AI Porting & Deployment Pack

Get AI workloads running on novel silicon, edge boards, embedded targets, or the browser — with a reproducible runbook.

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A model that runs in a notebook is not the same artefact as one that runs on the device, the browser, the on-prem cluster, or the constrained edge box your customers actually use. The gap between those two artefacts is where AI projects stall — runtime mismatches, language ceilings, missing toolchains, lock-in to a particular cloud or accelerator. We treat that gap as an engineering problem with a defined target.

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Novel silicon and accelerator hardware
Feasibility benchmark running on a target board

The Engagement Shape

Two gated phases. Feasibility (2–4 weeks, fixed-scope) produces a feasibility memo, a porting plan, a go / no-go recommendation, and a representative micro-benchmark on the target. Porting of 1 Workload (4–10 weeks, target-dependent) lands a working workload on the named target, a reproducible benchmark, a deployment runbook, and a risk-and-limit register. Pricing is fixed on Feasibility and milestone or fixed-price on the porting phase — against the working workload, the benchmark, and the runbook, not engineer-weeks against a porting backlog.

Four Target Surfaces

The Targets We Port To

All four share the same engagement pattern — Feasibility, then Porting of 1 Workload — and the same outcome shape: a working workload, a benchmark, and a runbook. Target-specific constraints drive timeline and risk.

Novel silicon target

Novel Silicon

Accelerators

AI workload onto a new accelerator, SoC, or single-board computer — SDK, runtime, and driver gap closure, kernel work, benchmark instrumentation.

Edge and embedded target

Edge & Embedded

Constrained

Constrained-target bring-up where memory, power, OS, or thermal envelopes drive the risk — runtime selection and benchmark against the operational envelope.

Browser and edge-client target

Browser & Edge-Client

Client-side

Client-side AI via WASM, WebGL, and WebGPU — porting to a browser-compatible runtime and profiling against the target browser and device matrix.

Python-to-native target

Python-to-Native

Rewrites

Python to C++ or Rust where the language ceiling, runtime cost, or distribution model makes Python untenable, with numerical parity validated against the baseline.

What the Runbook Is

The runbook is the deliverable that turns "we ported it" into something your team can defend and replay. Your team replays the benchmark on the target using the runbook and the numbers reproduce within an agreed tolerance, or we are not done. It travels with the workload — the same steps re-run on the next board revision or the next driver bump — alongside a risk-and-limit register that states what does and does not work in the operational envelope.

AI workload running on an edge device with a deployment runbook

What This Pack Covers

Feasibility Studies
Toolchain Bring-Up
SDK / Runtime / Driver Gaps
Kernel Work
Python-to-Native Rewrites
WASM / WebGPU Porting
Target Benchmarking
Deployment Runbooks
AI workload running on a headset edge target

Not Sure This Is the Right Pack?

If the workload already runs on the target and the question is making it cheaper or faster, that is the Inference Cost-Cut Pack. If it runs but gives wrong answers or has no release gate, that is the Production AI Monitoring Harness — porting proves runnable, the harness proves correct under load. If a committee needs LLM-comparison evidence, that is the LLM Selection Pack; if the question is readiness against a published rubric, the AI Readiness Scorecard.

How We Know This Works

Cross-API porting, Metal bring-up, and edge-target inference work. These engagements pre-date the packaged pack and stand as bridged proof.

Case-Study: V-Nova - GPU Porting from OpenCL to Metal

Case-Study: V-Nova - GPU Porting from OpenCL to Metal

Dec 15, 2023

Case study on moving a GPU application from OpenCL to Metal for our client V-Nova.

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Case-Study: V-Nova - Metal-Based Pixel Processing for Video Decoder

Case-Study: V-Nova - Metal-Based Pixel Processing for Video Decoder

Dec 15, 2022

TechnoLynx improved V-Nova’s video decoder with GPU-based pixel processing, Metal shaders, and efficient image handling for high-quality colour images…

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View case studies See all
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Featured Articles

What portability across targets really takes — cross-platform GPU performance, WASM inference, and the inference-engine layer.

