How do you set up local AI on Linux?

Key facts

GPU stacks	NVIDIA CUDA and AMD ROCm are the two common acceleration paths
Runtimes	llama.cpp, Ollama, LM Studio and vLLM all run on Linux
Containers	Docker and Podman simplify driver and runtime dependencies
Service management	systemd units keep inference servers running and restart on failure
Monitoring	GPU utilization, VRAM, queue depth and request latency are the key signals
Security	Bind to localhost or a private interface; add authentication and TLS before exposure
Cloud option	Plugsky serves 30+ models via an OpenAI-compatible API, on-prem and air-gapped supported

TL;DR

• Linux is the default server OS for local inference; driver setup is the main hurdle.
• Use CUDA for NVIDIA and ROCm for AMD, and verify support before buying hardware.
• Containers reduce dependency pain, especially for vLLM and CUDA versions.
• Run servers under systemd with explicit users, limits and restart policies.
• Monitor VRAM and queue depth, and secure the endpoint before exposing it.

How it works, step by step

1. Install the vendor driver and verify the GPU is visible to the system.
2. Install the compute stack: CUDA or ROCm, matching your runtime's requirements.
3. Choose a runtime and install it, preferably inside a container for dependency isolation.
4. Download a quantized model suitable for your VRAM and confirm it loads.
5. Expose the OpenAI-compatible endpoint on localhost and test it.
6. Create a systemd service with a dedicated user, memory limits and restart policy.
7. Add monitoring, authentication and firewall rules, then load-test before production.

Try it yourself

Open the Docker Compose generator for local AI →

Drivers and compute stacks

Everything depends on the GPU stack. NVIDIA users install a recent driver plus a CUDA toolkit version compatible with their runtime; version mismatches are the most common source of build failures. AMD users install ROCm, but supported hardware and framework coverage are narrower, so check that your exact card and runtime are listed before committing.

CPU-only Linux machines need no driver work and run llama.cpp well. They are a reasonable place to start or a permanent choice for batch workloads. Apple Silicon does not apply here, and Windows users often benefit from WSL for the Linux tooling ecosystem, though native builds increasingly work well too.

Runtimes, containers and service management

llama.cpp compiles from source with backend flags or installs from a package, and runs on CPU, CUDA, ROCm and Vulkan. Ollama ships an install script and a daemon. vLLM installs as a Python package and expects a working CUDA or ROCm environment, which is where containers earn their keep: a pinned image removes most dependency drift.

• Container benefits: reproducible CUDA versions, isolated Python environments, simple rollback.
• GPU passthrough: requires the container toolkit for your vendor.
• systemd: run the server as a dedicated user, set restart and resource limits.
• Logs: capture request metadata and errors, but avoid storing sensitive prompts by default.

Monitoring, security and hybrid capacity

Watch four signals: GPU utilization and VRAM, KV cache memory, queue depth and request latency percentiles. VRAM tells you whether cache is the constraint; queue depth tells you whether throughput is the constraint. Set alerts before users notice, and load-test at realistic concurrency rather than single requests.

Security on Linux follows familiar patterns: run services as non-root users, bind to localhost or a private interface, restrict firewall rules, terminate TLS at a reverse proxy and require API keys. Sandbox any agent tools with containers and seccomp-style restrictions.

When your hardware ceiling arrives, keep the same interface and route overflow upstream. Plugsky serves 30+ models behind an OpenAI-compatible endpoint with chat, streaming, JSON mode, function calling, embeddings, RAG and agents live, and supports on-prem and air-gapped deployment for teams that need full control. Audio, image, moderation, batch and fine-tuning endpoints are coming soon. Check the live pricing page for plan details.

Honest comparison

Stack	Hardware	Install effort	Best for	Notes
llama.cpp	CPU, NVIDIA, AMD, Vulkan	Moderate	Portable control	Build with backend flags
Ollama	CPU, NVIDIA, AMD	Low	Quick local serving	Daemon and model registry
vLLM	NVIDIA, AMD ROCm	High	Concurrent GPU serving	Pinned containers help
LM Studio	CPU, NVIDIA	Low	Desktop-style use	GUI on Linux
Plugsky	Hosted or on-prem	None for cloud	Elastic and managed capacity	OpenAI-compatible, 30+ models

Frequently asked questions

Which Linux distribution is best for local AI?

Most mainstream distributions work. Choose one with good driver packaging and long support, and prefer the vendor's documented setup path over improvisation.

Do I need Docker?

Not strictly, but containers simplify CUDA and Python dependency management, especially for vLLM. Native installs work when you pin versions carefully.

How do I run an inference server as a service?

Create a systemd unit with a dedicated non-root user, a restart policy and resource limits. Keep model paths in a stable location and log to the journal or a file.

Can I use an AMD GPU?

Yes, through ROCm, but verify that your card and runtime are supported. Coverage is narrower than CUDA, so check compatibility before purchasing.

How do I monitor a local AI server?

Track GPU utilization, VRAM usage, KV cache memory, queue depth and latency percentiles. Tools like vendor dashboards plus a metrics endpoint cover this.

Is Linux more secure than other platforms for AI?

Not automatically. Security comes from configuration: non-root services, restricted network exposure, authentication, TLS and sandboxed tools.

Can I deploy Plugsky on Linux on-prem?

Yes. Plugsky supports cloud, VPC, on-prem and air-gapped deployments for enterprise customers, with an OpenAI-compatible API across 30+ models.

What about Windows and macOS?

They run the same runtimes with different driver stories. Linux remains the most common choice for servers because of tooling and automation.