All LLM Models

Browse 136 LLM models with VRAM requirements, quantization options, and hardware compatibility.

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Qwen2 VL 72B Instruct

Alibaba · 73.4B · runs from 49.7 GB at Q4

Qwen2-VL-72B-Instruct is Alibaba's 73-billion-parameter instruction-tuned vision-language model, built by fine-tuning Qwen2-VL-72B. The card shows state-of-the-art results on benchmarks such as MathVista, DocVQA, RealWorldQA and MTVQA; it handles videos over 20 minutes long, recognizes multilingual text within images, and acts as an agent to control phones or robots. It added naive dynamic resolution, mapping images of any size to a variable number of visual tokens. A model this large requires multi-GPU setups or big unified-memory machines even when quantized. The context window holds 32,768 tokens. Released under the Tongyi Qianwen license instead of Apache 2.0, you should review its terms before commercial use. Published in September 2024, it has since been superseded by Qwen2.5-VL-72B-Instruct, which the card lists as the newer version.

Qwen2 7B

Alibaba · 7.6B · runs from 5.8 GB at Q4

Qwen2 7B is a 7.6B-parameter open language model from Alibaba in the Qwen 2 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.

QwQ 32B Preview

Alibaba · 32.8B · runs from 24.3 GB at Q4

QwQ 32B Preview is a 32.8B-parameter open language model from Alibaba in the QwQ 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 1.5B

Alibaba · 1.5B · runs from 1.7 GB at Q4

Qwen2.5-1.5B is Alibaba's 1.5-billion-parameter entry in the Qwen2.5 family, released as a base pretrained language model rather than an instruction-tuned chat model. Qwen2.5 improved knowledge, coding, and math capabilities over its predecessor through an expanded pretraining corpus, but this checkpoint is the raw base: the model card explicitly discourages using it directly for conversation and recommends fine-tuning (SFT, RLHF, or continued pretraining) first. At this size, it runs comfortably on almost any modern GPU, or even a CPU, without quantization. The model supports a 131,072 token context window and is released under the Apache 2.0 license, allowing unrestricted commercial and research use. It was published in September 2024 alongside the rest of the Qwen2.5 line, which spans from 0.5B to 72B parameters and adds multilingual support for over 29 languages.

Qwen3.5 4B Base

Alibaba · 4.7B · runs from 3.4 GB at Q4

Qwen3.5-4B-Base is a 4.7-billion-parameter dense checkpoint in Alibaba's Qwen3.5 base family, a native vision-language foundation model trained with early fusion of image and text tokens rather than a text model with a bolted-on vision tower. It is pretrained-only weights meant for fine-tuning or research, not direct conversation, though its control tokens support efficient LoRA-style adaptation with the official chat template. It uses the same hybrid Gated DeltaNet plus gated-attention architecture as its siblings. At under 5B parameters, it runs comfortably on a single mainstream consumer GPU. 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. It sits mid-lineup among five Qwen3.5-Base sizes, between the 2B and 9B dense checkpoints, with a 35B mixture-of-experts variant at the top.

Qwen2.5 7B

Alibaba · 7.6B · runs from 5.8 GB at Q4

Qwen2.5-7B is Alibaba's 7.6-billion-parameter base pretrained model in the Qwen2.5 series, one step up from the 1.5B checkpoint. Like its smaller sibling it is a raw causal language model rather than an instruction-tuned chat model: the card recommends applying supervised fine-tuning, RLHF, or further pretraining before using it conversationally. It uses a dense transformer with grouped-query attention, and at 7.6B parameters it fits comfortably on a single consumer GPU once quantized, or unquantized on a higher-memory card. Context length is 131,072 tokens, one of the longer windows in the 0.5B-to-72B Qwen2.5 lineup. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in September 2024. Qwen2.5 as a whole brought major gains in coding, math, and structured-data understanding over Qwen2.

Qwen2.5 0.5B

Alibaba · 494M · runs from 0.8 GB at Q4

Qwen2.5 0.5B is the smallest base (pretrained) model in Alibaba Cloud's Qwen 2.5 family, with 494 million parameters. As a base model, it is not instruction-tuned and is intended for fine-tuning, research, and as a foundation for custom applications. It supports a 128K token context window. Its minimal size makes it suitable for experimentation, rapid prototyping, and resource-constrained fine-tuning tasks. The model can run on virtually any hardware. Released under the Apache 2.0 license.