What Cross-Platform GPU Performance Portability Actually Requires

What Cross-Platform GPU Performance Portability Actually Requires

Jun 12, 2026

Portable GPU APIs translate code, not performance. What it actually takes to run fast on NVIDIA, AMD, and Intel from the same codebase.

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WebAssembly Python for Inference: How Pyodide and WASM Actually Work

WebAssembly Python for Inference: How Pyodide and WASM Actually Work

Jun 12, 2026

WASM Python runs CPython compiled to WebAssembly. Understand the interpreter overhead, sandbox limits, and where it fits for inference before porting.

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What an Inference Engine Is — and How It Shapes the Port Decision

What an Inference Engine Is — and How It Shapes the Port Decision

Jun 12, 2026

An inference engine is the layer that turns a trained model plus inputs into predictions.

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2019
Founded in
95%+
Client Satisfaction Rate
20+
Successful Projects Delivered

Client Testimonials

TechnoLynx delivered the project on time and provided quality outputs that met the client's expectations. The team was proactive in providing ideas and suggestions, and they were careful at properly planning the tasks. The client also praised the team's expertise in GPU programming and AI.

Guido Meardi - CEO

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Check V-Nova

TechnoLynx's skill in low-level software development was impressive. TechnoLynx was able to create four prototypes with common components and an interface for easy maintenance. The client was extremely happy with the solution's speed. Moreover, their communication was seamless and straightforward.

Alex Farrant - Director

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Check CloudRF

TechnoLynx's unique aspect is that they're able to transform complex theories into practicable and applicable results. TechnoLynx provides research reports and architecture planning documents. The team is able to transform complex theories into practicable and applicable results. TechnoLynx's project management is strong and delivers work on time without hardware issues, being responsive through virtual meetings.

Forrest Smith - CEO & Co-Founder

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Check Kineon

I’m delighted with our collaboration with their team. Thanks to TechnoLynx's work, the client has been able to co-author two patents. They lead responsive project management to solve problems quickly. The team also praises their skilled and knowledgeable team.

Gil Hagi - CEO

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Check Tasty

We had high-efficiency meetings. TechnoLynx’s work resulted in a successful breakthrough, and their input improved the client’s app. Their flexible and organised project management cultivated a healthy collaboration experience. Ultimately, their professionalism and commitment were impressive.

Anonymous - CEO

AI Porting & Deployment Pack FAQ

When is porting the right engagement instead of cost-cutting?

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When the AI path does not exist on the target yet, or runs only as a research prototype. If the workload already runs on the target and the question is making it cheaper or faster, that is the Inference Cost-Cut Pack. Porting exists precisely for the case where the path does not exist yet.

What targets do you port AI workloads to?

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Four canonical surfaces: novel silicon and new accelerators or single-board computers; constrained edge and embedded targets; the browser and edge clients (WASM, WebGL, WebGPU); and Python-to-native (C++ / Rust) rewrites where the language ceiling or distribution model makes Python untenable.

What does the Feasibility phase decide?

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Whether porting should proceed. In 2–4 weeks it produces a feasibility memo, a porting plan, a go / no-go recommendation, and a representative micro-benchmark on the target — so the larger porting commitment is made against evidence, not a hope.

Do I get something my team can re-run after handover?

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Yes. The deliverable is a working workload on the named target, a reproducible benchmark, a deployment runbook, and a risk-and-limit register. Your team replays the benchmark using the runbook and the numbers reproduce within an agreed tolerance, or we are not done.

Does porting prove the model is correct under load?

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No. Porting proves the workload is runnable on the target. Proving it is correct under load — regression coverage, release gates, drift checks — is the Production AI Monitoring Harness, and the two often follow one another.

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The AI-infrastructure / SaaS crosswalk routes porting-and-deployment work through this pack. Porting establishes runnability on the target; it does not certify or sign anything off against compliance standards.

If you have a named target (a silicon part, a board, a browser surface, a runtime), a representative workload, and someone who owns the question "can we ship this AI feature on this hardware?", contact us and tell us the target, the workload shape, and the deployment constraint you need to clear.

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AI deployment infrastructure on a named target