All LLM Models
Browse 112 LLM models with VRAM requirements, quantization options, and hardware compatibility.
Model List
Qwen2.5 VL 32B Instruct
Alibaba · 33.5B · runs from 24.7 GB at Q4
714.8K
510Qwen2.5 VL 32B Instruct is a 33.5-billion-parameter vision-language model from Alibaba's Qwen team, able to process images and text together for document parsing, chart reading, and visual question answering. It was tuned with reinforcement learning on top of the original Qwen2.5-VL release for more detailed, better-formatted answers and sharper accuracy on math and visual-logic problems. At this size, local inference needs quantization and a single high-end 24-32GB-class consumer or workstation GPU rather than lower-end hardware. The model supports a 128K token context window, enough for lengthy documents or multi-image inputs. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use, and was published in March 2025 as a mid-sized addition to the Qwen2.5-VL lineup, between the smaller 7B model and the flagship 72B version.
Qwen3.5 0.8B
Alibaba · 873M · runs from 0.9 GB at Q4
Qwen3.5-0.8B is the smallest model in Alibaba's Qwen3.5 generation, a dense sub-billion-parameter vision-language model using the same hybrid Gated DeltaNet and gated-attention design as the rest of the family. It is a post-trained, instruction-tuned release, and its publisher notes that at this scale it is best suited to prototyping, task-specific fine-tuning, and other research or development purposes rather than production chat. It supports a 262,144-token native context window and is released under the Apache 2.0 license. At under one billion parameters, it needs well under a gigabyte of memory at 4-bit, so it runs on essentially any modern laptop or GPU.
Qwen3 VL 30B A3B Instruct
Alibaba · 31.1B · runs from 19.7 GB at Q4
Qwen3 VL 30B A3B Instruct is Alibaba's 31-billion-parameter mixture-of-experts vision-language model in the Qwen 3 lineup, with about 3 billion parameters active per token (the A3B in its name). Because only the active experts run for each token, inference is faster than a similarly sized dense model, while all the weights still need to fit in memory. It handles images and video alongside text, supporting visual question answering, document and chart reading, and multi-image reasoning. At this size, local inference calls for quantization and a single high-end consumer GPU. The model supports a 262K token context window, suited to long documents, video transcripts, or extended visual conversations. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use, and was published in September 2025 as part of Qwen's third-generation vision-language family.
Qwen3 VL 32B Instruct
Alibaba · 33.4B · runs from 23 GB at Q4
Qwen3 VL 32B Instruct is Alibaba's largest dense model in the initial Qwen3-VL lineup, a 33.4-billion-parameter vision-language model built to process images and text in one pass. It handles image description, visual question answering, and document understanding, and its visual-agent tuning lets it read GUI screenshots and reason about on-screen elements. Local inference calls for quantization and a capable GPU, fitting on a single high-end consumer or workstation card. The model supports a 262,144 token context window, enough for long documents or extended multi-turn conversations. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use. Published in October 2025 alongside the 2B and 8B models, it shares the family's long native context and video-understanding capabilities, giving stronger multimodal reasoning than the smaller variants.
Qwen3 30B A3B
Alibaba · 30.5B · runs from 19.4 GB at Q4
Qwen3 30B A3B is a Mixture of Experts (MoE) model from Alibaba Cloud's Qwen 3 series, with 30 billion total parameters and approximately 3 billion active parameters per forward pass. The MoE architecture delivers quality significantly above what a standard 3B dense model could achieve, while keeping per-token compute costs low. It supports hybrid thinking mode for flexible reasoning. The model requires VRAM proportional to its full 30B parameter count for weight loading, but its low active parameter count results in fast inference throughput. It is an efficient option for users who want quality beyond dense small models without the full cost of larger architectures. Released under the Apache 2.0 license.
