Model ID
Qwen/Qwen3.5-0.8B
Capabilities
Qwen3.5 0.8B
Qwen3.5 0.8B is a compact multimodal language model combining a causal transformer with a vision encoder for efficient cross-modal reasoning. It features a 0.8B parameter architecture with 24 layers, 1024 hidden size, 8 attention heads (2 KV heads), and integrates Gated DeltaNet linear attention with periodic full attention layers. The model supports a 262K token context window and uses multi-token prediction (MTP). It is designed for efficient multimodal understanding, strong reasoning, and broad multilingual support across 200+ languages with low-latency inference.
About this model
Qwen3.5 0.8B is a compact multimodal language model combining a causal transformer with a vision encoder for efficient cross-modal reasoning. It features a 0.8B parameter architecture with 24 layers, 1024 hidden size, 8 attention heads (2 KV heads), and integrates Gated DeltaNet linear attention with periodic full attention layers. The model supports a 262K token context window and uses multi-token prediction (MTP). It is designed for efficient multimodal understanding, strong reasoning, and broad multilingual support across 200+ languages with low-latency inference.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (Qwen/Qwen3.5-0.8B). Released under Apache 2.0. Further reading: Blog
Deploy Private Inference Endpoint
Choose your hardware and inference engine to get deployment commands and performance benchmarks tailored to your infrastructure
Hardware
MI325X
2,048 GB
B200
1,440 GB
Engine
vLLM
SGLang
Llama.cpp
Deployment code
Access Restricted
Enter your email to get access to this content
Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on MI325X · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
AMD Instinct MI325X
Unlock Next-Generation AI Power on Vultr Cloud GPU