+ 01 / THE PLATFORM

Your ambition.
Our acceleration.↗

An inference API tuned to your workload. Eigenvector operates the models, the KV backend and DSpark; your application connects through authenticated requests.

A CLEAR SERVICE BOUNDARY

Inference, through an API.

Eigenvector provides managed AI inference through authenticated APIs. Customers do not receive SSH, root, operating-system or bare-metal access. The service does not provide model-training or fine-tuning functionality. Server administration remains with Eigenvector.

ONE MANAGED INFERENCE SERVICE

The right capacity
for your intelligence.

API access, dedicated inference capacity and application integration. Every option runs the tuned stack on Eigenvector-operated infrastructure.

MANAGED BY EIGENVECTOR. CONNECTED BY API.

An API for your applications

Send authenticated requests and receive model outputs. Eigenvector runs the models and the tuned inference stack, including server administration and maintenance.

Eigenvector-operated infrastructure
Authenticated API access
Agreed data processing and retention

For teams building AI features through a managed API.

Discuss this service

YOUR APPLICATION → EIGENVECTOR API

⌘Your applicationPRODUCT / WORKFLOW / API

Authenticated API requests

EIGENVECTOR-OPERATED INFRASTRUCTURE

Eigenvector gateway

WORKER 01

WORKER 02

WORKER 03

MANAGED MODEL INFERENCE

Eigenvector administers the servers · customers connect through the API

THE WHOLE PATH, OPTIMISED

There’s more to speed
than a faster GPU.

Good performance is the sum of many deliberate decisions. Inside the technology

DSpark speculative decoding

Our lossless DSpark drafts blocks of tokens that the target model verifies at once, optimised for your workload and scheduled for your load.

Custom KV backend

Tiered KV caching across GPU memory, host DRAM and local NVMe, with workload-aware eviction and support for hybrid-attention models.

Cache-aware routing

Route each request to the worker that already holds its context, and keep sessions together wherever the cache can serve them.

Workload-tuned engines

Model precision, parallelism and serving configuration are selected around the workload, optimising the path from request to output.

Controlled concurrency

Capacity limits, queue policy and timeouts are set per model, so busy workloads keep their latency target.

Meaningful measurement

Throughput per GPU, time to first token and tail latency, measured on the scenarios your users actually create.

FAMILIAR INTERFACE. TUNED INFRASTRUCTURE.

A new direction.
A familiar first line.

Connect compatible language workloads through an OpenAI-compatible API using service credentials. The acceleration happens behind the endpoint; your application logic stays 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=EV_API_BASE_URL,
    api_key=EV_API_KEY,
)

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

Integration example. Use the API endpoint, key and model ID issued by Eigenvector for your agreed service.

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.

Profile

Your traffic’s shape: prompt reuse, context lengths, output formats and concurrency.

Optimise

The KV backend and DSpark, optimised for your workload.

Prove

Standard serving against the tuned stack on replayed traffic, same GPUs, same latency target.

Serve

The tuned stack in production, re-measured as your traffic grows.

THE NEXT MOVE IS YOURS

Bring your workload.
We’ll bring the numbers.

Benchmark your workload

One benchmark.
A whole new cost curve.

Your ambition.Our acceleration.↗