All LLM Models
Browse 41 LLM models with VRAM requirements, quantization options, and hardware compatibility.
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.
Model List
Llama Guard 3 8B
Meta · 8.0B · runs from 17.7 GB
46.1K
327Meta Llama Guard 3 8B is an 8-billion parameter safety classifier model built on the Llama 3.1 architecture. Unlike general-purpose chat models, Llama Guard is specifically designed to classify whether prompts or responses contain unsafe content across categories such as violence, sexual content, criminal planning, and other policy violations. The model is intended to be used as a moderation layer in LLM-based applications, providing input and output safety filtering. It follows a taxonomy-based classification approach and can be customized for different safety policies. Released under the Llama 3.1 Community License.
Opt 2.7B
Meta · 2.7B · runs from 5.9 GB
Opt 2.7B is a 2.7B-parameter open language model from Meta. 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.
Llama 3.1 70B
Meta · 70.6B · runs from 33.0 GB
Meta Llama 3.1 70B is a 70.6-billion parameter base (pretrained) model from the Llama 3.1 family. It supports a 128K token context window and was trained on a massive multilingual corpus. As a base model, it is designed for fine-tuning and research rather than direct conversational use. The model serves as the foundation for the Llama 3.1 70B Instruct variant and numerous community fine-tunes. It delivers strong performance across language understanding and generation benchmarks. Released under the Llama 3.1 Community License.
Llama 2 13B HF
Meta · 13.0B · runs from 6.1 GB
Llama-2-13b-hf is Meta's 13-billion-parameter base (pretrained, not instruction-tuned) language model from the original Llama 2 family, intended as a general-purpose foundation for natural-language generation and further fine-tuning rather than direct assistant-style chat, for which Meta released separate Llama-2-Chat checkpoints. It is an auto-regressive transformer trained on 2 trillion tokens of publicly available data with a September 2022 knowledge cutoff, using a global batch size of 4 million tokens; unlike the 70B model, the 13B size does not use grouped-query attention. It is a historically significant open-weight release rather than a current state-of-the-art model by 2026 standards. At 13 billion parameters, it fits on a single consumer GPU once quantized. Context length is 4,096 tokens. It is released under the Llama 2 Community License, a custom license that is free for most commercial and research use but requires organizations with more than 700 million monthly active users to request separate permission from Meta. It was published in July 2023.
CodeLlama 34B HF
Meta · 33.7B · runs from 15.0 GB
CodeLlama 34B HF is a 33.7B-parameter open language model from Meta in the Code Llama family. It supports a context window of up to 16,384 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Llama 2 70B HF
Meta · 69.0B · runs from 151.8 GB
Llama 2 70B HF is a 69.0B-parameter open language model from Meta in the Llama 2 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Llama 3.1 405B Instruct
Meta · 405.9B · runs from 189.7 GB
Llama 3.1 405B Instruct is Meta's largest Llama 3.1 model, a 405.9-billion-parameter dense (non-mixture-of-experts) Transformer instruction-tuned for chat, tool use, and multilingual tasks across 8 languages. At release it was the first openly available model benchmarked as competitive with leading proprietary models on general knowledge, math, and reasoning. Running it at full precision requires a multi-GPU server; even quantized, it needs a substantial multi-GPU workstation rather than a single consumer card. Context length is 131,072 tokens (128K), a large jump from the 8,192-token window of the original Llama 3. It is released under the Llama 3.1 Community License, a custom license that requires organizations with more than 700 million monthly active users to obtain a separate license from Meta and imposes an acceptable-use policy on prohibited applications. It was published in July 2024, alongside the smaller 8B and 70B Llama 3.1 models.
Meta Llama Guard 2 8B
Meta · 8.0B · runs from 17.7 GB
Meta Llama Guard 2 8B is a 8.0B-parameter open language model from Meta in the Llama family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Llama 2 70B Chat HF
Meta · 69.0B · runs from 151.8 GB
Llama 2 70B Chat HF is a 69.0B-parameter open language model from Meta in the Llama 2 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
KernelLLM
Meta · 8.0B · runs from 4.0 GB
KernelLLM is a 8.0B-parameter open language model from Meta. 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.
MobileLLM R1.5 950M
Meta · 950M · runs from 2.1 GB
MobileLLM R1.5 950M is a 950M-parameter open language model from Meta. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
