All LLM Models

Browse 54 LLM models with VRAM requirements, quantization options, and hardware compatibility.

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Understanding LLM VRAM Requirements

How much VRAM you need depends on the model size and quantization level. Quantization reduces the precision of model weights, trading small quality losses for significantly lower VRAM usage. For example, a 7B parameter model needs ~14 GB at FP16 but only ~4 GB at Q4_K_M quantization.

Gemma 2B

Google · 2.5B · runs from 1.2 GB

Gemma 2B is a 2.5B-parameter open language model from Google in the Gemma 2 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Gemma 4 E2B

Google · 5.1B · runs from 2.5 GB

Gemma 4 E2B is Google DeepMind's smallest model in the Gemma 4 family, a dense architecture with roughly 5.1 billion total parameters, of which Google describes about 2.3 billion as its effective footprint at inference. This is the pretrained base checkpoint, not an instruction-tuned chat model, meant as a starting point for fine-tuning. The Gemma 4 family is multimodal — text, image, and at this size natively audio — and E2B targets efficient on-device execution on phones and laptops. It is easy to run locally, even on modest hardware once quantized. It supports a 131,072 token context window. It carries Google's Gemma 4 license terms, published as Apache 2.0 on Hugging Face with additional Gemma-specific usage terms linked from the card. Published in March 2026, it is the smallest of five sizes in the Gemma 4 lineup, aimed at mobile and edge deployment.

Gemma 3 1B Pt

Google · 1000M · runs from 0.3 GB

Gemma 3 1B Pt is a 1000M-parameter open language model from Google in the Gemma 3 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Gemma 1.1 2B IT

Google · 2.5B · runs from 1.1 GB

Gemma 1.1 2B IT is a 2.5B-parameter open language model from Google in the Gemma family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Gemma 4 12B IT Assistant

Google · 12B · runs from 5.4 GB

Gemma 4 12B IT Assistant is a 12B-parameter open language model from Google in the Gemma 4 family. It supports a context window of up to 262,144 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Gemma 2 2B

Google · 2.6B · runs from 1.2 GB

Google Gemma 2 2B is a 2-billion parameter base (pretrained) model from Google's Gemma 2 family. As a base model, it is not instruction-tuned and is intended for fine-tuning, research, and custom downstream applications. Its compact size makes it suitable for experimentation, rapid prototyping, and domain-specific fine-tuning on consumer hardware with minimal VRAM. Released under the Gemma license.

Gemma 4 12B

Google · 12.0B · runs from 6.1 GB

Gemma 4 12B Unified is a base checkpoint in Google DeepMind's Gemma 4 family, an open-weight multimodal model with roughly 12 billion parameters that takes text, image, and audio input and produces text output. Its "Unified" design skips separate encoders per modality, projecting raw image patches and audio directly into the language model, cutting multimodal latency. As a pretrained release, it's a base for fine-tuning or research rather than direct chat use. It supports a 256K token context window and multilingual pretraining across 140+ languages, and is released under the Apache 2.0 license. At roughly 12 billion parameters, it runs on a single consumer GPU with around 8-12 GB of VRAM once quantized to 4-bit.

Gemma 3 270M

Google · 268M · runs from 0.1 GB

Google Gemma 3 270M is a 270-million parameter base (pretrained) model from Google's Gemma 3 family. It is an experimental release intended for research, fine-tuning, and exploring the capabilities of ultra-small language models. The model runs on virtually any hardware with negligible resource requirements. Released under the Gemma license.

Gemma 2 9B

Google · 9.2B · runs from 4.3 GB

Gemma 2 9B is a 9.2B-parameter open language model from Google in the Gemma 2 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Codegemma 2B

Google · 2.5B · runs from 1.2 GB

Codegemma 2B is a 2.5B-parameter open language model from Google in the Gemma 2 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Gemma 7B IT

Google · 8.5B · runs from 18.8 GB

Google Gemma 7B IT is a 7-billion parameter instruction-tuned model from the original Gemma generation. It is fine-tuned for conversational use and general instruction following, running efficiently on consumer GPUs with 8GB or more of VRAM. As a first-generation Gemma model, it has been superseded by Gemma 2 and Gemma 3 models in quality and capability, but it remains well-supported by inference frameworks. Released under the Gemma license.

Gemma 7B

Google · 8.5B · runs from 18.8 GB

Google Gemma 7B is a 7-billion parameter base (pretrained) model from the original Gemma generation, Google's first openly available family of language models. It represents Google's initial entry into the open-weight LLM space. While superseded by Gemma 2 and Gemma 3 in terms of benchmark performance, the original Gemma 7B remains a solid foundation model and a useful reference point in the evolution of Google's open models. Released under the Gemma license.

Gemma 2B IT

Google · 2.5B · runs from 1.2 GB

Gemma 2B IT is a 2.5B-parameter open language model from Google in the Gemma 2 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Functiongemma 270M IT

Google · 268M · runs from 0.6 GB

Functiongemma 270M IT is a 268M-parameter open language model from Google in the Gemma family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Gemma 3n E4B IT Litert Lm

Google · 4B · runs from 1.9 GB

Gemma 3n E4B IT Litert Lm is a 4B-parameter open language model from Google in the Gemma 3 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

T5gemma 2B 2B Ul2

Google · 5.6B · runs from 2.6 GB

T5gemma 2B 2B Ul2 is a 5.6B-parameter open language model from Google in the Gemma 2 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Gemma 2 2B Jpn IT

Google · 2.6B · runs from 5.8 GB

Gemma 2 2B Jpn IT is a 2.6B-parameter open language model from Google in the Gemma 2 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Txgemma 2B Predict

Google · 2.6B · runs from 1.2 GB

Txgemma 2B Predict is a 2.6B-parameter open language model from Google in the Gemma 2 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Gemma 3n E2B IT Litert Lm

Google · 2B · runs from 0.9 GB

Gemma 3n E2B IT Litert Lm is a 2B-parameter open language model from Google in the Gemma 3 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Shieldgemma 2B

Google · 2.6B · runs from 1.2 GB

Shieldgemma 2B is a 2.6B-parameter open language model from Google in the Gemma 2 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Vaultgemma 1B

Google · 1.0B · runs from 2.3 GB

Vaultgemma 1B is a 1.0B-parameter open language model from Google in the Gemma family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Codegemma 7B IT

Google · 8.5B · runs from 4.0 GB

Codegemma 7B IT is a 8.5B-parameter open language model from Google in the Gemma family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

T5gemma L L Ul2 IT

Google · 1.2B · runs from 2.7 GB

T5gemma L L Ul2 IT is a 1.2B-parameter open language model from Google in the Gemma family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

T5gemma B B Ul2 IT

Google · 591M · runs from 1.3 GB

T5gemma B B Ul2 IT is a 591M-parameter open language model from Google in the Gemma family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.