Qwen3 VL 2B Instruct
Alibaba · 2.1B · runs from 3.5 GB at Q4
Qwen3 VL 2B Instruct is Alibaba's smallest model in the Qwen3-VL lineup, a 2.1-billion-parameter vision-language model built to handle images and text together. It performs image captioning, visual question answering, and document reading, and its visual-agent tuning lets it interpret GUI screenshots for simple automation. Its small size suits on-device and edge deployment, running comfortably on modest consumer GPUs or even some laptops once quantized. The model supports a 262,144 token context window, enough for lengthy documents or multi-turn conversations. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use. Published in October 2025 alongside the 8B and 32B models, it targets edge and mobile use cases, trading some visual reasoning depth for a much smaller footprint.
Qwen2.5 Coder 1.5B Instruct
Alibaba · 1.5B · runs from 1.7 GB at Q4
Qwen2.5 Coder 1.5B Instruct is a 1.5B-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 Coder 3B Instruct
Alibaba · 3.1B · runs from 2.8 GB at Q4
Qwen2.5 Coder 3B Instruct 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.
Qwen2.5 Omni 7B
Alibaba · 10.7B · runs from 9.9 GB at Q4
Qwen2.5-Omni-7B is Alibaba's 10.7-billion-parameter flagship in the Qwen2.5-Omni family, an end-to-end model that perceives text, images, audio, and video and generates both text and natural streaming speech in response. It shares the family's Thinker-Talker design and TMRoPE positional scheme for synchronizing audio and video timestamps, tuned for low-latency, real-time conversation rather than turn-based chat alone. At just under 11 billion parameters, it needs a single mainstream-to-high-end consumer GPU once quantized. Its language backbone carries a 32,768 token context window. It is released under Alibaba's Qwen Research License, a non-commercial license limited to research and evaluation rather than the Apache 2.0 used for Qwen's text-only models. Published in March 2025, it was the first Omni release, with the smaller 3B variant following about a month later.
Qwen AgentWorld 35B A3B
Alibaba · 34.7B · runs from 21.3 GB at Q4
Qwen AgentWorld 35B A3B is Alibaba's 35-billion-parameter mixture-of-experts model, with about 3 billion parameters active per token (the A3B in its name). Unlike a typical chat model, it is a language world model: given an agent's action and history, it predicts what the environment does next, covering domains such as terminal, web, Android, and software engineering. Only the active experts run per token, so inference stays fast even though all the weights must fit in memory. At this size, local inference calls for quantization and a single high-end consumer GPU. The model supports a 262K token context window and is released under the Apache 2.0 license, allowing unrestricted commercial and research use. Published in June 2026, it is built on a Qwen3.5-35B-A3B base and is meant for simulating agents, not direct conversation.
Qwen2 VL 2B Instruct
Alibaba · 2.2B · runs from 2.1 GB at Q4
Qwen2 VL 2B Instruct is a 2.2-billion-parameter vision-language model from Alibaba's Qwen2-VL series, able to process images, multi-image comparisons, and video alongside text prompts. It targets visual question answering, document and chart reading, and basic agentic tasks such as interpreting a screenshot to plan a next action. Its small size makes it well suited to laptops and even some phones, running comfortably on modest consumer hardware once quantized. The model supports a 32K token context window, enough for moderate documents or extended chat. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use, and was published in August 2024. Qwen2-VL introduced Naive Dynamic Resolution and Multimodal Rotary Position Embedding, letting it handle arbitrary image resolutions and understand videos well over twenty minutes long.
Qwen3 4B Thinking 2507
Alibaba · 4.0B · runs from 4.2 GB at Q4
Qwen3 4B Thinking 2507 is the reasoning-optimized variant of Alibaba's compact 4-billion-parameter Qwen3 model, released in the July 2025 update cycle. Despite its small size, this thinking variant is tuned to produce chain-of-thought reasoning and step-by-step problem solving, making it a surprisingly capable lightweight reasoner. This model is ideal for users who want basic reasoning and analytical capabilities on very modest hardware. It can run on most consumer GPUs and even some CPU-only setups when quantized, providing an accessible entry point for experimenting with reasoning-style models without any significant hardware investment.
