All LLM Models
Browse 593 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
Qwen3.6 28B REAP20 A3B
0xSero · 28.2B · runs from 11.3 GB
1.1K
27Qwen3.6 28B REAP20 A3B is a 28.2B-parameter open language model from 0xSero in the Qwen 3.6 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 27B IT
Google · 27.2B · runs from 9.0 GB
Google Gemma 2 27B IT is a 27.2-billion parameter instruction-tuned model from Google's Gemma 2 generation. It is a text-only chat model optimized for conversational use, reasoning, and instruction following. Gemma 2 27B IT was one of the strongest openly available models in its size class at release. The model requires a GPU with at least 24GB of VRAM for quantized local inference. It is widely supported by popular inference engines and remains a strong choice for users seeking high-quality local chat without needing 70B-class hardware. Released under the Gemma license.
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.
Qwen2.5 Coder 3B
Alibaba · 3.1B · runs from 1.4 GB
Qwen2.5 Coder 3B is a 3.1B-parameter open language model from Alibaba in the Qwen 2.5 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 4 Reasoning Plus
Microsoft · 14.7B · runs from 4.8 GB
Phi 4 Reasoning Plus is a 14.7B-parameter open language model from Microsoft in the Phi 4 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.
Llama 3.1 8B
Meta · 8.0B · runs from 3.8 GB
Meta Llama 3.1 8B is an 8-billion parameter base (pretrained) model from the Llama 3.1 family. It is not instruction-tuned and is intended for fine-tuning, research, and custom downstream applications. Compared to Llama 3 8B, it extends the context window to 128K tokens and benefits from improved training data and methodology. The model uses grouped-query attention and was trained on a multilingual corpus. It is released under the Llama 3.1 Community License and is widely used as a foundation for community fine-tunes and specialized models.
NeuralDaredevil 8B Abliterated
mlabonne · 8.0B · runs from 4.0 GB
NeuralDaredevil 8B Abliterated is a 8.0B-parameter open language model from mlabonne. 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.
Deepseek Coder 1.3B Instruct
DeepSeek · 1.3B · runs from 1.3 GB
DeepSeek Coder 1.3B Instruct is an ultra-compact code model designed for environments where hardware resources are extremely limited. Despite having just 1.3 billion parameters, it can handle basic code completion, simple generation tasks, and code Q&A across common programming languages. This is one of the smallest viable code models available, capable of running on integrated graphics or very low-end dedicated GPUs. It is well suited for edge deployment, embedded development environments, or as a fast local autocomplete engine where response speed matters more than handling complex multi-file reasoning tasks.
Olmo 3 7B Instruct
Allen AI · 7.3B · runs from 3.4 GB
OLMo 3 7B Instruct is an instruction-tuned language model from the Allen Institute for AI, built as part of their Open Language Model initiative. Like all OLMo releases, it comes with fully open training data, code, and intermediate checkpoints, setting a high standard for reproducibility and scientific transparency in the LLM space. At roughly 7 billion parameters, this model delivers competitive performance on instruction following, reasoning, and general knowledge tasks while remaining runnable on consumer GPUs with 8 GB or more of VRAM. It is an excellent choice for users who value open science and want a capable, well-documented model for local chat and assistant applications.
Moonlight 16B A3B Instruct
Moonshot AI · 16.0B · runs from 5.1 GB
Moonlight 16B A3B Instruct is a 16.0B-parameter open language model from Moonshot AI in the Moonlight 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.
Granite 4.0 Micro
IBM · 3.4B · runs from 1.4 GB
Granite 4.0 Micro is a 3.4B-parameter open language model from IBM in the Granite 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.
Qwen2.5 Coder 1.5B
Alibaba · 1.5B · runs from 1 GB
Qwen2.5 Coder 1.5B is a 1.5-billion parameter code-specialized model from Alibaba Cloud's Qwen 2.5 Coder series. It is the smallest Coder variant that balances meaningful code generation capability with extremely low resource requirements, running on GPUs with as little as 2-4GB of VRAM. The model is suitable for lightweight code completion, simple code generation tasks, and as a compact local coding assistant in resource-constrained environments. It supports a 128K token context window. Released under the Apache 2.0 license.
Pantheon Reasoning 27B
Gryphe · 27.8B · runs from 8.4 GB
Pantheon Reasoning 27B is a 27.8B-parameter open language model from Gryphe. 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.
