All LLM Models
Browse 96 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
Qwen2.5 VL 3B Instruct
Alibaba · 3.8B · runs from 1.4 GB
2.4M
704Qwen2.5 VL 3B Instruct is Alibaba's 3.8-billion-parameter vision-language model in the Qwen 2.5 lineup, built to process images and text together in a single conversation. It can describe images, answer questions about visual content, read charts and documents, and locate objects within a scene, making it a compact option for on-device or edge multimodal applications. Its small size means it runs comfortably on modest consumer GPUs, and even on laptops or lower-end hardware once quantized, without requiring a workstation-class card. The model supports a 128K token context window, enough for lengthy documents or extended visual conversations. It was published in January 2025 alongside the larger Qwen2.5-VL models, sharing the same architecture and vision encoder scaled down for lighter-weight, latency-sensitive deployments.
Qwen2.5 Coder 3B Instruct
Alibaba · 3.1B · runs from 1.7 GB
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.
Qwen3 VL 32B Instruct
Alibaba · 33.4B · runs from 9.8 GB
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 VL 2B Instruct
Alibaba · 2.1B · runs from 1.1 GB
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.
Qwen3 30B A3B Thinking 2507
Alibaba · 30.5B · runs from 8.8 GB
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 Omni 7B
Alibaba · 10.7B · runs from 3.3 GB
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.
Qwen2.5 Math 7B Instruct
Alibaba · 7.6B · runs from 3.0 GB
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 VL 32B Instruct
Alibaba · 33.5B · runs from 10.0 GB
Qwen2.5 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.
Qwen2 VL 2B Instruct
Alibaba · 2.2B · runs from 1.1 GB
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.
Qwen2.5 Coder 0.5B Instruct
Alibaba · 494M · runs from 0.5 GB
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.
Qwen2.5 Math 1.5B Instruct
Alibaba · 1.5B · runs from 1 GB
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.
Qwen2.5 Omni 3B
Alibaba · 5.5B · runs from 1.7 GB
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.
Qwen3 VL 8B Thinking
Alibaba · 8.8B · runs from 3.0 GB
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 32B
Alibaba · 32.8B · runs from 9.8 GB
Qwen2.5-Coder-32B is Alibaba's 32.8-billion-parameter code-specialized base language model, the largest of six sizes in the Qwen2.5-Coder family (0.5B to 32B), continued-pretrained from Qwen2.5-32B on 5.5 trillion tokens of source code, text-code grounding data, and synthetic data. It is a raw pretrained model, not instruction-tuned, so it is meant as a foundation for building code assistants, agents, or chat models through further fine-tuning rather than for direct conversational use. Alibaba states its coding ability rivals GPT-4o, and at this size it needs a high-end consumer GPU or multi-GPU setup once quantized, more at full precision. Context length defaults to 32,768 tokens, extendable to 131,072 tokens with YaRN scaling. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in November 2024.
Qwen2 0.5B Instruct
Alibaba · 494M · runs from 0.5 GB
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.
Qwen2.5 7B Instruct 1M
Alibaba · 7.6B · runs from 3.0 GB
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.
Qwen2.5 3B
Alibaba · 3.1B · runs from 1.6 GB
Qwen2.5 3B 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.
Qwen3 VL 2B Thinking
Alibaba · 2.1B · runs from 1.1 GB
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 VL 7B Instruct
Alibaba · 8.3B · runs from 3.2 GB
Qwen2 VL 7B Instruct is Alibaba's 8.3-billion-parameter vision-language model from the original Qwen 2 generation, built to process images and video alongside text in a single conversation. It can describe images, answer visual questions, and reason over multi-image or video input, suiting it to multimodal chat and visual document tasks. At this parameter count, local inference is practical on a single mainstream or high-end consumer GPU once the weights are quantized, rather than requiring multi-GPU hardware. The model supports a 32K token context window, enough for moderate-length documents or multi-turn visual conversations. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use. Published in August 2024, it was later superseded by Qwen2.5-VL-7B-Instruct, which its own model card lists as its successor.
Qwen2.5 Coder 14B
Alibaba · 14.8B · runs from 5.1 GB
Qwen2.5 Coder 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 32,768 tokens. See its VRAM requirements by quantization and which GPUs and Macs can run it locally below.
Qwen2.5 Coder 3B
Alibaba · 3.1B · runs from 1.4 GB
Qwen2.5-Coder-3B is Alibaba's 3.1-billion-parameter base (pretrained, not instruction-tuned) code language model, one of six sizes in the Qwen2.5-Coder family built on Qwen2.5-3B. It was pretrained on 5.5 trillion tokens of source code, text-code grounding data, and synthetic data, and is intended as a foundation for further fine-tuning, reinforcement learning, or fill-in-the-middle code-completion tasks rather than direct chat use; Alibaba explicitly advises against using it for conversations as-is. The family's larger 32B model is described as matching GPT-4o-level coding ability, though this 3B checkpoint targets lightweight, resource-constrained deployment. At just over 3 billion parameters, it runs easily on a single modest consumer GPU or even a CPU. Context length is 32,768 tokens. It is released under the Qwen Research License, a non-commercial license restricted to research and evaluation use; commercial use requires a separate license from Alibaba Cloud. It was published in November 2024.
QwQ 32B Preview
Alibaba · 32.8B · runs from 10.7 GB
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 7B
Alibaba · 7.6B · runs from 3.6 GB
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 0.5B
Alibaba · 494M · runs from 0.5 GB
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.
Qwen3 0.6B Base
Alibaba · 596M · runs from 0.7 GB
Qwen3 0.6B Base is the smallest pretrained foundation model in Alibaba Cloud's Qwen 3 family, with approximately 600 million parameters. As a base model, it is not tuned for chat or instructions and is intended for fine-tuning, research, and experimentation. Its minimal size makes it suitable for rapid prototyping and resource-constrained training experiments. The model runs on virtually any hardware, including CPU-only setups. It is useful for educational purposes, architecture exploration, and as a compact foundation for task-specific fine-tuning where model size is a primary constraint. Released under the Apache 2.0 license.
Qwen3 8B Base
Alibaba · 8.2B · runs from 4.1 GB
Qwen3 8B Base is an 8.2-billion parameter pretrained foundation model from Alibaba Cloud's Qwen 3 series. As a base model, it is not instruction-tuned and is intended for fine-tuning, research, and as a starting point for custom downstream applications. It was trained on a large multilingual corpus with improved data quality and training methodology compared to the Qwen 2.5 generation. The model runs efficiently on consumer GPUs with 8GB or more of VRAM and serves as the foundation for the Qwen3 8B instruction-tuned variant and community fine-tunes. It is a strong choice for practitioners building specialized models through further training. Released under the Apache 2.0 license.
Qwen2 1.5B Instruct
Alibaba · 1.5B · runs from 0.8 GB
Qwen2 1.5B Instruct is a 1.5B-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 14B Base
Alibaba · 14.8B · runs from 4.7 GB
Qwen3 14B Base is a 14.8B-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 GB
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.
Qwen3.5 0.8B Base
Alibaba · 873M · runs from 0.6 GB
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.