All LLM Models
Browse 136 LLM models with VRAM requirements, quantization options, and hardware compatibility.
Model List
Qwen2.5 0.5B Instruct
Alibaba · 494M · runs from 0.8 GB at Q4
4.6M
737Qwen2.5 0.5B Instruct is the smallest instruction-tuned model in Alibaba Cloud's Qwen 2.5 family, with just 494 million parameters. It is designed for ultra-lightweight deployment scenarios where minimal hardware resources are available, running comfortably on virtually any modern GPU or even CPU-only configurations. Despite its tiny footprint, the model supports a 128K token context window and can handle basic chat, simple summarization, and lightweight instruction following. It is primarily useful for edge deployment, experimentation, and prototyping where model size is a critical constraint. Released under the Apache 2.0 license.
Qwen3.5 2B
Alibaba · 2.3B · runs from 1.9 GB at Q4
Qwen3.5 2B is a 2.3-billion-parameter model from Alibaba's Qwen team, one of the smaller entries in the Qwen3.5 lineup (0.8B–9B) built to handle text and image input together. It uses a hybrid architecture mixing linear-attention layers with periodic full-attention layers to keep long-context inference efficient. As a vision-capable model it can read and reason about images alongside written prompts, and its small size lets it run on a laptop or even a phone once quantized, without a dedicated GPU. It supports an unusually long 262K token context window for its size. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use, and was published in late February 2026 as part of Alibaba's push toward compact, natively multimodal edge models.
Qwen2.5 VL 32B Instruct
Alibaba · 33.5B · runs from 24.7 GB at Q4
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.
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.
Qwen2.5 72B Instruct
Alibaba · 72.7B · runs from 49.3 GB at Q4
Qwen2.5 72B Instruct is the flagship model of the Qwen 2.5 series from Alibaba Cloud, with 72.7 billion parameters. It is instruction-tuned for conversational use and excels across reasoning, coding, mathematics, and multilingual tasks. Qwen2.5 72B delivers performance competitive with leading open-weight 70B-class models while supporting a 128K token context window and structured output generation. The model uses a Transformer architecture with grouped-query attention and was pretrained on a diverse multilingual corpus of over 18 trillion tokens. Running it locally requires high-VRAM GPUs or multi-GPU setups, though quantized formats make it accessible on workstation-class hardware. 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.
Qwen3 Omni 30B A3B Instruct
Alibaba · 35.3B · runs from 27.2 GB at Q4
Qwen3-Omni-30B-A3B-Instruct is Alibaba's flagship omni-modal model, a mixture-of-experts Thinker-Talker system that processes text, images, audio, and video and responds with real-time streaming text and speech. Its text backbone routes across 128 experts with 8 active per token, roughly 3 billion active parameters out of around 35 billion total, since it keeps both the "thinker" and the speech-generating "talker" plus its audio codec resident in memory. It supports 119 text languages, 19 spoken input languages, and 10 spoken output languages, and needs a high-end consumer GPU or multi-GPU setup once quantized. Its language backbone supports a 65,536 token context window. It is released under the Apache 2.0 license, and was published in September 2025 as the instruct counterpart to Qwen3-Omni-30B-A3B-Thinking, which drops the talker to focus on text output with chain-of-thought reasoning.
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.
Qwen3.5 122B A10B
Alibaba · 125.1B · runs from 75.5 GB at Q4
Qwen3.5 122B A10B is Alibaba's 125-billion-parameter mixture-of-experts model from the Qwen 3.5 medium series, with about 10 billion parameters active per token (the A10B in its name). Because only the active experts run per token, inference is markedly faster than a dense model of comparable size, though all the weights still need to be held in memory. It is a unified vision-language model, handling images alongside text for reasoning, coding, and agentic workflows. Given its size, local inference needs a multi-GPU workstation or a large unified-memory machine; most people access a model this size through a hosted endpoint instead. It supports a 262K token context window and is released under the Apache 2.0 license, allowing unrestricted commercial and research use. Published in February 2026, it sits alongside Qwen3.5-Flash, Qwen3.5-27B, and Qwen3.5-35B-A3B in Alibaba's medium-model series.
