Low-latency serving
An optimized inference path that keeps time-to-first-token and end-to-end latency low enough for interactive use.
Scalable AI Inference | ACM Global Tech
AI inference
Serve models at scale, at low latency.
High-throughput, low-latency model serving with autoscaling, continuous batching, and GPU efficiency, deployable on-prem or in the cloud. Run frontier and open-weight models inside your own boundary, fast enough for the request path and cheap enough for production.
The problem
Inference is where AI gets expensive.
A model that works in a notebook rarely survives production. Serving it to real traffic means latency that users feel, GPUs that idle and burn budget, and load that swings far faster than fixed capacity can follow. For a regulated institution, sending that traffic to a third party is often not an option at all.
Latency
Slow on the request path
Naive serving adds seconds per call. In member-facing and decision flows, that latency is the difference between usable and not.
GPU cost
Accelerators sit idle
Without batching and packing, expensive GPUs run far below capacity. You pay for silicon you never use.
Scale
Traffic is spiky
Demand swings by the hour. Fixed capacity either wastes money at the trough or fails users at the peak.
Residency
Data cannot leave
Regulated workloads often cannot send inputs to an external API. Serving has to run inside your own boundary.
What we deliver
Throughput and latency, on your hardware.
A production serving stack tuned for high throughput and low latency, that autoscales with demand and runs wherever your data has to live.
Continuous batching
Requests are dynamically batched and packed to keep GPUs saturated, lifting throughput without sacrificing latency. This is where efficiency is won.
Autoscaling
Capacity follows demand, scaling out under load and back down when idle, so you pay for what traffic actually needs.
GPU efficiency
Quantization, paged attention, and memory-aware scheduling extract more tokens per accelerator and lower cost per request.
On-prem or cloud
Run the same stack in your data center, your private cloud, or ours, so workloads that cannot leave your boundary do not have to.
Open-weight ready
Serve frontier and open-weight models behind one consistent interface, with no dependence on a single external provider.
The proof
Proprietary IP. Open source. Battle-tested.
Real infrastructure you can read and run, not slideware.
40+ proprietary innovations
Across AI infrastructure, inference, serving, and accelerator efficiency.
Open-source core
The serving stack is published in the open, so the inference path can be audited and run rather than trusted on faith.
Open-weight models
Our own open language models, built to run efficiently in your own environment with no external dependency.
Run inference at production scale.
License the IP, resell it under your brand, or co-build with our team. Deployable into any regulated market.
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