All LLM Models

Browse 719 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.

Qwen3 8B Base

Alibaba · 8.2B · runs from 4.1 GB

Qwen3 8B Base is an 8.2-billion parameter pretrained foundation model from Alibaba Cloud's Qwen 3 series. As a base model, it is not instruction-tuned and is intended for fine-tuning, research, and as a starting point for custom downstream applications. It was trained on a large multilingual corpus with improved data quality and training methodology compared to the Qwen 2.5 generation. The model runs efficiently on consumer GPUs with 8GB or more of VRAM and serves as the foundation for the Qwen3 8B instruction-tuned variant and community fine-tunes. It is a strong choice for practitioners building specialized models through further training. Released under the Apache 2.0 license.

Qwen3Guard Gen 8B

Alibaba · 8.2B · runs from 4.1 GB

Qwen3Guard Gen 8B is a 8.2B-parameter open language model from Alibaba in the Qwen 3 family. It supports a context window of up to 32,768 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

C4ai Command R V01

CohereForAI · 35.0B · runs from 16.4 GB

C4ai Command R V01 is a 35.0B-parameter open language model from CohereForAI in the Command R family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Qwen1.5 14B Chat

Alibaba · 14.2B · runs from 8 GB

Qwen1.5 14B Chat is a 14.2B-parameter open language model from Alibaba in the Qwen family. It supports a context window of up to 32,768 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Gpt2 Xl

OpenAI · 1.6B · runs from 0.7 GB

GPT-2 XL is the largest variant of the GPT-2 family at 1.6 billion parameters, representing the full release of the model OpenAI originally withheld over safety concerns in 2019. It produces the most coherent and capable outputs of the GPT-2 lineup, though it remains far behind modern multi-billion-parameter instruction-tuned models. At its size, GPT-2 XL still runs easily on most consumer GPUs and even on CPUs with reasonable speed, making it useful for experimentation, fine-tuning projects, and as a baseline for comparing against newer architectures. It requires roughly 3 GB of VRAM at full precision.

SmolLM2 1.7B

Hugging Face · 1.7B · runs from 1.4 GB

SmolLM2 1.7B is the base pretrained model from Hugging Face's second-generation SmolLM family. Unlike the instruct variant, this model has not been fine-tuned for chat or instruction following, making it a strong foundation for custom fine-tuning, domain adaptation, or research into small-scale language model behavior. At 1.7 billion parameters, it provides meaningful language understanding and generation capabilities while remaining lightweight enough to train and experiment with on consumer hardware. Researchers and developers who want full control over downstream behavior will find this base model more flexible than the instruction-tuned version.

SmolLM2 135M

Hugging Face · 135M · runs from 0.4 GB

SmolLM2 135M is one of the smallest capable language models available, developed by Hugging Face as part of their SmolLM2 family. With just 135 million parameters, it requires virtually no VRAM and can run on almost any hardware, making it an excellent starting point for researchers experimenting with language model behavior, fine-tuning workflows, or edge deployment scenarios. Despite its tiny footprint, SmolLM2 135M benefits from improved training data and techniques compared to its first-generation predecessor. It is best suited for lightweight text generation tasks, prototyping, and educational purposes rather than production-grade applications.

Qwen2 72B Instruct

Alibaba · 72.7B · runs from 21.0 GB

Qwen2 72B Instruct is a 72.7B-parameter open language model from Alibaba in the Qwen 2 family. It supports a context window of up to 32,768 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

OLMo 2 0425 1B

Allen AI · 1.5B · runs from 1.2 GB

OLMo 2 0425 1B is a 1.5B-parameter open language model from Allen AI in the OLMo family. It supports a context window of up to 4,096 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Qwen1.5 7B Chat

Alibaba · 7.7B · runs from 4.7 GB

Qwen1.5 7B Chat is a 7.7B-parameter open language model from Alibaba in the Qwen family. It supports a context window of up to 32,768 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

SmolLM2 360M

Hugging Face · 362M · runs from 0.5 GB

SmolLM2 360M is a 362M-parameter open language model from Hugging Face in the SmolLM family. It supports a context window of up to 8,192 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Qwen1.5 32B Chat

Alibaba · 32.5B · runs from 14.3 GB

Qwen1.5 32B Chat is a 32.5B-parameter open language model from Alibaba in the Qwen family. It supports a context window of up to 32,768 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Phi 1 5

Microsoft · 1.4B · runs from 0.7 GB

Phi 1 5 is a 1.4B-parameter open language model from Microsoft in the Phi family. It supports a context window of up to 2,048 tokens. 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.

