eigenvector↗Let’s talk
+ 01 / THE PLATFORM

Your ambition.
Our acceleration.↗

From the first request to the final token, every layer should work for your business. We bring the model, the compute and the inference path together.

THREE WAYS TO MAKE IT YOURS

The right place
for your intelligence.

Private infrastructure. Dedicated capacity. Or a carefully defined hybrid. The architecture starts with your requirements.

BUILT AROUND YOUR BOUNDARIES

In your environment

Keep model weights, prompts and outputs inside a boundary you control. We configure the inference stack around your hardware and security requirements.

  • Customer-controlled infrastructure
  • Explicit network and data boundaries
  • Workload-specific performance tuning
For teams with their own cloud, GPUs or on-premise infrastructure.
Discuss this deployment
YOUR DEPLOYMENT
⌘Your applicationPRODUCT / WORKFLOW / API
Authenticated requests
YOUR PRIVATE ENVIRONMENT
Eigenvector gateway
GPU 01
GPU 02
GPU 03
YOUR MODELS. YOUR CAPACITY.
Architecture illustration · actual topology is workload-specific
THE WHOLE PATH, OPTIMISED

There’s more to speed
than a faster GPU.

Good performance is the sum of many deliberate decisions.

01

Session-aware routing

Keep conversations close to their context. Route to a suitable worker with available capacity, while preserving session affinity where possible.

02

Cache-aware performance

Reuse prefix and session context where supported. Tune cache policy to your request mix, model and memory budget.

03

Workload-tuned engines

Select model precision, parallelism and serving configuration around the workload. Optimise the full path from request to output.

04

Controlled concurrency

Set capacity limits, queue policy and timeouts per model. Keep busy workloads from overwhelming your deployment.

05

Meaningful measurement

Measure time to first token, throughput and tail latency. Test the scenarios your users actually experience.

06

Operational visibility

Understand model capacity, queue depth and worker health. See where time goes before adding more hardware.

FAMILIAR INTERFACE. YOUR INFRASTRUCTURE.

A new direction.
A familiar first line.

Connect compatible language workloads through an OpenAI-compatible API. Keep your application logic close to what you already know.

  • Streaming and non-streaming responses
  • Model-specific configuration
  • Separate, scoped media integrations

Compatibility is validated for your model and application. Image and video processing use asynchronous job workflows.

YOUR NEXT CHAPTER.PY
from openai import OpenAI

client = OpenAI(
    base_url=YOUR_DEPLOYMENT_URL,
    api_key=YOUR_API_KEY,
)

response = client.chat.completions.create(
    model=YOUR_DEPLOYED_MODEL,
    messages=[{
        "role": "user",
        "content": "Let's build something extraordinary."
    }],
    stream=True,
)

Integration example. Replace the variables with your agreed deployment details.

ENGINEERING, MADE TANGIBLE

Less guesswork.
More evidence.

Selected internal engineering results.
Real configurations. Clearly scoped measurements.

PRODUCTION ACCEPTANCE RUN

Ideogram 4.0

13.68seconds

Generation stage

08 SEP 2026
OUTPUT
1024 × 1024
SAMPLING
12 steps
END-TO-END
19.54 seconds

Official NF4 Turbo12 configuration. One production acceptance sample, including a verified image retrieval. It is an observed run, not a latency percentile or service guarantee.

Download measurement notes
FROM POSSIBLE TO PRODUCTION

A deliberate path forward.

01

Understand

Your workload, usage pattern, data boundaries and goals.

02

Benchmark

A representative test with agreed quality, latency and cost measures.

03

Deploy

An agreed architecture, operating scope and integration plan.

04

Refine

Tune the stack as your workload and business evolve.

THE NEXT MOVE IS YOURS

Find your
unfair advantage.

Let’s build your direction

One conversation.
A whole new trajectory.