Agentic systems
Long-horizon agents
Agents that accumulate tool calls, memory, and multi-turn state across hundreds of thousands of tokens — without truncating history or losing earlier context to make room for new turns.
Powered by Latent Graph Attention
New attention architecture.
First-ever 5M-token context. 10× lower cost.
Latent Graph Attention is the world's first sub-quadratic attention architecture — delivering up to 10× faster inference at 512K context and the first-ever 5M-token context window, without loss in reasoning quality.
- 10×
- Faster inference at 512K context, same hardware
- 5M
- Token context window, served at production cost
- O(n²) → O(n)
- Attention cost scaling, dense vs. Antilattice
01 / Latent Graph Attention
Latent Graph Attention— An architectural breakthrough.
Breakthrough
Antilattice is the first architecture built on Latent Graph Attention — a fully learned, graph attention mechanism that routes compute only to the token relationships that matter, instead of processing every possible pair. At 512K tokens, this makes inference 10× faster (512K) and 100× faster (5M), making a 5M-token context window possible for the first time in the world.
And we're not stopping there. The same architecture is built to scale to 10–15M tokens without performance loss, putting us at the forefront of the technology needed to make true ultra-long context real, for the first time in the world.
- 10×
- faster at 512K tokens
- 100×
- faster at 5M tokens
- 5M
- context window, served today
Every pair (left) collapses to the routed edges that matter (right)
02 / Measured on our stack
10× throughput at 512K. Still serving at 5M.
Relative throughput (8K = 100) Dense Antilattice
8K
Dense 100
Antilattice 100
32K
Dense 62
Antilattice 96
128K
Dense 21
Antilattice 90
4× dense
512K
Dense 8
Antilattice 80
10× dense
1M
Dense — memory-infeasible
Antilattice 68
2M
Dense — memory-infeasible
Antilattice 54
5M
Dense — memory-infeasible
Antilattice 41
Internal benchmarking on a vLLM-based serving stack, normalized to short-context throughput (8K = 100). The dense-attention baseline becomes memory-infeasible to serve past roughly 512K–1M tokens on the same hardware; Antilattice keeps scaling.
03 / Built for
The workloads that outgrow dense attention
Enterprise
Document intelligence at full scale
Reason across an entire contract set, filing archive, or knowledge base in a single context window, instead of stitching together an answer from chunked retrieval.
Developer tooling
Whole-repository code reasoning
Give coding agents and review tools the entire repository as context — dependencies, tests, and history included — instead of a handful of retrieved files.
Infrastructure
Cost-sensitive scale
Serve long-context requests at a per-token cost that doesn't compound with usage, so long context stops being the tier you ration and starts being the default.
04 / The bottleneck
Long context was never free — it was deferred
Compute is the visible cost
Dense attention costs FLOPs quadratically: double the context, quarter the throughput. That cost is easy to see on a spec sheet, so most optimization work goes toward computing that full grid faster — a better kernel, a bigger batch, more GPUs.
Memory is the quieter one
The KV cache grows linearly with context and has to live in HBM for the entire generation. Long before compute becomes the limit, cache pressure forces smaller batches, shorter effective context, or both — and throughput collapses well ahead of any theoretical FLOPs ceiling.
Support is not the same as serving
Most "long-context" support today is a checkbox in a spec sheet: the model accepts the tokens, but serving it at real latency and real cost is a different problem entirely. Solving it means rethinking which token pairs are worth attending to in the first place — not just computing all of them faster.
How to read it
- FLOPs ~O(n²) — Compute cost quadruples every time context doubles. At 512K you're paying 4,000× what you paid at 8K.
- KV cache O(n) — Memory grows linearly but must stay resident in HBM. At long context, it exhausts the GPU before compute does.
- Antilattice — Latent Graph Attention keeps cost near-linear in effective context. The serving limit never arrives.
05 / Get started
Same intelligence.10× lower cost.
We build ultra-long context models from the ground up. If you're working on problems where context length is the bottleneck, we want to hear from you.
or write to hello@antilattice.com