Qwen3 VL 235B A22B Instruct FP8 dynamic

Qwen 3

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released May 2025context N/A

A large-scale multimodal model that, despite being listed with vision-language capabilities in its name, is currently configured here for text-in, text-out interactions only. It carries the Qwen3 lineage's reputation for strong multilingual reasoning and instruction-following. The FP8 dynamic quantization keeps memory footprint manageable for a 235B parameter model with 22B active parameters via sparse mixture-of-experts architecture.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Multimodal

Moderate

Factual Knowledge

Moderate

Creative Writing

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Qwen3 VL 235B A22B Instruct FP8 dynamic

Qwen 3

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released May 2025context N/A

A large-scale multimodal model that, despite being listed with vision-language capabilities in its name, is currently configured here for text-in, text-out interactions only. It carries the Qwen3 lineage's reputation for strong multilingual reasoning and instruction-following. The FP8 dynamic quantization keeps memory footprint manageable for a 235B parameter model with 22B active parameters via sparse mixture-of-experts architecture.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Multimodal

Moderate

Factual Knowledge

Moderate

Creative Writing

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

Active ParametersThe subset of a model's total parameters that are actually used during inference for each input, as opposed to all parameters being used every time.ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.Dynamic QuantizationA quantization approach that adjusts precision levels during inference based on the input data, optimizing the balance between speed and accuracy on-the-fly.FP8 Dynamic QuantizationA compression technique that reduces model size and speeds up inference by representing weights and activations using 8-bit floating-point numbers, with dynamic scaling adjusted per batch to maintain accuracy.FP8 Dynamic QuantizationA specific quantization method that uses 8-bit floating-point numbers and adjusts precision dynamically based on the data being processed, balancing speed and accuracy.Instruction-FollowingThe ability of a model to understand and execute specific tasks or commands given in natural language prompts.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.MultilingualA model trained to understand and generate text in multiple languages, not just English.Multilingual ReasoningThe capability to understand, process, and reason through problems in multiple languages, not just English.MultimodalA model that can process and understand multiple types of input, such as both text and images.Multimodal ModelAn AI model that can process and understand multiple types of input data, such as video, images, and text together.Parameter ModelA neural network described by the number of learnable weights it contains; more parameters generally mean greater capacity to learn complex patterns, but also require more computational resources.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Text-In, Text-OutA model that accepts text as input and produces text as output, without support for images, audio, or other data types.Vision-LanguageA model designed to understand and reason about both visual content (images) and natural language text together.