Qwen1.5 110B Chat

Alibaba · 111.2B · runs from 72.4 GB at Q4

Qwen1.5-110B-Chat is Alibaba Cloud's 111-billion-parameter dense chat model and the largest member of the Qwen1.5 series, which the team describes as a beta version of Qwen2 spanning nine sizes from 0.5B up to this 110B model plus a 14B mixture-of-experts variant. Unlike most other Qwen1.5 sizes, the 110B model uses grouped-query attention, and it was post-trained with supervised fine-tuning and direct preference optimization for improved chat alignment. At 111 billion parameters it needs a multi-GPU workstation to run even when quantized. Context length is 32,768 tokens, one of Qwen1.5's standout features since every size in the family shares a stable 32K window. It is released under the Tongyi Qianwen License Agreement, Alibaba's custom license permitting research and commercial use. It was published in April 2024, a beta step between the original Qwen and Qwen2.

Qwen3 VL 235B A22B Thinking

Alibaba · 235.7B · runs from 143.3 GB at Q4

Qwen3-VL-235B-A22B-Thinking is the reasoning-enhanced edition of Alibaba's Qwen3-VL vision-language family, a mixture-of-experts model with roughly 22.8 billion active parameters out of about 235.7 billion total, that reasons step by step before answering on both text and visual inputs. Compared to the Instruct edition, Thinking emphasizes deeper multimodal reasoning for STEM, math, and causal analysis. The model can operate PC and mobile GUIs as a visual agent, generate code from images or video, judge spatial relationships and 3D layouts, and read OCR text in 32 languages. Given its scale, it needs a multi-GPU server to run even quantized. Context length is 262,144 tokens natively, expandable to 1,048,576 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in September 2025.

VisionFunctions

Qwen2 72B Instruct

Alibaba · 72.7B · runs from 49.3 GB at Q4

Qwen2-72B-Instruct is the instruction-tuned, chat-ready 72.7-billion-parameter model from Alibaba's Qwen2 series, built on a Transformer architecture with SwiGLU activation, QKV attention bias, and grouped-query attention, and trained with supervised fine-tuning plus direct preference optimization. It generally surpassed the prior Qwen1.5 line and competed with proprietary models on language understanding, generation, multilingual tasks, coding, and math benchmarks at release, though it has since been superseded by Qwen2.5-72B-Instruct. At 72.7 billion parameters, it needs a multi-GPU workstation to run in full precision. Context length is natively 32,768 tokens, extendable to 131,072 tokens using YaRN scaling, as documented on the model card. It is released under Alibaba's Tongyi Qianwen custom license, which permits commercial use but requires a separate license once a deployment passes 100 million monthly active users. It was published in May 2024.

Qwen2 0.5B

Alibaba · 494M · runs from 0.8 GB at Q4

Qwen2-0.5B is Alibaba's roughly 494-million-parameter dense base language model, the smallest member of the Qwen2 family that scales up to 72B and includes one Mixture-of-Experts variant. This is a pretrained base model, not an instruction-tuned chat model; Alibaba explicitly recommends applying supervised fine-tuning, RLHF, or further pretraining before using it for open-ended generation. Given its size, it runs easily on nearly any GPU and even on CPUs. Its configuration specifies a context window of up to 131,072 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in May 2024. It was superseded within the same year by Qwen2.5-0.5B, part of Alibaba's fast release cadence for its small-model line.

Qwen1.5 0.5B Chat

Alibaba · 620M · runs from 2.3 GB at Q4

Qwen1.5 0.5B Chat is an early-generation small language model from Alibaba's Qwen series with just 620 million parameters. As one of the smallest models in the Qwen family, it was designed to demonstrate that useful conversational ability is possible even at sub-billion parameter scales. This model runs easily on virtually any hardware including CPUs, older GPUs, and even mobile devices. While its capabilities are limited compared to larger Qwen models, it remains a useful option for embedded applications, rapid prototyping, or situations where minimal resource consumption is the top priority.

Qwen3Guard Gen 8B

Alibaba · 8.2B · runs from 7.6 GB at Q4

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.

Qwen 7B Chat

Alibaba · 7.7B · runs from 7.6 GB at Q4

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.

Qwen3Guard Gen 0.6B

Alibaba · 752M · runs from 1.7 GB at Q4

Qwen3Guard Gen 0.6B is a 752M-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.