Falcon 40B

TII UAE · 41.8B · runs from 19.6 GB

Falcon 40B is a 41.8B-parameter open language model from TII UAE in the Falcon family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Qwen 14B Chat

Alibaba · 14.2B · runs from 6.6 GB

Qwen 14B Chat is a 14.2B-parameter open language model from Alibaba in the Qwen family. It supports a context window of up to 8,192 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Qwen1.5 7B

Alibaba · 7.7B · runs from 4.7 GB

Qwen1.5 7B is a 7.7B-parameter open language model from Alibaba in the Qwen family. It supports a context window of up to 32,768 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

SmolLM 1.7B

Hugging Face · 1.7B · runs from 1.4 GB

SmolLM 1.7B is the largest model in Hugging Face's first-generation SmolLM family. At 1.7 billion parameters, it delivers solid general-purpose text generation in a compact package that runs easily on entry-level hardware, though it has been superseded by the improved SmolLM2 and SmolLM3 series. This model remains a reasonable choice for applications where proven stability matters more than cutting-edge performance. For most new projects, however, users should consider the SmolLM2 1.7B or SmolLM3 3B models, which offer better quality at comparable or only slightly higher resource requirements.

Qwen 7B

Alibaba · 7.7B · runs from 3.6 GB

Qwen 7B is a 7.7B-parameter open language model from Alibaba in the Qwen family. It supports a context window of up to 32,768 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Sarvam 30B Uncensored

aoxo · 32.2B · runs from 14 GB

Sarvam 30B Uncensored is a 32.2B-parameter open language model from aoxo. It supports a context window of up to 131,072 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Phi 3 Mini 128k Instruct

Microsoft · 3.8B · runs from 2.7 GB

Phi 3 Mini 128k Instruct is a 3.8B-parameter open language model from Microsoft in the Phi 3 family. It supports a context window of up to 131,072 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

CodeQwen1.5 7B

Alibaba · 7.3B · runs from 3.5 GB

CodeQwen1.5 7B is a 7.3B-parameter open language model from Alibaba in the Qwen family. It supports a context window of up to 65,536 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Llama 2 7B Chat HF

Nous Research · 6.7B · runs from 4.2 GB

Llama 2 7B Chat HF is a 6.7B-parameter open language model from Nous Research in the Llama 2 family. It supports a context window of up to 4,096 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Llama 3.1 Nemotron Nano 8B V1

NVIDIA · 8B · runs from 2.8 GB

Llama 3.1 Nemotron Nano 8B is an 8-billion parameter chat model by NVIDIA, a compact entry in the Nemotron family derived from Meta's Llama 3.1 architecture. It applies NVIDIA's alignment and fine-tuning techniques to deliver improved response quality over the base Llama 3.1 8B Instruct model at the same parameter count. The model runs on consumer GPUs with 8GB or more of VRAM and supports a 128K token context window. Its small footprint and NVIDIA-tuned quality make it a practical option for local inference on mainstream hardware.

Falcon 7B

TII UAE · 7.2B · runs from 3.4 GB

Falcon 7B was one of the first truly competitive open-source large language models, released in mid-2023 by the Technology Innovation Institute in Abu Dhabi. Trained on the massive RefinedWeb dataset, it demonstrated that carefully curated web data could rival models trained on more traditionally assembled corpora. At 7 billion parameters, Falcon 7B helped establish the 7B class as the sweet spot for local inference, offering genuine language understanding on consumer GPUs with as little as 6 GB of VRAM.

Qwen1.5 14B

Alibaba · 14.2B · runs from 8 GB

Qwen1.5 14B is a 14.2B-parameter open language model from Alibaba in the Qwen family. It supports a context window of up to 32,768 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Qwen 14B

Alibaba · 14.2B · runs from 6.6 GB

Qwen 14B is a 14.2B-parameter open language model from Alibaba in the Qwen family. It supports a context window of up to 8,192 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Distilgpt2

distilbert · 88M · runs from 0.0 GB

DistilGPT-2 is a distilled version of OpenAI's GPT-2 model, compressed to just 88 million parameters while retaining much of the original model's text generation ability. Created using knowledge distillation techniques, it offers significantly faster inference than the full GPT-2 with only a modest reduction in output quality. This model is one of the lightest autoregressive language models available and can run on virtually any hardware, including CPUs. It is a practical choice for educational projects, quick prototyping, and applications where inference speed and minimal resource usage are more important than state-of-the-art generation quality.

Qwen1.5 32B

Alibaba · 32.5B · runs from 14.3 GB

Qwen1.5 32B is a 32.5B-parameter open language model from Alibaba in the Qwen family. It supports a context window of up to 32,768 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

QwQ 32B Preview

Alibaba · 32.8B · runs from 14.8 GB

QwQ 32B Preview is a 32.8B-parameter open language model from Alibaba in the QwQ family. It supports a context window of up to 32,768 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.