Interactive generation
Optimize time-to-first-token and streaming speed for responsive assistants.
Cortex performance layer · online
Multiply inference.
Keep your hardware.
EucaX Cortex installs on top of existing AI infrastructure to increase inference output without a hardware replacement. From a single HGX H100, HGX H200, or B200 node to full data-center fleets, small AI labs and infrastructure operators typically unlock 3× or more serving capacity — up to 6× on HGX-class systems — depending on model, workload, and configuration.
X
optimized core
3–6×
uplift on HGX-class nodes
~4×
end-to-end speedup
~10×
generation-only speedup
3.7×
throughput at C=1
Uplift ranges and demonstrated results vary by model, workload, configuration, concurrency, and hardware.
01 / Who runs Cortex
From one node to a full fleet.
Cortex is built for anyone who owns AI hardware — a small lab with a single node, a data center serving many tenants, or an enterprise running private infrastructure.
Small AI labs
Single HGX H100 or HGX H200 nodes (8× GPU) and B200 systems. Cortex typically lifts output 3–6× — capacity that previously required more nodes.
Data centers & AI clouds
Raise service output per rack and serve more concurrent customers without adding another hardware cycle.
Private infrastructure
Organizations running their own models get more inference work from the hardware they already own — a minimum of 3× more capacity.
Reference point
One B200 node with Cortex can deliver the output of up to six H200 HGX boxes.
Equivalent-output comparison from a demonstrated configuration; results depend on model, workload, and sizing.
02 / The Cortex layer
Performance software above your infrastructure.
Cortex works across the complete inference path while preserving the hardware foundation already in place. It coordinates model execution, decoding, scheduling, memory, and workload behavior as one optimization surface.
Workload
Prompt shape · concurrency
Decoding
Token path · reasoning traces
Inference engine
Scheduling · batching · runtime
Model architecture
Execution-aware optimization
Hardware
Kernels · memory · utilization
03 / Performance frontier
One benchmark number is not enough.
The relevant operating point changes with the application. EucaX targets a broader frontier across latency, throughput, concurrency, memory pressure, and infrastructure cost.
Concurrent throughput
Useful work per unit hardware
EucaXvLLM baseline
3.7×@ C=1
124816
Concurrency →Demonstrated relative throughput
Low concurrency
Execution-path, decoding, runtime, and kernel improvements have the largest relative effect.
Rising load
GPU utilization improves while memory bandwidth and KV-cache pressure become more important.
Near saturation
Scheduling and batching dominate, compressing the multiplier while absolute throughput remains higher.
04 / How it works
Your infrastructure. Your model. Your benchmark.
There is no generic multiplier. EucaX profiles your actual setup and measures the uplift on it — so the number you get is the number your hardware delivers.
Step 01
Profile the infrastructure
We map what you run: GPU type and count (HGX H100, H200, B200…), nodes, memory, interconnect, and the serving stack in place.
Step 02
Identify the model & workload
Which models you serve, and how — interactive chat, long-form decoding, reasoning traces, or agent loops — defines the operating points that matter.
Step 03
Benchmark the uplift
Cortex is benchmarked against your baseline on your configuration, producing a measured projection of the capacity increase you can expect.
Demonstrated results range from a minimum of 3× up to 6× on HGX-class systems — the assessment determines where your setup lands.
05 / Infrastructure operators
Turn installed compute into more service output.
For small AI labs, data centers, AI clouds, and organizations running private AI hardware, Cortex increases the useful inference work delivered by the infrastructure already owned.
Long-form decoding
Sustain throughput across hundreds or thousands of output tokens for code, research, and documents.
Reasoning
Accelerate sequential traces where each generated token depends on every token before it.
Agent loops
Reduce the cumulative cost of reason → act → observe cycles across an entire task.
06 / Developer walkthrough
See the two stacks run.
The same model and prompts are executed through a vLLM baseline and the EucaX optimized stack, side by side.
Expand without replacing
More capacity from the infrastructure you own.
Install Cortex as the performance layer above your AI stack to typically multiply inference capacity by 3× or more—without beginning with another hardware expansion cycle.
WATCH THE TECHNICAL DEMO