All LLM Models
Browse 107 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
Qwen3 0.6B Base
Alibaba · 596M · runs from 0.7 GB
719.6K
195Qwen3 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.
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.
Qwen3.5 9B Base
Alibaba · 9.7B · runs from 4.7 GB
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.
Qwen1.5 14B
Alibaba · 14.2B · runs from 8 GB
Qwen1.5-14B is Alibaba's base pretrained language model, not instruction-tuned, from the Qwen1.5 series that served as a beta preview of Qwen2. It is a dense Transformer with SwiGLU activations, QKV bias, and an improved multilingual tokenizer, released alongside a matching aligned chat model. The Qwen1.5 line spans eight sizes from 0.5B to 72B dense models plus a 14B-class Mixture-of-Experts model, all with stable 32K context support. At 14 billion parameters it fits comfortably on a single consumer GPU once 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 January 2024; Qwen1.5 has since been superseded by the Qwen2, Qwen2.5, and Qwen3 series.
Qwen1.5 14B Chat
Alibaba · 14.2B · runs from 8 GB
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.
Qwen 14B Chat
Alibaba · 14.2B · runs from 6.6 GB
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.
Qwen2.5 Coder 1.5B
Alibaba · 1.5B · runs from 1.0 GB
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.
Qwen 7B
Alibaba · 7.7B · runs from 3.6 GB
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.
Qwen2.5 Math 7B
Alibaba · 7.6B · runs from 3.6 GB
Qwen2.5 Math 7B 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.
Qwen1.5 7B
Alibaba · 7.7B · runs from 4.7 GB
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.