Overview
The summaryAntigravity Local Models is a compatibility‑checking tool aimed at users who want to run Gemma 4 language models on their own hardware. By selecting a computer’s GPU/CPU configuration and the exact Gemma 4 variant (E2B, E4B, 12B, 26B, or 31B), the site’s checker returns a clear “yes”, “no”, or “slower fallback” result and suggests the appropriate runtime—LiteRT or an OpenAI‑compatible local server such as Ollama. The product supplies step‑by‑step guides for each supported operating system (Windows, macOS, Linux) and for different hardware types (NVIDIA, AMD, Intel Arc, Apple Silicon). It also offers CLI and SDK documentation for developers who need finer control. The primary audience appears to be technically proficient individuals or teams that require offline AI agents, want to avoid cloud latency or costs, and have sufficient GPU or unified memory to meet the model’s memory requirements. The workflow consists of gathering hardware specs, selecting a model, running the compatibility check, and then following the provided commands to launch the model locally, with fallback options if the hardware falls short.
Antigravity Local Models lets you verify whether your computer can run specific Gemma 4 LLMs locally, guiding you through setup steps.
What this product is
In plain languageAntigravity Local Models is a compatibility‑checking tool aimed at users who want to run Gemma 4 language models on their own hardware. By selecting a computer’s GPU/CPU configuration and the exact Gemma 4 variant (E2B, E4B, 12B, 26B, or 31B), the site’s checker returns a clear “yes”, “no”, or “slower fallback” result and suggests the appropriate runtime—LiteRT or an OpenAI‑compatible local server such as Ollama. The product supplies step‑by‑step guides for each supported operating system (Windows, macOS, Linux) and for different hardware types (NVIDIA, AMD, Intel Arc, Apple Silicon). It also offers CLI and SDK documentation for developers who need finer control. The primary audience appears to be technically proficient individuals or teams that require offline AI agents, want to avoid cloud latency or costs, and have sufficient GPU or unified memory to meet the model’s memory requirements. The workflow consists of gathering hardware specs, selecting a model, running the compatibility check, and then following the provided commands to launch the model locally, with fallback options if the hardware falls short.
From Antigravity Local Models's own site:“Can this computer run the model you want?”
What makes Antigravity Local Models different?
vs. alternatives in this categoryAntigravity Local Models distinguishes itself by focusing on pre‑flight hardware validation for specific Gemma 4 LLMs, rather than offering a generic cloud API. It combines a simple yes/no compatibility matrix with detailed, OS‑specific installation guides and supports both LiteRT and Ollama runtimes, giving users flexibility to run AI agents fully offline on a variety of hardware platforms.
Key features
From the product's own siteShows whether a chosen Gemma 4 model will run, run slower, or not at all on the selected GPU/CPU configuration.
Supports Gemma 4 variants from 7.5 GB (E2B) up to 20 GB (31B) with package‑size details listed on the site.
Offers LiteRT or an external OpenAI‑compatible server such as Ollama, depending on the hardware and route.
Provides OS‑specific step‑by‑step instructions and verification checklists to launch the selected model locally.
Includes command‑line controls and SDK documentation for developers needing custom integration.
Who it's for
Grounded in pricing, the site's own claims, and categoryShould use this
Technical users or small teams who want to run Gemma 4 models offline, have access to a GPU or unified memory of at least 12 GB, and are comfortable following detailed setup guides.
Shouldn't use this
Anyone without sufficient VRAM or system RAM for the desired model, users who need a fully managed cloud solution, and organizations that require transparent pricing before committing.
Use cases
Who uses this and whyA machine‑learning engineer uses Antigravity Local Models to confirm a workstation can host Gemma 4 26B with LiteRT, avoiding unexpected crashes during development.
A data‑science team selects the Gemma 4 12B model on a laptop with 12 GB VRAM, follows the step‑by‑step guide, and runs the model locally to keep sensitive data off the cloud.
An AI‑ops specialist checks compatibility for an Apple Silicon Mac, chooses the appropriate Gemma 4 tag, and deploys the model via Ollama for low‑latency inference.
Where we found it
Little foundLittle found outside the product's own pages and its launch listing. Common for a new product. We found 0 independent sources. This is a count of places, not a judgement of whether the product's claims are true.
- Own siteProduct website
- Product HuntProduct Hunt listing
What works
Grounded in the site's own pages and the evidence foundThe site says the compatibility checker delivers a clear yes/no/slow fallback result for each hardware‑model pair, and the guided guides walk users through installing LiteRT or Ollama. It also supports Windows, macOS, and Linux, and the Reddit post notes that the setup enables Google's AI agents to run completely offline.
What to know
Caveats, gotchas, and gapsPricing information is not disclosed on the website and there is no free tier. The documentation recommends 24 GB+ VRAM for the Gemma 4 26B + LiteRT route, but clarifies that lower‑VRAM models (E2B, E4B, 12B) fit inside 12 GB package size. This is a recent launch with limited third‑party coverage.
Pros & cons
+Pros
- Provides an immediate yes/no hardware compatibility result for each Gemma 4 model.
- Offers detailed OS‑specific guides that walk users through local installation steps.
- Supports multiple runtimes (LiteRT, Ollama) and major desktop operating systems.
−Cons
- No publicly disclosed pricing; users must contact the vendor for cost information.
- Requires high VRAM (24 GB+) for the largest model, limiting use on typical consumer GPUs.
- Limited to Gemma 4 models; does not mention support for other LLM families.
What the site says
3 from its own pagesStatements taken from Antigravity Local Models's own pages, each with the page it came from. We list them; we do not rule on whether they are true.
- Gemma 4 26B — about 19 GBantigravitylocalmodels.com
- The current documented route recommends 24 GB+ VRAM or unified/shared memory and a supported accelerated platformantigravitylocalmodels.com
- The current E2B, E4B and 12B default tags fit inside 12 GB by package sizeantigravitylocalmodels.com
What others say
0 outside pagesWe found no independent pages about Antigravity Local Models yet. That is normal for a new launch, and it is a fact about coverage, not a mark against the product.
How we checked this one
Full methodologyThis review was produced by the SaaSNet.work pipeline — no hands-on testing, no payment from the product. Here's exactly what was checked for Antigravity Local Models.
We never tested the product hands-on and never accepted payment for this review. Spot something wrong? Request a correction.
Frequently asked questions
4 questionsIs Antigravity Local Models free?
No. The site does not list a free tier and the pricing page is not found.
How much does Antigravity Local Models cost?
Pricing details are not disclosed on the website; there is no publicly available tier or price list.
What are Antigravity Local Models's main limitations?
It requires sufficient GPU/VRAM (24 GB+ for the 26B model) and is limited to Gemma 4 variants; performance may degrade if the model falls back to CPU.
Can I run Gemma 4 26B on a machine with 12 GB VRAM?
The site says the 26B model needs 24 GB+ VRAM; with only 12 GB you would need to choose a smaller model or accept a slower fallback.
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