ANTILATTICE EDGE · ON-DEVICE INTELLIGENCE

The fastest AI ever built for the device in your pocket.

A 1B fp16 model. 256K context in 4GB of RAM. No cloud.

EDGE / DEVICE READOUT
MODEL1B · fp16
CONTEXT262,144 tokens
MEMORY3.1 / 4.0 GB
THROUGHPUT78 tok/s @ 256K
NETWORKoffline — 0 bytes out
STATUSresident
262,144 TOKENS RESIDENT · 3.1GB PEAK · fp16 WEIGHTSPOWERED BY LATENT GRAPH ATTENTION

Meet Antilattice Edge. Built to live in your pocket — and nowhere else.

Trace how frontier capability fits in 4GB of RAM — a full fp16 model whose cache grows with meaning, not with every pair.

01 / The memory math

256K context. 4GB of RAM. It fits.

A 1B fp16 model is 2GB of weights. Dense attention adds ~192KB of KV per token — 51.5GB at full context, thirteen times the entire budget. Latent Graph Attention routes the cache along learned edges at ~2KB per token, and the whole footprint lands at 3.1GB.

ANTILATTICE EDGE @ 256K — ONE BUDGET3.1 / 4.0GB0.05GB / CELL
WEIGHTS — 1B fp16 · 2.0GBROUTED KV @ 256K · 0.5GBRUNTIME · 0.6GBFREE · 0.9GB
DENSE fp16 KV @ 256K — SAME CONTEXT51.5GB13× OVER BUDGET
4GB

The cache alone outweighs the phone thirteen times over. Squeezed into one budget, dense holds roughly 8K tokens — Edge holds 256K, a 32× larger window.

02 / Measured on-device

78% of short-context speed — at 256K.

CTXDENSE fp16ANTILATTICE EDGE
8K100100
32Kmemory-infeasible96
128Kmemory-infeasible88
256Kmemory-infeasible78

Internal benchmarking on a 2026 flagship smartphone with a 4GB memory budget, normalized to short-context generation speed. Dense fp16 KV exhausts the budget before 16K tokens and is memory-infeasible from 32K; Antilattice Edge keeps serving to 256K.

78%

of short-context generation speed, held at 256K tokens

32×

more context than dense fp16 fits in the same 4GB budget

04 / The memory wall

Local AI was never slow — it was starved

4GB BUDGETOOM××
8K32K128K256KTOTAL FOOTPRINT →
DENSE fp16 — weights + KVANTILATTICE EDGE — weights + routed KV╌ 4GB BUDGET

Figures

Fig. 01 — the cache that never stops

Memory is the wall

Dense KV grows at ~192KB per token for a 1B model. At 256K that's 51GB of cache — before weights, before activations, before the OS gets a byte.

Fig. 02 — the usual escape hatch

Quantization hides the symptom

int4 weights shrink the model but not the dense cache. Context stays capped, precision gets thrown away, and the wall stays where it was.

Fig. 03 — what Edge keeps

Route the cache itself

Latent Graph Attention keeps KV only along learned edges — ~2KB per token. The cache collapses, fp16 survives, and 256K fits in 4GB.

05 / Get started

Same intelligence.
No data center.

We build frontier models that live entirely on the device. If your product needs intelligence that never leaves the hardware, we want to hear from you.

Join the beta> hello@antilattice.com