What is the difference between Portable Computer, Ollama and LM Studio?

Ollama and LM Studio are model runners — they load open-weight models and serve tokens, and you supply the agent logic yourself. Perplexity Portable Computer, launched August 25, 2026, ships the agent layer too: harness, orchestrator, OS-enforced sandbox and post-trained models as one installed product. Ollama and LM Studio are more flexible and free; Portable Computer is more complete and requires a Perplexity Pro or Max subscription.

Which one should I use to run local AI agents?

Use Portable Computer if you have NVIDIA hardware on Linux, a Pro or Max subscription, and want a working agent loop without assembling one. Use Ollama if you want the widest model choice, scripting control and cross-platform support including Apple Silicon. Use LM Studio if you want a GUI, easy model discovery and an OpenAI-compatible local endpoint to point other tools at.

Does Ollama or LM Studio work on a Mac?

Yes. Both run natively on Apple Silicon and take advantage of unified memory, which is why Macs are popular for local inference. Perplexity Portable Computer does not support macOS at launch — it targets NVIDIA DGX Spark, RTX PRO and GeForce RTX with 24GB or more, on Linux, with Windows RTX support stated as coming later.

Is running agents locally actually cheaper?

Only at sustained volume. Local steps have zero per-token cost, but you pay for hardware up front. A $4,699 DGX Spark equals roughly 21,000 GPT-5.6 Sol calls at a 30K-in/5K-out shape ($0.22 each). Below a few thousand tasks a month, cloud APIs are cheaper and more capable. Local wins on privacy and on unlimited retry loops, not on casual use.

Quick Answer

Portable Computer vs Ollama vs LM Studio: Local Agents

Published: August 26, 2026

The Short Answer

Ollama and LM Studio run models. Perplexity Portable Computer runs agents. That is the whole comparison in one line, and it is the distinction most “local AI” write-ups blur.

If you already have an agent framework and just need tokens, the free tools win. If you want a working sandboxed agent loop you did not have to assemble, Portable Computer is the only one of the three that ships it.

The Comparison

	Portable Computer	Ollama	LM Studio
Launched / status	Aug 25, 2026	Mature, active	Mature, active
What it ships	Harness + orchestrator + models + sandbox	Model runtime + CLI/API	Model runtime + GUI + local server
Agent loop included	Yes	No — bring your own	No — bring your own
Sandboxing	OS-enforced	None built in	None built in
Cost	Perplexity Pro / Max sub	Free, open source	Free (proprietary)
Platforms	Linux + NVIDIA (Windows stated as coming)	macOS, Linux, Windows	macOS, Linux, Windows
Apple Silicon	No	Yes	Yes
AMD GPUs	No	Yes	Yes
Model choice	Curated: Qwen 3.8 27B, PPLX 27B	Very wide	Very wide
Cloud escalation	Built in, permissioned	Not applicable	Not applicable
Best for	Turnkey private agents	Scripting and control	GUI users and local endpoints

Verified August 26, 2026.

Where Each One Actually Wins

Portable Computer — the assembled stack

Perplexity’s launch, built with NVIDIA, puts the agent harness, the orchestrator, post-trained models and an OS-enforced sandbox on your machine as a single install. Local steps cost nothing per token; heavier reasoning can escalate to a cloud model, but only after explicit permission.

The value is not the 27B models — Qwen 3.8 27B is freely available and PPLX 27B is a post-train of comparable class. The value is that somebody did the orchestration and containment work. Anyone who has tried to sandbox a filesystem-touching agent properly knows that is not a weekend job.

The constraints are real: NVIDIA only, Linux only at launch, 24GB VRAM minimum on GeForce, and a paid subscription.

Ollama — the control option

Ollama remains the default for people who want to script against local models. Widest model catalogue, simple pull-and-run ergonomics, a clean API, and it runs on macOS, Linux and Windows across NVIDIA, AMD and Apple Silicon.

What it does not give you is an agent. There is no harness, no orchestrator, no sandbox — you wire those up with LangGraph, a custom loop, or whatever framework you prefer. For engineers that is a feature. For everyone else it is the reason their local agent project stalled.

LM Studio — the GUI and the endpoint

LM Studio’s edge is approachability plus a very practical trick: it exposes an OpenAI-compatible local server. Point any tool that speaks the OpenAI API at localhost and it works against local weights. That makes LM Studio the easiest way to swap a cloud model out of an existing application without rewriting the application.

Same caveat as Ollama: it serves tokens, it does not run agents.

The Decision, Honestly

Choose Portable Computer if all three are true: NVIDIA hardware on Linux, an existing Perplexity Pro/Max subscription, and work sensitive enough that a sandboxed local harness is worth accepting a curated model list.

Choose Ollama if you are on a Mac, on AMD, or you want to script the loop yourself. It is also the right answer if you want to keep model choice open — the open-weight field moves monthly and a curated catalogue ages.

Choose LM Studio if the goal is “make this existing app use a local model” or you simply want a GUI.

Choose none of them if your workload is occasional. A few hundred tasks a month runs cheaper and dramatically better on a frontier API. Gemini 3.7 Flash at $0.75/$3.75 per million tokens is about $0.041 for a 30K-in/5K-out task — you would need to run a very large number of those to justify a $4,699 box, and the cloud model is more capable at every one of them.

The Capability Gap Nobody Should Skip

A 27B-class local model is not a frontier model. On long-horizon reasoning, multi-file refactors and hard agentic tasks, Claude Opus 5 ($5/$25 per MTok) and GPT-5.6 Sol ($4/$20 since its August 21, 2026 cut) remain meaningfully ahead of anything you will run on a desktop.

Local-first is a privacy and marginal-cost decision, not a capability one. Any comparison that implies otherwise is selling hardware. The tiered pattern — local for the bulk of cheap steps, permissioned cloud escalation for the hard ones — is the design Portable Computer ships precisely because the gap is real.