AutoGPU vs Teralynk

AutoGPU and Teralynk are both large language models tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

AutoGPU

The repo describes autonomous agents writing RTL, running it through real EDA tools, reading timing and layout reports, and revising the design — iterating without a human in the seat for each pass. The documented target is small systolic array architectures, specifically matrix-multiply accelerators; the codebase includes ISA definitions, physical design configs, and golden reference models. At that constrained scope, researchers report the agent loop closes. Scale the design complexity beyond what the existing module hierarchy covers and the agents lose the plot — the feedback loops that work for a mac array do not generalize to a multi-block SoC. Teams pushing past the documented scope end up writing their own agent scaffolding on top, at which point AutoGPU is a reference rather than a runtime.

Teralynk

The scraped page content does not match the tool described in the structured data — the page belongs to Spotter, a travel identification app, not Teralynk's workflow automation platform. No production details about Teralynk's agent architecture, file system integrations, MCP tool use, or governance controls can be sourced from the provided page. The vendor states a freemium model with storage limits and capped workflow runs on the free tier; paid-only features unlock higher run volumes and expanded storage. Teams evaluating this for compliance auditing or multi-cloud document workflows cannot rely on this listing for verified capability claims — vendor documentation should be consulted directly.

Attribute	AutoGPU	Teralynk
Pricing	Free	Paid
Price	—	$9.99/mo
Free trial	No	No
Open source	Yes	No
Has API	No	Yes
Self-hosted option	Yes	Yes
Platforms	—	Web-based SaaS
Released	2026-06	2026-05-25
Pros	Full-stack agentic loop from RTL generation through physical layout hardening, so you avoid the manual handoff between code generation and EDA execution that makes most LLM hardware tools a partial solution. Ships with ISA definitions, module RTL, and golden reference models for matrix-multiply accelerators, which means the agent has structured domain context on day one rather than hallucinating architecture details from scratch. Entirely open-source with no paid-only features, so the full agent scaffolding, EDA integration hooks, and design configs are auditable and forkable — no black-box inference calls gating the loop. Self-hosted by default, which means your RTL, timing reports, and design IP stay on your own infrastructure rather than transiting a vendor's API. Iterative revision loop reads real EDA output — timing reports, layout feedback — and feeds it back into the agent, so design errors surface and get corrected inside the automated loop rather than piling up for a human review session.	Human approval checkpoints built into the agent workflow, so regulated teams can automate the bulk of a compliance or finance process without removing the sign-off step that their audit trail requires. Self-hosted deployment option, which means organizations with strict data residency rules or multi-cloud storage environments can run the platform without sending documents through external SaaS infrastructure. API access, so teams can connect Teralynk's agent execution to existing internal systems rather than forcing a full interface migration — the agents slot into the stack instead of replacing it. No-code agent builder, so business-side teams in legal or HR can configure and modify workflows without queuing every change through an engineering sprint. MCP tool integrations and file system access described in the validator, which means agents can reach across cloud storage environments and external services rather than being limited to data already inside the platform.
Cons	The agent's planning and feedback parsing are scoped to the existing module hierarchy — small systolic arrays and mac structures. When a design introduces module types outside that vocabulary, the agent loses coherent planning context and the loop stalls or produces nonsense RTL; teams at that point are extending the framework from source, not using it. No API surface and no abstraction layer between the agent and the raw EDA toolchain means EDA tool version changes or environment differences break the agent loop silently; debugging requires tracing through agent execution logs and EDA stdout, not a structured error interface. Star and fork counts from the repository indicate this is an early-stage research artifact with a single primary contributor — community-reported workarounds, tested configurations, and maintained documentation are sparse, so teams that hit an undocumented edge case have the source code and nothing else. Teams needing a maintained, production-grade EDA automation layer with active support will move to a commercial EDA vendor's scripting environment instead.	The free tier caps storage and limits workflow runs to a small number — teams move past proof-of-concept into any real document volume and the ceiling appears immediately, forcing an upgrade decision before the tool is validated in production. No verified production evidence can be cited from the vendor's own page because the scraped content is from an entirely different product; teams cannot cross-check claimed capabilities against live documentation through this listing, and must independently audit vendor claims before committing engineering time. When workflow complexity scales beyond what the no-code builder can express — branching logic that depends on what a prior agent returned, or conditional routing across more than a few steps — teams that need that depth will either add a code extension layer or switch to a platform like n8n or Temporal where complex branching is a first-class design primitive, not a workaround.

Bottom line

AutoGPU is free while Teralynk is paid; AutoGPU is open source; only Teralynk exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between AutoGPU and Teralynk?

AutoGPU is Free and open source, while Teralynk is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is AutoGPU better than Teralynk?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.