All LLM Models

Browse 116 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.

Qwen3.5 9B Base

Alibaba · 9.7B · runs from 4.7 GB

Qwen3.5-9B-Base is the largest dense checkpoint in Alibaba's Qwen3.5 base family, at 9.7 billion parameters, a native vision-language foundation model trained with early fusion of image and text tokens. It is pretrain-only, meant for fine-tuning or research rather than direct interaction, though its control tokens are compatible with the official chat template for LoRA-style adaptation. It shares the family's hybrid design, alternating Gated DeltaNet linear attention with periodic gated full-attention layers, at a larger hidden size than its smaller siblings. At just under 10B parameters, it needs a single mainstream-to-high-end consumer GPU once quantized. Context length is a native 262,144 tokens, extensible up to 1,010,000 tokens. It is released under the Apache 2.0 license, and was published in February 2026. Above it sits only the 35B mixture-of-experts Qwen3.5-Base model, which activates roughly 3 billion parameters per token.

Qwen1.5 14B

Alibaba · 14.2B · runs from 8 GB

Qwen1.5-14B is Alibaba's base pretrained language model, not instruction-tuned, from the Qwen1.5 series that served as a beta preview of Qwen2. It is a dense Transformer with SwiGLU activations, QKV bias, and an improved multilingual tokenizer, released alongside a matching aligned chat model. The Qwen1.5 line spans eight sizes from 0.5B to 72B dense models plus a 14B-class Mixture-of-Experts model, all with stable 32K context support. At 14 billion parameters it fits comfortably on a single consumer GPU once quantized. Context length is 32,768 tokens. It is released under Alibaba's custom Tongyi Qianwen license, which permits commercial use below 100 million monthly active users. It was published in January 2024; Qwen1.5 has since been superseded by the Qwen2, Qwen2.5, and Qwen3 series.

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.

Qwen 14B Chat

Alibaba · 14.2B · runs from 6.6 GB

Qwen-14B-Chat is Alibaba Cloud's 14-billion-parameter bilingual Chinese/English chat model, aligned from the pretrained Qwen-14B base using supervised alignment techniques on top of large-scale web text, books, and code pretraining data. It supports tool and API use through ReAct-style prompting, the same pattern used by LangChain agents, and can extend beyond its native context via optional NTK-aware interpolation and LogN attention scaling for long-document summarization. At 14 billion parameters it fits a single consumer GPU once quantized. Context length is 8,192 tokens natively. It is released under the Tongyi Qianwen License Agreement, Alibaba's custom license that allows research and commercial use, with a separate application required for larger-scale commercial deployment. It was published in September 2023, part of the original Qwen generation that Alibaba has since superseded with Qwen1.5, Qwen2, and Qwen3.

Qwen2.5 Coder 1.5B

Alibaba · 1.5B · runs from 1.0 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.

Qwen 7B

Alibaba · 7.7B · runs from 3.6 GB

Qwen-7B is Alibaba's original 7.7-billion-parameter base language model from the first Qwen series, pretrained (not instruction-tuned) on over 2.4 trillion tokens of Chinese, English, multilingual, code, and math text, with a roughly 150,000-token vocabulary aimed at multilingual friendliness. Alibaba positions it as a foundation for further fine-tuning; the aligned chat assistant Qwen-7B-Chat is a separate release built on top of it. Its native training context is 8,192 tokens, and the card documents NTK-aware interpolation, LogN attention scaling, and windowed attention to extrapolate usable context out to 32,768 tokens. At under 8 billion parameters, it fits on a single consumer GPU. Context length is 8,192 tokens natively, extendable to 32,768 tokens with the NTK, LogN, and window-attention techniques above. It is released under the Tongyi Qianwen License Agreement, which allows academic use freely but requires applying for a commercial license for commercial deployment. It was published in August 2023, an early entry in a Qwen lineage now several generations ahead.

Qwen2.5 Math 7B

Alibaba · 7.6B · runs from 3.6 GB

Qwen2.5 Math 7B is a 7.6B-parameter open language model from Alibaba in the Qwen 2.5 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

Alibaba · 7.7B · runs from 4.7 GB

Qwen1.5-7B is Alibaba's dense 7.7-billion-parameter base language model, part of the Qwen1.5 series that served as a beta step between the original Qwen and Qwen2. It is a decoder-only Transformer using SwiGLU activation, released as a pretrained checkpoint rather than a chat model; its card explicitly recommends applying further post-training such as SFT or RLHF rather than prompting it directly for text generation. It offers a stable 32K token context window and is distributed under Alibaba's Tongyi Qianwen license, a custom license rather than a fully open one. At 7.7 billion parameters, it runs comfortably on an 8-12 GB consumer GPU once quantized to 4-bit, making it an accessible option for local fine-tuning experiments.

CodeQwen1.5 7B

Alibaba · 7.3B · runs from 3.5 GB

CodeQwen1.5-7B 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.

Qwen2 57B A14B Instruct

Alibaba · 57.4B · runs from 24.8 GB

Qwen2 57B A14B Instruct is a 57.4B-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.

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.