Nanbeige4.1 3B
Nanbeige · 3.9B · runs from 2.1 GB
Nanbeige4.1 3B is a compact chat model from Nanbeige, a Chinese AI startup focused on building efficient small-scale language models. At just under 4 billion parameters, it is designed to run on virtually any modern GPU or even on CPU, making it one of the more accessible options for users with limited hardware. Despite its small size, it handles basic conversation, simple reasoning, and Chinese-English bilingual tasks, serving as a practical entry point for local LLM experimentation.
Starcoder2 15B
BigCode · 16.0B · runs from 7.3 GB
Starcoder2 15B is a 16.0B-parameter open language model from BigCode in the StarCoder 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.
Phi 3 Mini 4k Instruct
Microsoft · 3.8B · runs from 2.7 GB
Microsoft Phi 3 Mini 4K Instruct is a 3.8-billion parameter instruction-tuned model from Microsoft Research's Phi 3 generation, with a 4K token context window. The Phi 3 family demonstrated that small models trained on carefully curated, high-quality data can achieve performance competitive with models several times their size. The model runs on consumer GPUs with as little as 4-6GB of VRAM when quantized, making it one of the most accessible capable chat models for local deployment. Released under the MIT license.
Qwen2.5 3B
Alibaba · 3.1B · runs from 1.6 GB
Qwen2.5 3B is a 3.1B-parameter open language model from Alibaba in the Qwen 2.5 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.
Magistral Small 2506
Mistral AI · 23.6B · runs from 7.2 GB
Magistral Small 2506 is a 23.6B-parameter open language model from Mistral AI in the Mistral family. It supports a context window of up to 40,960 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
HRM Text 1B
sapientinc · 1.2B · runs from 1 GB
HRM Text 1B is a 1.2B-parameter open language model from sapientinc. 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.
Falcon H1 7B Instruct
TII UAE · 7.6B · runs from 2.6 GB
Falcon H1 7B Instruct is a 7.6B-parameter open language model from TII UAE in the Falcon 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.
Llama 3.2 1B
Meta · 1.2B · runs from 0.6 GB
Meta Llama 3.2 1B is a 1.2-billion parameter base (pretrained) model from Meta's Llama 3.2 release. It is the smallest model in the Llama 3.2 family and is designed for research, fine-tuning, and embedding into resource-constrained environments. It supports a 128K token context window. As a base model, it is not optimized for conversational use without further fine-tuning. Its minimal resource requirements make it suitable for experimentation, edge deployment, and as a starting point for domain-specific fine-tuning. Released under the Llama 3.2 Community License.
Nanbeige4.1 3B Heretic
heretic-org · 3.9B · runs from 2.1 GB
Nanbeige4.1 3B Heretic is a 3.9B-parameter open language model from heretic-org. 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.
Medgemma 27B Text IT
Google · 27.0B · runs from 8.2 GB
Google MedGemma 27B Text IT is a 27-billion parameter instruction-tuned model specialized for the medical domain, built on the Gemma architecture by Google. It is fine-tuned on medical and clinical text data to provide improved performance on healthcare-related tasks such as medical question answering, clinical reasoning, and health information summarization. The model requires a GPU with at least 24GB of VRAM for quantized inference. Its domain specialization makes it notably more capable than general models on clinical benchmarks, though it should not be used as a substitute for professional medical advice. Released under the Gemma license.
Cogito V1 Preview Qwen 32B
deepcogito · 32B · runs from 10.4 GB
Cogito V1 Preview Qwen 32B is a 32B-parameter open language model from deepcogito in the Qwen 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.
OmniCoder 9B
Tesslate · 9.4B · runs from 3.5 GB
OmniCoder 9B is a 9.4B-parameter open language model from Tesslate. 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.
Hermes 4.3 36B
Nous Research · 36.2B · runs from 10.5 GB
Hermes 4.3 36B is a 36.2B-parameter open language model from Nous Research in the Hermes family. It supports a context window of up to 524,288 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
North Mini Code 1.0
Cohere · 30.5B · runs from 8.8 GB
North Mini Code 1.0 is a 30.5B-parameter open language model from Cohere. It supports a context window of up to 500,000 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Zephyr 7B Beta
Hugging Face · 7.2B · runs from 3.6 GB
Zephyr 7B Beta is a 7.2B-parameter open language model from Hugging Face. 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 4 Reasoning
Microsoft · 14.7B · runs from 4.8 GB
Phi 4 Reasoning is a 14.7B-parameter open language model from Microsoft in the Phi 4 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.
Huihui MiniCPM5 1B Abliterated
huihui-ai · 1.1B · runs from 0.6 GB
Huihui MiniCPM5 1B Abliterated is a 1.1B-parameter open language model from huihui-ai in the MiniCPM 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.