Qwen2.5 Omni 3B
Alibaba · 5.5B · runs from 6.0 GB at Q4
Qwen2.5-Omni-3B is Alibaba's 5.5-billion-parameter end-to-end omni-modal model, built to perceive text, images, audio, and video and respond with both text and natural streaming speech. It uses a Thinker-Talker architecture with a time-aligned position embedding (TMRoPE) that synchronizes video frames with audio, enabling real-time voice and video chat with chunked input and immediate output. As the smaller of the two Omni checkpoints, it runs on a single consumer GPU once quantized. Its language backbone supports a 32,768 token context window. It is released under Alibaba's Qwen Research License, which permits only non-commercial research and evaluation, unlike the Apache 2.0 used for Qwen's text-only models. Published in April 2025, about a month after the larger Qwen2.5-Omni-7B, it brings real-time multimodal chat to smaller-scale deployments.
QwQ 32B
Alibaba · 32.8B · runs from 24.3 GB at Q4
QwQ 32B is a 32-billion parameter reasoning-focused model from Alibaba Cloud's Qwen family. Unlike standard chat models, QwQ is specifically optimized for step-by-step logical reasoning, complex problem solving, and mathematical tasks. It employs extended chain-of-thought processing, generating detailed internal reasoning before producing final answers, which significantly improves accuracy on challenging analytical problems. The model requires a GPU with at least 24GB of VRAM for quantized inference and delivers reasoning performance competitive with much larger models. It is particularly well suited for users who need strong analytical capabilities for math, science, coding logic, and multi-step problem solving. Released under the Apache 2.0 license.
Qwen2.5 Coder 0.5B Instruct
Alibaba · 494M · runs from 0.8 GB at Q4
Qwen2.5 Coder 0.5B Instruct is a 494M-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.
Qwen3 Omni 30B A3B Thinking
Alibaba · 31.7B · runs from 24.7 GB at Q4
Qwen3-Omni-30B-A3B-Thinking is the reasoning-focused variant of Alibaba's Qwen3-Omni family, a mixture-of-experts model that accepts text, audio, image, and video input and reasons over it with explicit chain-of-thought before answering in text. Unlike the Instruct variant, it keeps only the "thinker" component and drops the speech-generating "talker" and audio codec, so it outputs text only, not speech. Its backbone still routes across 128 experts with 8 active per token, roughly 3 billion active out of about 31.7 billion total, and needs a high-end consumer GPU or multi-GPU setup once quantized. Context length is 65,536 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in September 2025, a few days ahead of the Instruct variant that adds real-time speech generation on top of the same reasoning core.
Qwen2 7B Instruct
Alibaba · 7.6B · runs from 5.8 GB at Q4
Qwen2 7B Instruct is a 7.6B-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.
Qwen3 30B A3B Thinking 2507
Alibaba · 30.5B · runs from 19.4 GB at Q4
Qwen3 30B A3B Thinking 2507 is the reasoning-focused variant of Alibaba's 30-billion-parameter mixture-of-experts model, updated in July 2025. Like its instruct sibling, it activates only about 3 billion parameters per token, keeping resource demands low while enabling multi-step reasoning and chain-of-thought problem solving. This thinking variant is designed for tasks that benefit from deliberate, step-by-step logic such as math, coding puzzles, and analytical questions. Its efficient MoE design means users with modest GPUs can still access strong reasoning capabilities without needing datacenter-class hardware.
Qwen2.5 14B
Alibaba · 14.8B · runs from 12.4 GB at Q4
Qwen2.5 14B is a 14.8B-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 Math 1.5B Instruct
Alibaba · 1.5B · runs from 1.3 GB at Q4
Qwen2.5 Math 1.5B Instruct 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.
Qwen3.5 9B Base
Alibaba · 9.7B · runs from 6.6 GB at Q4
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.
Qwen Image 2.1 PE T2I
Alibaba · 9.4B · runs from 6.5 GB at Q4
Qwen Image 2.1 PE T2I is a 9.4B-parameter open language model from Alibaba in the Qwen 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 Math 7B Instruct
Alibaba · 7.6B · runs from 5.1 GB at Q4
Qwen2.5 Math 7B Instruct 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.