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.
Qwen3 Next 80B A3B Instruct
Alibaba · 81.3B · runs from 49.3 GB at Q4
Qwen3 Next 80B A3B Instruct is a Mixture of Experts (MoE) model from Alibaba Cloud's Qwen 3 series, with approximately 81.3 billion total parameters and around 3 billion active parameters per forward pass. This extreme ratio between total and active parameters allows the model to encode extensive knowledge across its expert layers while maintaining very fast per-token inference, making it an unusually efficient design for its capability level. The model is instruction-tuned for general-purpose chat and requires VRAM proportional to its full 80B parameter count for weight loading, typically needing high-VRAM GPUs or quantized multi-GPU setups. Its low active parameter count results in fast generation speeds despite the large total model size. Released under the Apache 2.0 license.
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.
Qwen3 235B A22B Instruct 2507
Alibaba · 235.1B · runs from 142.9 GB at Q4
Qwen3 235B A22B Instruct 2507 is Alibaba's flagship instruction-tuned model from the July 2025 update, featuring 235 billion total parameters with approximately 22 billion active during inference. As the largest instruct model in the Qwen3 lineup, it delivers top-tier conversational quality, knowledge depth, and instruction following. Despite its massive total parameter count, the MoE architecture keeps active compute manageable. Running this model locally still requires substantial hardware, typically multi-GPU setups with 48 GB or more of total VRAM, but the 2507 refresh makes it one of the most capable open-weight models available for users with high-end local infrastructure.
Qwen3.5 397B A17B
Alibaba · 403.4B · runs from 242.6 GB at Q4
Qwen3.5 397B A17B is a 403.4B-parameter open language model from Alibaba in the Qwen 3.5 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.
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 235B A22B
Alibaba · 235.1B · runs from 142.9 GB at Q4
Qwen3 235B A22B is the largest model in Alibaba Cloud's Qwen 3 series, a Mixture of Experts (MoE) model with 235 billion total parameters and approximately 22 billion active parameters per forward pass. The MoE architecture enables it to deliver performance competitive with the best available open-weight models while requiring significantly less compute per token than a comparably sized dense model. It supports hybrid thinking mode for flexible chain-of-thought reasoning. Due to its massive total parameter count, running Qwen3 235B A22B locally requires substantial VRAM to load all expert weights, typically needing multiple high-end professional GPUs even at reduced precision. In heavily quantized formats it becomes accessible on workstation-class multi-GPU setups. Released under the Apache 2.0 license.
Qwen3 235B A22B Thinking 2507
Alibaba · 235.1B · runs from 142.9 GB at Q4
Qwen3 235B A22B Thinking 2507 is the reasoning and chain-of-thought variant of Alibaba's largest Qwen3 mixture-of-experts model, updated in July 2025. With 235 billion total parameters and about 22 billion active per forward pass, it represents the pinnacle of Qwen3's reasoning capabilities. This model excels at complex multi-step problems, mathematical reasoning, code analysis, and tasks requiring deep logical thinking. It demands serious hardware to run locally, but for users with multi-GPU setups, it offers reasoning performance that rivals the best proprietary models while keeping all computation on your own machines.
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.
Qwen3 Next 80B A3B Thinking
Alibaba · 81.3B · runs from 49.3 GB at Q4
Qwen3 Next 80B A3B Thinking is Alibaba's 81-billion-parameter mixture-of-experts model, with about 3 billion parameters active per token (the A3B in its name), drawn from 512 experts with only 10 routed at a time. Because only the active experts run per token, inference is far cheaper than a dense model this size, though the full weight set still needs to fit in memory. It combines mostly-linear Gated DeltaNet attention with periodic full attention layers, and this Thinking variant always reasons through a visible chain of thought before answering. Running it locally needs multiple GPUs or a large unified-memory machine. It supports a 262K token context window, is released under the Apache 2.0 license, and was published in September 2025 as part of the Qwen3-Next line introducing this hybrid-attention architecture.
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.
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.