Model ID
Qwen/Qwen3.5-4B
Capabilities
Qwen3.5 4B
Qwen3.5 4B is a multimodal causal language model built with a 4B parameter architecture. It uses a 32-layer transformer with 16 attention heads, 4 KV heads, and a 2,560 hidden size, paired with a 9,216 intermediate dimension. The model supports a native 262K token context window, extendable beyond 1M tokens, and integrates a 24-layer vision encoder with 1,024 hidden size. It combines Gated DeltaNet and attention layers, enabling efficient long-context reasoning, strong multimodal understanding, and broad multilingual capability across 200+ languages.
About this model
Qwen3.5 4B is a multimodal causal language model built with a 4B parameter architecture. It uses a 32-layer transformer with 16 attention heads, 4 KV heads, and a 2,560 hidden size, paired with a 9,216 intermediate dimension. The model supports a native 262K token context window, extendable beyond 1M tokens, and integrates a 24-layer vision encoder with 1,024 hidden size. It combines Gated DeltaNet and attention layers, enabling efficient long-context reasoning, strong multimodal understanding, and broad multilingual capability across 200+ languages.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (Qwen/Qwen3.5-4B). Released under Apache 2.0. Further reading: Blog
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Hardware
MI325X
2,048 GB
B200
1,440 GB
Engine
vLLM
SGLang
Llama.cpp
Deployment code
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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
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