All LLM Models

Browse 984 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 30B A3B Instruct 2507

Alibaba · 30.5B · runs from 8.8 GB

Qwen3 30B A3B Instruct 2507 is a July 2025 updated mixture-of-experts model from Alibaba with 30 billion total parameters but only around 3 billion active during inference. This MoE architecture gives it a remarkably small memory and compute footprint relative to its total parameter count, letting users run a model with broad knowledge on mid-range hardware. The 2507 instruct refresh improves alignment and instruction-following quality over the original release. Because only a fraction of the weights are active at any given time, this model can often run on a single consumer GPU with 8 GB or more of VRAM when quantized, making it an excellent choice for users who want strong chat performance without heavyweight hardware.

Meta Llama 3.1 8B Instruct

Meta · 8.0B · runs from 2.4 GB

Meta Llama 3.1 8B Instruct is a 8.0B-parameter open language model from Meta in the Llama 3 family. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Kimi K2.7 Code

Moonshot AI · 1058.6B · runs from 295.0 GB

Kimi K2.7 Code is a 1058.6B-parameter open language model from Moonshot AI in the Kimi K2 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 IT

Google · 2.6B · runs from 0.9 GB

Google Gemma 2 2B IT is a 2-billion parameter instruction-tuned model from Google's Gemma 2 family, the smallest variant in the Gemma 2 series. It is designed for efficient local inference on resource-constrained hardware, handling basic conversational tasks and simple instruction following at minimal compute cost. The model can run on GPUs with as little as 4GB of VRAM when quantized, and even on CPU-only setups. Released under the Gemma license.

Gemma 4 12B IT Qat Q4 0 Unquantized

Google · 12.0B · runs from 6.1 GB

Gemma 4 12B IT Qat Q4 0 Unquantized is a 12.0B-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.

MiniMax M3

MiniMax · 427.0B · runs from 117.9 GB

MiniMax M3 is a 427.0B-parameter open language model from MiniMax in the MiniMax family. It supports a context window of up to 1,048,576 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

VisionFunctions

Qwen3.5 122B A10B

Alibaba · 125.1B · runs from 53.5 GB

Qwen3.5 122B A10B is a 125.1B-parameter open language model from Alibaba in the Qwen 3.5 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 4 31B IT Qat Q4 0 Unquantized

Google · 32.7B · runs from 15.5 GB

Gemma 4 31B IT Qat Q4 0 Unquantized is a 32.7B-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.

Qwen2.5 1.5B Instruct

Alibaba · 1.5B · runs from 0.8 GB

Qwen2.5 1.5B Instruct is a 1.5-billion parameter instruction-tuned model from Alibaba Cloud's Qwen 2.5 series. It is a lightweight model suitable for deployment on minimal hardware, including low-VRAM GPUs and even CPU-only setups with acceptable latency. It supports a 128K token context window. The model handles basic conversational tasks, simple question answering, and text generation. While limited in reasoning depth compared to larger variants, it is useful for applications where fast response times and minimal resource consumption are priorities. Released under the Apache 2.0 license.

LFM2.5 1.2B Instruct

Liquid AI · 1.2B · runs from 0.8 GB

LFM2.5 1.2B Instruct is an instruction-tuned model from Liquid AI that uses a novel hybrid architecture combining state-space models with attention mechanisms. At just 1.2 billion parameters, it is exceptionally lightweight and can run on virtually any hardware, including laptops and edge devices. Liquid AI's unconventional architecture aims to deliver better efficiency and longer context handling than traditional transformer models at this scale, making it an interesting option for users exploring alternatives to standard transformer-based LLMs.

Qwen3 0.6B

Alibaba · 752M · runs from 0.6 GB

Qwen3 0.6B is the smallest instruction-tuned model in Alibaba Cloud's Qwen 3 family, with approximately 752 million parameters. It is designed for ultra-lightweight deployment where minimal hardware resources are available, running comfortably on virtually any modern GPU or CPU-only setups. The model supports hybrid thinking mode despite its tiny footprint. While limited in reasoning depth compared to larger variants, Qwen3 0.6B handles basic chat, simple summarization, and lightweight instruction following. It is primarily useful for edge deployment, rapid prototyping, and experimentation where model size is a critical constraint. Released under the Apache 2.0 license.

Kimi K2.5

Moonshot AI · 1058.6B · runs from 295.0 GB

Kimi K2.5 is a 1058.6B-parameter open language model from Moonshot AI in the Kimi K2 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 4 E4B IT Qat Q4 0 Unquantized

Google · 7.9B · runs from 3.9 GB

Gemma 4 E4B IT Qat Q4 0 Unquantized is a 7.9B-parameter open language model from Google in the Gemma 4 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.

MiniMax M2.7

MiniMax · 228.7B · runs from 63.5 GB

MiniMax M2.7 is a 228.7B-parameter open language model from MiniMax in the MiniMax family. It supports a context window of up to 204,800 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.

Phi 3.5 Mini Instruct

Microsoft · 3.8B · runs from 2.3 GB

Phi 3.5 Mini 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.

DeepSeek R1 0528 Qwen3 8B

DeepSeek · 8.2B · runs from 2.9 GB

DeepSeek R1 0528 Qwen3 8B is a 8.2B-parameter open language model from DeepSeek in the DeepSeek R1 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.

Jan v3 4B Base Instruct

janhq · 4.4B · runs from 2.0 GB

Jan v3 4B Base Instruct is a 4.4B-parameter open language model from janhq. 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.