Qwen2 1.5B

Alibaba · 1.5B · runs from 1.7 GB at Q4

Qwen2 1.5B is a 1.5-billion parameter base (pretrained) model from Alibaba Cloud's older Qwen 2 generation. It was trained on a multilingual corpus and supports a context window of up to 32K tokens. As a base model, it is designed for fine-tuning and research rather than direct conversational use. While superseded by the Qwen 2.5 series in terms of training data quality and benchmark performance, Qwen2 1.5B remains a lightweight option for experimentation and as a baseline for comparison. Released under the Apache 2.0 license.

Qwen 14B Chat

Alibaba · 14.2B · runs from 10.6 GB at Q4

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.

Qwen1.5 32B

Alibaba · 32.5B · runs from 24.1 GB at Q4

Qwen1.5-32B is Alibaba's 32.5-billion-parameter dense base language model, one of eight sizes (0.5B to 72B, plus a 14B mixture-of-experts variant) in the Qwen1.5 series, a beta preview of what became Qwen2. It is a raw pretrained Transformer with SwiGLU activation, QKV attention bias, and group-query attention (added specifically for the 32B and larger sizes), and Alibaba does not recommend using it directly for chat, only as a foundation for further fine-tuning or alignment. At 32.5B parameters it needs a high-end consumer GPU or multi-GPU setup once quantized, more at full precision. Context length is 32,768 tokens, stable across all Qwen1.5 model sizes. It is released under a custom Tongyi Qianwen Research License, free for research and academic use, with commercial deployment requiring a separate license from Alibaba. It was published in April 2024.

Qwen1.5 14B Chat

Alibaba · 14.2B · runs from 22.2 GB at Q4

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.

Qwen1.5 1.8B Chat

Alibaba · 1.8B · runs from 4.6 GB at Q4

Qwen1.5 1.8B Chat 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.

Qwen 1 8B

Alibaba · 1.8B · runs from 1.9 GB at Q4

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.

Qwen1.5 7B

Alibaba · 7.7B · runs from 13.5 GB at Q4

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 5.7 GB at Q4

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.

Qwen2.5 Math 1.5B

Alibaba · 1.5B · runs from 1.3 GB at Q4

Qwen2.5 Math 1.5B is a 1.5B-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.

ChatMathFunctions

Qwen 72B

Alibaba · 72.3B · runs from 52.5 GB at Q4

Qwen-72B is Alibaba's 72-billion-parameter dense base language model from the original Qwen series, pretrained on over 3 trillion tokens of web text, books, and code. This is the pretrained base checkpoint, not a chat model; Alibaba released it for further fine-tuning and separately published Qwen-72B-Chat as the instruction-tuned counterpart. At this scale, local inference realistically needs a high-end multi-GPU workstation or heavy quantization onto a single very high-VRAM card. The model supports a 32,768 token context window. It is released under Alibaba's Tongyi Qianwen License Agreement, a custom license rather than a standard open-source one, and was published in November 2023. It uses an unusually large, roughly 150,000-token vocabulary for multilingual coverage, and predates and was superseded by the Qwen1.5 and later Qwen2/2.5 generations.

Qwen3Guard Gen 4B

Alibaba · 4.4B · runs from 4.5 GB at Q4

Qwen3Guard Gen 4B is a 4.4B-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 7B Chat

Alibaba · 7.7B · runs from 13.5 GB at Q4

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 14B

Alibaba · 14.2B · runs from 10.6 GB at Q4

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.

Qwen1.5 110B

Alibaba · 111.2B · runs from 72.4 GB at Q4

Qwen1.5-110B is Alibaba's largest dense base model in the Qwen1.5 series, a pretrained language model that is not instruction-tuned, released as a beta preview ahead of Qwen2. Like the rest of the Qwen1.5 line it uses a SwiGLU Transformer with an improved multilingual tokenizer, and at 110B it is one of only two sizes in the series, alongside 32B, to include grouped-query attention. A matching aligned chat model was released alongside it. At 111 billion parameters it requires a multi-GPU workstation or server to run, even 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 April 2024; Qwen1.5 has since been superseded by the Qwen2, Qwen2.5, and Qwen3 series.

Qwen 7B

Alibaba · 7.7B · runs from 7.6 GB at Q4

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.

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.