ENTERPRISE · FORWARD-DEPLOYED ENGINEERING

Inference, deployed where you need it.

A forward-deployed engineer from Blackbox deploys the inference engine for your workload: a private endpoint, an air-gapped instance on your own hardware, or a GPU configuration sized to your traffic.

YOUR ENVIRONMENT PRIVATE ENDPOINT AIR-GAPPED INSTANCE GPU RACK BLACKBOX INFERENCE ENGINE WHAT GENERALIZES RELEASE CORE ENGINE LOOP BENCHMARK ACROSS WORKLOADS PROMOTE TO DEFAULTS DEPLOY MEASURE TUNE SHIP CONFIG FDE LOOP FIG. 07 · FDE LOOP · CORE ENGINE LOOP FAST LOOP SLOW LOOP
INSIDE THE LOOPS

Two loops, one engine

Your engineer runs a fast loop inside your environment. What generalizes moves into the core engine loop, so later deployments start from better defaults.

FDE LOOP

Fast. It runs on your deployment, against your traffic.

  1. Deploy

    The engineer stands up the engine in your environment: a private endpoint, an air-gapped instance, or GPUs that you own.

  2. Measure

    The engineer measures the engine on the shape of your real traffic: prompt and output lengths, concurrency, and the latency that your product needs.

  3. Tune

    The engineer changes one setting at a time and keeps a change only when it beats the current best on your workload.

  4. Ship config

    The winning configuration goes to production on your deployment. Then the loop starts again with the next measurement.

CORE ENGINE LOOP

Slow. It runs on the engine that every deployment shares.

  1. Benchmark across workloads

    A change that helped one workload is tested against many others before any other deployment uses it.

  2. Promote to defaults

    Changes that hold up across workloads become the engine defaults.

  3. Release

    A new engine release carries the new defaults, so the next deployment starts from a better baseline.

WHAT WE DEPLOY

Three ways to run the engine

Each engagement ends with an inference engine in production. The engineer deploys it in the shape that your workload and your security review require.

Private endpoint

An inference endpoint that serves only your account, on GPUs that no other customer shares. Your applications call it through an OpenAI-compatible API.

Air-gapped instance

The inference engine on hardware that you control, with no connection out of your network. Prompts, outputs, and weights stay inside it.

GPU configuration

The GPUs, the parallelism, and the engine settings for your model and your traffic, sized before the first request and tuned after it.

WHAT GETS TUNED

The settings behind the speed

Most inference speed comes from configuration. These are the settings that your engineer tunes against your workload.

GPU TYPE AND COUNT
Enough memory for the model and its context, without GPUs that sit idle.
PARALLELISM
How the model splits across GPUs: tensor, pipeline, or expert parallelism.
BATCHING AND CONCURRENCY
How many requests run together before the latency of each request starts to climb.
SPECULATIVE DECODING
How many tokens the engine drafts ahead, set to the number that the model accepts.
CONTEXT AND KV CACHE
The longest context that your workload needs, and the cache memory that it takes.
ENGINE FLAGS
Scheduler, kernel, and memory settings, kept only when quality checks pass.

We run the same search on our own endpoints. See how agents tune the inference engine.

WHO IT IS FOR

When a shared API is not enough

Forward-deployed engineering is for workloads that need a deployment of their own.

Data that cannot leave

Regulated data, customer records, or source code that must stay in your account or inside your network.

A latency target

A product with a response time that it must hold at production concurrency, not only in a benchmark.

Your own weights

An open-weight model that you fine-tuned, served on GPUs that are dedicated to it.

HOW AN ENGAGEMENT RUNS

From the first call to production traffic

  1. 01
    Scope the workload

    We start with the model, the traffic, where the model must run, and the controls that your security review requires.

  2. 02
    Choose the deployment

    A private endpoint, an air-gapped instance, or both, with a GPU configuration sized to the workload.

  3. 03
    Deploy and tune

    The engineer stands up the inference engine and tunes it against your workload until it meets the targets that we agreed.

  4. 04
    Go to production

    Your applications move to the new engine, and the engineer verifies it under production traffic.

FAQ

Common questions.

What does a forward-deployed engineer do?

The engineer deploys the inference engine for your workload: a private endpoint, an air-gapped instance, or both. The engineer also selects and tunes the GPU configuration that runs it.

Can the model run on our own hardware?

Yes. An air-gapped instance runs the inference engine on hardware that you control, with no connection out of your network.

Which models can you deploy?

Open-weight models, including weights that you fine-tuned. Tell us the model, and we scope the GPUs that it needs.

What does the engineer tune?

The GPU type and count, parallelism, batching and concurrency, speculative decoding, context length, and engine flags. Each change stays only if it beats the current best on your workload. See how the search runs.

How does an engagement start?

With a conversation, not a signup. Talk to sales about the workload and where it must run.

Do you want to deploy inference at the customer's side? We are hiring engineers.

VIEW OPEN ROLES

Tell us where the model must run.

Tell us the model, the traffic, and the controls that it must satisfy. We reply with a deployment plan.

TALK TO SALES