Qwen2.5 7B Instruct 1M
Alibaba · 7.6B · runs from 5.8 GB at Q4
Qwen2.5 7B Instruct 1M is a 7.6B-parameter open language model from Alibaba in the Qwen 2.5 family. It supports a context window of up to 1,010,000 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Qwen3 VL 8B Thinking
Alibaba · 8.8B · runs from 8.0 GB at Q4
Qwen3 VL 8B Thinking is an 8.8-billion-parameter vision-language model from Alibaba's Qwen team, the reasoning-focused counterpart to the Qwen3-VL-8B Instruct model. It processes images, video, and text together and is tuned to work through problems step by step before answering, which tends to help on multi-step visual reasoning, STEM problems, and chart or document analysis. At under 9 billion parameters, it runs well on a single mainstream consumer GPU once quantized, making local multimodal use practical without server-class hardware. It supports a 262K token context window for long documents, multi-image input, or extended video. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use. Published in October 2025 alongside the non-thinking Instruct variant, it trades some response speed for deeper visual-language reasoning.
Qwen2.5 Coder 1.5B
Alibaba · 1.5B · runs from 1.7 GB at Q4
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.
Qwen3 VL 4B Thinking
Alibaba · 4.4B · runs from 4.5 GB at Q4
Qwen3 VL 4B Thinking is Alibaba's 4.4-billion-parameter vision-language model within the Qwen3-VL lineup: a reasoning-focused edition that processes images and text before responding. The card emphasizes visual agent skills for controlling PC and mobile GUIs, coding directly from images and video, improved spatial awareness, and OCR support covering 32 languages. At this scale, it runs smoothly on one modest consumer GPU after quantization. The model handles a 262,144 token context window; the card notes this is native at 256K and expandable up to 1M. Released under the Apache 2.0 license, it allows unrestricted commercial and research use. Published in October 2025, it comes alongside an Instruct variant of identical size and introduces Interleaved-MRoPE, DeepStack, and text-timestamp alignment to enhance video understanding.
Qwen3.5 0.8B Base
Alibaba · 873M · runs from 0.9 GB at Q4
Qwen3.5-0.8B-Base is Alibaba's smallest base checkpoint in the Qwen3.5 family, at 0.87 billion parameters, a native vision-language foundation model that fuses image and text tokens during pretraining rather than bolting a vision encoder onto a text-only model. Like the rest of the -Base line, it ships as pretrained-only weights for fine-tuning and research, not direct interaction, though its control tokens support efficient LoRA-style adaptation with the official chat template. Its hybrid architecture pairs Gated DeltaNet linear attention with periodic full attention layers. It runs easily on a single modest consumer GPU, even unquantized. Context length is 262,144 tokens natively, extensible up to 1,010,000 tokens. It is released under the Apache 2.0 license, and was published in February 2026 as the smallest of five Qwen3.5-Base sizes, from 0.8B dense up to a 35B mixture-of-experts model.
Qwen3 VL 2B Thinking
Alibaba · 2.1B · runs from 3.5 GB at Q4
Qwen3-VL-2B-Thinking is Alibaba's 2.1-billion-parameter vision-language model, the reasoning-enhanced "Thinking" edition of the smallest Qwen3-VL checkpoint, alongside a matching Instruct edition. Beyond image description and visual question answering, it acts as a visual agent capable of operating PC and mobile GUIs, generating code from screenshots or diagrams, and reasoning about 2D and 3D spatial relationships. Its OCR pipeline covers 32 languages and is tuned for low light, blur, and tilted text. At just over 2 billion parameters, it runs easily on a single modest consumer GPU, even unquantized. The model natively supports a 262,144 token context window, expandable to 1,010,000 tokens for long documents or hours of video. It is released under the Apache 2.0 license, and was published in October 2025. Compared with Qwen2.5-VL, Qwen3-VL adds DeepStack vision-feature fusion and timestamp-grounded video event localization.
Qwen2 0.5B Instruct
Alibaba · 494M · runs from 0.8 GB at Q4
Qwen2 0.5B Instruct is a 494M-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.
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.