Gemma, Explained
Google's Open Weights — Worth Running Yourself?
Gemma 4 (April 2026) is the real thing: Apache 2.0, five sizes from edge to workstation, weights you actually download. This page answers the two questions that matter — what local hosting truly takes, and what to use when you want the capability without the GPU stack.
Comparison basis
Gemma 4 facts come from Google's release and developer documentation
Google's April 2026 release identifies the five Gemma 4 sizes and Apache 2.0 license. Hardware fit, latency, and answer quality still depend on the chosen size, quantization, runner, and device, so test representative prompts before committing to a local stack.
Unless a separate method is stated above, this page does not claim a head-to-head product-quality benchmark.
Checked: 2026-08-17
Choose by constraint
Local Gemma or a hosted model?
Choose local Gemma for offline work, weight-level control, fine-tuning, and data that must remain on your machine. Choose @vustbot when you want to compare hosted models on the same task without installing a runner, sizing quantization, or maintaining hardware.
See the difference
Open weights, the real cost of local, and the no-setup path.
Who Gemma 4 actually fits
- Privacy-hard requirements
- Data that must remain on an owned machine — legal, medical, trade secrets
- A fully local, offline-configured Gemma runner can keep prompts on that machine. Apache 2.0 permits commercial use subject to its terms; deployment, access controls, and security remain yours to verify.
- Tinkerers with hardware
- An idle GPU and curiosity about running models yourself
- Gemma 4's five sizes (E2B to 31B) give a real ladder: start small on a laptop, scale to what your VRAM holds.
- People who want the outcome, not the setup
- Comparing capable models without managing a local stack
- Use @vustbot to run the same task across hosted models, then decide whether local control is worth the hardware and maintenance.
The open-weights reality check
- 01
Download is the easy part
Weights are free under Apache 2.0. A runner (Ollama, LM Studio) gets a small Gemma answering on a laptop in an evening.
- 02
The size sets the bill
E-series runs on modest hardware; 12B and up reward a real GPU with generous VRAM. Quantization trades quality for fit.
- 03
Benchmark the real task
Compare representative prompts before choosing. Model size, quantization, hardware, latency, and answer quality all affect whether local or hosted is the better fit.
Compare hosted models without the GPU stack
Open @vustbot, give several models the same task, and compare the answers before deciding whether local hosting is worth the setup.
Choose by the constraint that matters
Local control
Gemma is the better shape for offline work, fine-tuning, and data that must remain on your machine.
No-setup comparison
@vustbot is the shorter path when you want to compare hosted models on one task without choosing quantization or maintaining a GPU stack.
Facts verified
Gemma 4: released April 2026, Apache 2.0, five sizes (E2B, E4B, 12B, 31B, 26B A4B) — checked against Google's developer docs on August 17, 2026.
Frequently asked questions
What is Gemma 4 and how is it different from Gemini?
Gemini is Google's closed frontier family served through Google's cloud; Gemma is the open-weights sibling you download and run yourself. Gemma 4 (April 2026) ships five sizes — E2B, E4B, 12B, 31B and a 26B A4B variant — under Apache 2.0, which permits commercial use and fine-tuning without a bespoke license.
Is Gemma 4 really free?
Google provides the weights without a license fee, and Apache 2.0 is permissive. Running them still costs hardware, electricity, setup time, and maintenance. Smaller sizes fit more modest devices; larger variants require more memory and compute.
What do I need to run Gemma locally?
A runner like Ollama or LM Studio, the model weights in a quantization your RAM/VRAM can hold, and some patience for setup. The E-series sizes are built for edge devices; 12B and up reward real GPU memory. There's no universal spec sheet — the size you pick sets the bill.
Can I use Gemma in @vustbot?
Run Gemma on your own hardware when open weights are the requirement. Use @vustbot when you want to compare hosted frontier models on the same prompt without installing a runner or managing a GPU.
When is local Gemma the better choice than a cloud model?
When data must not leave your machine, when you need offline operation, when you want to fine-tune weights, or when you already own suitable hardware. Hosted models are usually simpler when setup time and maintenance matter more than local control — see our cloud-vs-local decision table.
Does Gemma 4 replace GPT or Claude?
Gemma can replace a cloud model for tasks your chosen size handles well, especially when local control matters. For difficult workloads, test the same representative prompts against local and hosted options before choosing; model size, quantization, hardware, latency, and answer quality all change the tradeoff.
Ready when you are
Local when control matters. Hosted when time matters.
Gemma 4 fits privacy, offline work, and fine-tuning. @vustbot fits fast comparison without hardware maintenance.