Deploying LLMs on Private Infra

Deploying Large Language Models (LLMs) presents significant opportunities for enterprises, but moving beyond public APIs to private infrastructure introduces substantial hurdles. With the release of increasingly powerful open models—from Meta’s Llama 4 series (including the 17B parameter Scout and Maverick models with up to 10 million token context windows) to OpenAI’s groundbreaking GPT-OSS that runs on high-end laptops—the case for private deployment has never been stronger. As organizations look to leverage these models on their own hardware for security, control, or compliance reasons, they face complex technical and operational challenges (Coralogix). These include managing significant computational resources, ensuring data security, optimizing performance, and handling diverse hardware targets (A10 Networks).

AI and LLM inference require significant computational resources like GPUs/TPUs, memory, and storage as well as huge amount of power. … On-premises: Enterprises must invest heavily in compute resources and upgrade their existing power and cooling infrastructure… This presents a huge upfront cost… (A10 Networks)

This is where the need for efficient, adaptable deployment solutions becomes critical. I recognize that I can help enterprises deploy their private LLMs on private infra by creating a technology solution powered by the Zig programming language.

My inspiration for this came from analyzing Zig’s impressive cross-compilation capabilities, clearly demonstrated in its release notes (Zig 0.14.0 Release Notes). Zig is designed with cross-compilation as a core feature.

I have carefully designed Zig since the very beginning to treat cross compilation as a first class use case. (Andrew Kelley, Zig Blog)

Zig supports building for an extensive array of target systems directly, without complex toolchain setups for each target. Here are some examples:

(Shortened table for brevity)

The ability to target such diverse architectures (from standard x86_64 and ARM64 on Linux, Windows, and macOS to RISC-V, POWER, MIPS, and even WebAssembly) with a single toolchain is powerful. Zig’s focus on simplicity, performance, and control makes it well-suited for building the deployment tooling needed for LLMs.

Zig’s appeal lies in its simplicity, modern design, and the balance it strikes between low-level control and runtime safety. (Ali Cheragi, quoted in LeadDev)

Zig’s low-level control over memory and lack of hidden control flow makes it much simpler to write fast software. (Andrew Kelley, GOTO Conferences)

This capability directly addresses the deployment challenge: How do you efficiently get a specific LLM running optimally on a specific piece of hardware within an enterprise’s private infrastructure?

Imagine an enterprise wants to deploy Meta’s latest Llama 4 Scout model—with its 17B parameters and 10M token context window—or OpenAI’s GPT-OSS (which remarkably performs at o4-mini level while running on a high-end laptop) specifically onto their fleet of Apple Silicon Macs.

Knowing that Zig easily targets aarch64-macos, we can build a deployment solution tailored for this exact scenario. This allows for highly specific optimization and marketing.

Example Landing Page Concepts:

/deploy/models/llama-4-scout/backends/aarch64-macos /deploy/models/gpt-oss/backends/aarch64-macos

Targeted Sales Copy:

‘We help enterprises deploy Llama 4 Scout or GPT-OSS efficiently on 64-bit ARM architecture, running Apple's macOS (Apple Silicon), leveraging optimized binaries built with Zig for maximum performance and control within your private infrastructure.’

By combining the power of LLMs with the portability and performance focus of Zig, we can create targeted solutions that solve real deployment pain points for enterprises operating on diverse private hardware.

References

1. Top Challenges in Building Enterprise LLM Applications by Coralogix
2. Building AI and LLM Inference in Your Environment? Be Aware of These Five Challenges by A10 Networks
3. zig cc: a Powerful Drop-In Replacement for GCC/Clang by Andrew Kelley
4. Why Zig is one of the hottest programming languages to learn by LeadDev
5. Intro to the Zig Programming Language by Andrew Kelley at GOTO 2022