Meta Llama 3 8B Instruct

Meta · 8.0B · runs from 2.6 GB

Meta Llama 3 8B Instruct is the instruction-tuned version of Meta's Llama 3 8B base model, with 8 billion parameters. It is fine-tuned for dialogue and chat use cases using supervised fine-tuning and RLHF, making it ready for conversational applications out of the box. The model supports an 8K token context window and performs well across coding, reasoning, and general knowledge tasks. Its efficient size makes it one of the most popular models for local inference on consumer hardware. Released under the Meta Llama 3 Community License.

Qwen2.5 3B Instruct

Alibaba · 3.1B · runs from 1.3 GB

Qwen2.5 3B Instruct is a 3.1-billion parameter instruction-tuned model from Alibaba Cloud's Qwen 2.5 family. It is designed for efficient local inference on consumer hardware, supporting a 128K token context window despite its compact footprint. The model can run on GPUs with as little as 4GB of VRAM when quantized. Despite its small size, Qwen2.5 3B Instruct delivers competitive performance for basic conversational tasks, summarization, and simple instruction following. It is a good option for edge deployment and resource-constrained environments. Released under the Apache 2.0 license.

Granite 4.1 3B

IBM · 3.4B · runs from 1.6 GB

Granite 4.1 3B 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 32B Instruct

Alibaba · 32.8B · runs from 9.8 GB

Qwen2.5 Coder 32B Instruct is a 32.8-billion parameter code-specialized model from Alibaba Cloud, instruction-tuned for programming assistance and code generation. It is trained on a large corpus of source code alongside natural language data, making it highly capable for tasks such as code completion, debugging, code explanation, and software engineering dialogue. The model supports a 128K token context window and delivers code generation quality competitive with the best open-weight coding models at any scale. It requires a GPU with at least 24GB of VRAM for quantized inference. Released under the Apache 2.0 license.

Gemma 3 4B IT

Google · 4.3B · runs from 1.3 GB

Gemma 3 4B IT is a 4.3B-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 4 E2B IT Qat Q4 0 Unquantized

Google · 5.1B · runs from 2.5 GB

Gemma 4 E2B IT Qat Q4 0 Unquantized is a 5.1B-parameter open language model from Google in the Gemma 4 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.

Mistral 7B Instruct v0.2

Mistral AI · 7.2B · runs from 3.6 GB

Mistral 7B Instruct v0.2 is a 7.2B-parameter open language model from Mistral AI in the Mistral 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 Mini Instruct

Microsoft · 3.8B · runs from 2.2 GB

Microsoft Phi 4 Mini Instruct is a 3.8-billion parameter instruction-tuned model from Microsoft Research's Phi 4 family. It applies the Phi series' data-centric training philosophy to a compact model, delivering strong performance in coding, reasoning, and chat tasks relative to its small footprint. The model runs on consumer GPUs with as little as 4-6GB of VRAM when quantized, making it accessible on mainstream and even entry-level hardware. Released under the MIT license.

Qwen2.5 Coder 14B Instruct

Alibaba · 14.8B · runs from 5.1 GB

Qwen2.5 Coder 14B Instruct is a 14.8B-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.

Qwen2.5 0.5B Instruct

Alibaba · 494M · runs from 0.5 GB

Qwen2.5 0.5B Instruct is the smallest instruction-tuned model in Alibaba Cloud's Qwen 2.5 family, with just 494 million parameters. It is designed for ultra-lightweight deployment scenarios where minimal hardware resources are available, running comfortably on virtually any modern GPU or even CPU-only configurations. Despite its tiny footprint, the model supports a 128K token context window and can handle basic chat, simple summarization, and lightweight instruction following. It is primarily useful for edge deployment, experimentation, and prototyping where model size is a critical constraint. Released under the Apache 2.0 license.

DeepSeek R1 0528

DeepSeek · 684.5B · runs from 192.1 GB

DeepSeek R1 0528 is an updated release of the R1 reasoning model, incorporating improvements to training and inference that sharpen its performance on complex multi-step problems. It retains the same 684.5 billion parameter mixture-of-experts architecture as the original R1, with approximately 37 billion parameters active per forward pass. This revision addresses several edge cases where the original R1 struggled, delivering more consistent reasoning chains and fewer hallucinations on difficult math and coding tasks. Hardware requirements remain identical to the original R1, so users already set up to run the first version can swap in the 0528 weights with no changes to their infrastructure.

Qwen3 1.7B

Alibaba · 2.0B · runs from 1.1 GB

Qwen3 1.7B is a 1.7-billion parameter instruction-tuned model from Alibaba Cloud's Qwen 3 series. It is a lightweight model designed for deployment on minimal hardware, including low-VRAM GPUs and even CPU-only configurations with acceptable latency. Despite its compact size, it supports hybrid thinking mode and handles basic conversational tasks, simple question answering, and text generation. The model is useful for edge deployment, embedded applications, and scenarios where fast inference with minimal resource consumption is the priority. It represents a significant quality improvement over Qwen 2.5 at the sub-2B scale. Released under the Apache 2.0 license.

Qwen3 4B Instruct 2507

Alibaba · 4.0B · runs from 1.6 GB

Qwen3 4B Instruct 2507 is a July 2025 refresh of Alibaba's compact 4-billion-parameter chat model from the Qwen3 family. This updated release brings improved instruction following and conversational quality while remaining lightweight enough to run on most modern GPUs and even some higher-end integrated graphics setups. With its modest size, the 4B Instruct 2507 strikes a practical balance between capability and resource efficiency. It is well suited for everyday chat, summarization, and light assistant tasks on consumer hardware, making it one of the more accessible entry points into the Qwen3 lineup.