dense holds roughly 8K tokens in one budget. Edge holds 256K — a 32× window, on the device.
05 / The bottleneck
Long context was never free — it was deferred
Fig. 01 — every pair computed
Compute is the visible cost
Dense attention costs FLOPs quadratically: double the context, quarter the throughput. Most optimization work just computes that full grid faster.
Fig. 02 — KV cache vs. HBM ceiling
Memory is the quieter one
The KV cache grows linearly and must live in HBM for the whole generation. Cache pressure collapses throughput well before any FLOPs ceiling.
Fig. 03 — claimed vs. served
Support is not the same as serving
Most long-context support is a checkbox. Solving it means rethinking which token pairs are worth attending to in the first place.
How to read it
FLOPs ~O(n²) — Compute quadruples every time context doubles. At 512K you pay 4,000× what you paid at 8K.
KV cache O(n) — Memory grows linearly but must stay resident in HBM, exhausting the GPU before compute does.
Antilattice — Latent Graph Attention keeps cost near-linear. The serving limit never arrives.
06 / Get started
Same intelligence.
10× lower cost.
We build ultra-long context models from the ground up. If context length is your bottleneck, we want to hear from you.