All LLM Models
Browse 107 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
CodeQwen1.5 7B
Alibaba · 7.3B · runs from 3.5 GB
1.6K
105CodeQwen1.5-7B is Alibaba's 7.3-billion-parameter code-specialized base language model, built on the Qwen1.5 architecture and pretrained on 3 trillion tokens of code data covering 92 programming languages. It uses group-query attention for efficient inference and is a raw pretrained model, not tuned for chat; Alibaba advises against using it directly for conversation and instead for fine-tuning, code infilling, and code generation. The card highlights strong text-to-SQL and bug-fixing performance for its size. At 7B parameters it fits comfortably on a single consumer GPU, even less once quantized. Context length is 65,536 tokens. It is released under a custom Tongyi Qianwen Research License, free for academic research, with commercial use requiring direct contact with Alibaba. It was published in April 2024.
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.
Qwen 1 8B
Alibaba · 1.8B · runs from 0.9 GB
Qwen-1.8B is Alibaba's first-generation 1.8-billion-parameter base language model, pretrained from scratch on over 2.2 trillion tokens of Chinese, English, multilingual, code, and math data, with the same roughly 150,000-token vocabulary used across the Qwen family. It is a raw pretrained model rather than a chat assistant; Alibaba's aligned Qwen-1.8B-Chat is built on top of it. Its main selling point is low-cost deployment: the card reports int4/int8 quantized versions needing under 2GB of memory for inference and as little as 6GB for fine-tuning, so it runs comfortably on almost any consumer GPU or even a laptop. Context length is 8,192 tokens. It is released under a custom Tongyi Qianwen Research License, free for academic research, with commercial use requiring direct contact with Alibaba. It was published in November 2023.
Qwen 14B
Alibaba · 14.2B · runs from 6.6 GB
Qwen-14B is Alibaba's first-generation 14-billion-parameter base language model, pretrained from scratch on over 3 trillion tokens of Chinese, English, multilingual, code, and math data, with a roughly 150,000-token vocabulary for broader multilingual coverage. It is a raw pretrained Transformer, not tuned for conversation; Alibaba's aligned Qwen-14B-Chat is the assistant built on top of it. The card reports it beating other open models of similar size, and in some benchmarks larger models too, on Chinese and English evaluation suites. At 14B parameters it needs a capable consumer GPU at full precision, considerably less once quantized. Context length is 8,192 tokens, extendable further with the NTK-aware interpolation and window-attention techniques described in the card. It is released under a custom Tongyi Qianwen License Agreement that is free for research, with commercial use requiring a separate application to Alibaba. It was published in September 2023.
Qwen3.5 4B
Alibaba · 4.7B · runs from 2.5 GB
Qwen3.5-4B is Alibaba's dense 4-billion-parameter vision-language model from the Qwen3.5 generation, sharing the same hybrid Gated DeltaNet and gated-attention architecture as its larger siblings. This is the post-trained, instruction-tuned release, able to process images together with text and handle general chat, coding assistance, and agentic tool use. It has a 262,144-token native context window, extensible up to roughly 1,010,000 tokens, and is released under the Apache 2.0 license. At around 4.7 billion parameters, 4-bit quantization needs under 3GB of memory, so it runs easily on almost any modern GPU or laptop.
Qwen3.5 35B A3B
Alibaba · 36.0B · runs from 15.7 GB
Qwen3.5 35B A3B is Alibaba's mixture-of-experts vision-language model in the Qwen3.5 line, totaling 36 billion parameters with about 3 billion active per token (the A3B in its name). It accepts text, images, and video as input, using a hybrid linear-attention and sparse-MoE architecture built for efficiency. Only the active parameters compute per token, so generation is faster than a same-sized dense model, though all 36 billion weights must fit in memory, calling for a high-end 24-32GB-class GPU once quantized. The model offers a 262K token context window for long documents or multi-turn chats. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in February 2026 as part of Alibaba's push toward efficient multimodal MoE models.
Qwen2.5 Coder 7B
Alibaba · 7.6B · runs from 3.6 GB
Qwen2.5 Coder 7B is a 7.6-billion parameter code-specialized base (pretrained) model from Alibaba Cloud's Qwen 2.5 Coder series. It is trained on a large dataset of source code and natural language but is not instruction-tuned, making it suitable for fine-tuning, code-related research, and custom downstream applications. The model supports a 128K token context window and runs efficiently on consumer GPUs. It serves as the foundation for the Qwen2.5 Coder 7B Instruct variant and community fine-tunes targeting specific programming languages or workflows. Released under the Apache 2.0 license.
Qwen1.5 MoE A2.7B
Alibaba · 14.3B · runs from 6.8 GB
Qwen1.5 MoE A2.7B is a Mixture of Experts (MoE) model from Alibaba Cloud's Qwen 1.5 generation, with 14.3 billion total parameters but only 2.7 billion active parameters per forward pass. The MoE architecture allows it to deliver performance closer to dense 7B models while requiring less compute during inference, as only a subset of expert layers are activated for each token. The model supports a 32K token context window and requires VRAM proportional to its total parameter count for loading, despite lower compute cost per token. It is an interesting architectural variant for users exploring efficient inference and MoE models locally. Released under a custom Qwen license.
Qwen1.5 0.5B
Alibaba · 620M · runs from 0.8 GB
Qwen1.5 0.5B is a 620M-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 7B Chat
Alibaba · 7.7B · runs from 3.6 GB
Qwen 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.
Qwen2.5 32B
Alibaba · 32.8B · runs from 14.8 GB
Qwen2.5 32B is a 32.8B-parameter open language model from Alibaba in the Qwen 2.5 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 0.5B
Alibaba · 494M · runs from 0.5 GB
Qwen2.5 Coder 0.5B is a 494-million parameter code-specialized model from Alibaba Cloud, the smallest in the Qwen 2.5 Coder series. It is designed for ultra-lightweight deployment where code-aware text generation is needed with minimal hardware resources. The model runs on virtually any GPU and even on CPU-only setups. While limited in capability compared to larger coding models, it is useful for basic code completion, prototyping, and experimentation. It supports a 128K token context window. 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.
Qwen1.5 MoE A2.7B Chat
Alibaba · 14.3B · runs from 6.8 GB
Qwen1.5 MoE A2.7B Chat is a 14.3B-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.
Qwen1.5 1.8B
Alibaba · 1.8B · runs from 1.5 GB
Qwen1.5 1.8B is a 1.8B-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.
Qwen1.5 4B
Alibaba · 4.0B · runs from 2.8 GB
Qwen1.5 4B is a 4.0B-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.
WebWorld 8B
Alibaba · 8.2B · runs from 4.1 GB
WebWorld 8B is a 8.2B-parameter open language model from Alibaba. 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.