All LLM Models
Browse 116 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 VL 7B Instruct
Alibaba · 8.3B · runs from 3.2 GB
747.3K
1.3KQwen2 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 Published in August 2024, it was later superseded by Qwen2.5-VL-7B-Instruct, which its own model card lists as its successor.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 72B
Alibaba · 72.7B · runs from 31.0 GB
Qwen2.5-72B is the base, pretrained 72.7-billion-parameter language model from Alibaba's Qwen2.5 series — it is not instruction-tuned and is not intended for direct conversational use; Alibaba recommends applying SFT, RLHF, or further pretraining on top of it. It uses a Transformer architecture with RoPE, SwiGLU, RMSNorm, QKV attention bias, 80 layers, and grouped-query attention (64 query heads, 8 key/value heads), with stronger coding, math, and structured-output ability than the earlier Qwen2 line, plus multilingual coverage across 29-plus languages. At 72.7 billion parameters, it needs a multi-GPU workstation to run at full precision, though quantized versions fit fewer cards. Context length is 131,072 tokens. It is released under Alibaba's custom Qwen license, which permits research and commercial use but requires a separate license from Alibaba once a product or service passes 100 million monthly active users. It was published in September 2024.
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.
Qwen2.5 1.5B
Alibaba · 1.5B · runs from 1 GB
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 35B A3B Base
Alibaba · 36.0B · runs from 10.3 GB
Qwen3.5-35B-A3B-Base is the mixture-of-experts member of Alibaba's Qwen3.5 base family, pairing a 256-expert MoE layer (8 routed plus 1 shared expert per token) with the same Gated DeltaNet and gated-attention hybrid backbone used across the line. It totals roughly 36 billion parameters but activates only about 3 billion per token (the "A3B" in its name), so decoding stays fast even though the full expert set must stay resident in memory. Like its dense siblings it is pretrain-only, for fine-tuning and research, not direct chat; unlike them, its card lists only a pretraining stage, with no post-training pass. It needs a high-end consumer GPU once quantized. Context length is 262,144 tokens natively, extensible to 1,010,000 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in February 2026, alongside four smaller dense Qwen3.5-Base models from 0.8B to 9B parameters.
Qwen2 7B
Alibaba · 7.6B · runs from 3.6 GB
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.
Qwen2 72B Instruct
Alibaba · 72.7B · runs from 21.0 GB
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.
Qwen3 4B Base
Alibaba · 4.0B · runs from 1.6 GB
Qwen3-4B-Base is Alibaba's 4-billion-parameter base pretrained model from the Qwen3 generation, the successor to Qwen2.5. Like other -Base checkpoints, it is a raw causal language model meant for fine-tuning or research, not direct conversation. Qwen3 was trained on 36 trillion tokens across 119 languages, tripling Qwen2.5's language coverage, using a three-stage pipeline that builds general knowledge, then reasoning skills in code and STEM, then extends context length. At 4B dense parameters, it runs easily on a single consumer GPU, even without heavy quantization. The model supports a 32,768 token context window. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in April 2025. Qwen3 introduced a mix of dense and mixture-of-experts models alongside refinements like QK layernorm for training stability.
Qwen2.5 0.5B
Alibaba · 494M · runs from 0.5 GB
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.
Qwen3 1.7B Base
Alibaba · 1.7B · runs from 1.0 GB
Qwen3 1.7B Base is a 1.7-billion parameter pretrained foundation model from Alibaba Cloud's Qwen 3 family. It is a compact base model designed for fine-tuning, research, and custom applications rather than direct conversational use. Its small size makes it accessible for resource-constrained fine-tuning and rapid experimentation. The model can run on virtually any modern GPU and benefits from the improved pretraining data of the Qwen 3 generation. It is suitable as a lightweight foundation for domain-specific fine-tunes and student models in distillation pipelines. Released under the Apache 2.0 license.
Qwen3.5 2B Base
Alibaba · 2.3B · runs from 1.4 GB
Qwen3.5-2B-Base is a 2.3-billion-parameter dense checkpoint in Alibaba's Qwen3.5 base model family, a native vision-language foundation model rather than a text-only model with vision bolted on. It is pretrain-only: fine-tuning, in-context-learning, or further research, not direct conversation, though its control tokens are compatible with the official chat template for efficient LoRA-style adaptation. Its hybrid architecture pairs Gated DeltaNet linear attention with periodic gated full-attention layers. At 2.3B parameters, it runs comfortably on a single consumer GPU, even unquantized. The model supports a native 262,144 token context window, extensible up to 1,010,000 tokens. It is released under the Apache 2.0 license, and was published in February 2026. Qwen3.5 introduced early-fusion multimodal pretraining that Alibaba says outperforms the separate Qwen3-VL models on reasoning, coding, and visual understanding.
Qwen3 30B A3B Base
Alibaba · 30.5B · runs from 12.2 GB
Qwen3 30B A3B Base is a 30.5B-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.
Qwen3Guard Gen 0.6B
Alibaba · 752M · runs from 0.7 GB
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.
Qwen1.5 0.5B Chat
Alibaba · 620M · runs from 0.8 GB
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 4B
Alibaba · 4.4B · runs from 2.4 GB
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.
Qwen2.5 Math 1.5B
Alibaba · 1.5B · runs from 1 GB
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.
Qwen1.5 1.8B Chat
Alibaba · 1.8B · runs from 1.5 GB
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.
Qwen1.5 32B Chat
Alibaba · 32.5B · runs from 14.3 GB
Qwen1.5-32B-Chat is Alibaba's instruction-tuned, 32.5-billion-parameter chat model from the Qwen1.5 series, a beta release of the Qwen2 architecture that sits between the 14B and 72B dense models in the lineup. Qwen1.5 improved on the original Qwen with stable 32K context support across all model sizes, broader multilingual coverage, and no need for custom trust_remote_code, and this 32B checkpoint additionally uses grouped-query attention, unlike the smaller Qwen1.5 sizes, for faster inference. It was aligned on top of the pretrained base with supervised fine-tuning and direct preference optimization. At 32.5 billion parameters it needs a high-end consumer GPU, or a multi-GPU setup once quantized, to run comfortably. Context length is 32,768 tokens. It is released under Alibaba's Tongyi Qianwen license, a custom license that is free for most commercial and research use but requires a separate license from Alibaba once a deployment exceeds 100 million monthly active users. It was published in April 2024, part of Alibaba's second LLM generation, later followed by Qwen2 and Qwen2.5.
Qwen1.5 32B
Alibaba · 32.5B · runs from 14.3 GB
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.
Qwen3.5 4B Base
Alibaba · 4.7B · runs from 2.5 GB
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.