Attention That Scales.
Memory That Doesn't.
Up to
5×
Faster inference at 500K+ context
Up to
10×
KV memory reduction measured at 100K
Up to
10×
More concurrent users per GPU on H100
At Antilattice, we are solving the core throughput bottleneck in transformer inference — the attention mechanism itself. The gains others claim require a quality trade-off. Ours do not.
The Long-Context Wall
Context is the new compute
Frontier LLMs are being deployed at context lengths that were theoretical just eighteen months ago. Agentic workflows, RAG pipelines, document reasoning, code understanding — all of them push models toward longer and longer sequences. The attention mechanism scales quadratically with sequence length. At the context lengths that matter now, it dominates both compute time and memory footprint.
The KV cache is the binding constraint
Every token processed at inference time must be stored in the KV cache. At long context, this cache alone saturates GPU memory — forcing operators to shrink batch sizes, cap context windows, or overprovision hardware far beyond what the compute workload requires. The economics collapse long before the models do.
The concurrency gap
The ceiling on concurrent users is almost never compute. It is memory. Serving thousands of simultaneous long-context sessions requires holding enormous KV caches for each. With today's attention implementations, that means GPU fleets that are financially unsustainable. Every operator running frontier models faces this wall. Very few are close to solving it.
Antilattice
In lattice theory, an antilattice is a structure where no two distinct elements share a common bound — ordered, but never forced to converge. We applied this principle to how attention moves through memory.
Drop-in at the inference layer.
Tested & compatible with
* Model-agnostic — tested across model families and GQA ratios.
The model doesn't know we're here.
The outputs prove it.
The Inference Horizon
What a 10× memory reduction actually means
It does not just make existing deployments cheaper. It changes what is deployable at all. Models that required multi-node setups fit on a single node. Context lengths that were economically prohibitive become viable at production scale. Operators can serve 10× more concurrent users within the same power and hardware envelope. The constraint shifts from infrastructure to demand.
Scaling laws still hold
The gains compound with context length. The longer the sequence, the larger the advantage — in latency, in memory, and in concurrent user capacity. We have validated that model quality is preserved across the sequence lengths where these gains matter most. The scaling behaviour that makes this system defensible is not an artifact of short-context testing.
The agentic wave needs this now
Multi-step agents, long-horizon reasoning, real-time document analysis — the workloads that define the next wave of AI deployment generate sequences far longer than current chat interfaces. Without efficient long-context inference, these workloads either cannot run in production or cannot run at a cost that sustains a business. Antilattice is being built for exactly this inflection.
Built where systems engineering meets ML research
Our team works at the layer where the theory of attention and the physical reality of GPU memory hierarchies must be reconciled. We obsess over one number: how many tokens of genuine intelligence a single node can produce per second, at a cost an operator can sustain.
We are a small team building deep. If that is your kind of work, we would like to hear from you.
View open roles ›Focus
Tokens of intelligence per second, per dollar
Location
Hyderabad, India
Stage
Building deep, hiring selectively
If you are thinking about inference costs at scale, let's talk.
Whether you are a model provider, cloud operator, AI infrastructure company, or enterprise deploying long-context workloads — we are interested in your constraint.