Model ID
Qwen/Qwen3.5-122B-A10B
Capabilities
Qwen3.5 122B A10B
Qwen3.5 122B A10B is a multimodal Mixture-of-Experts (MoE) large language model optimized for long-context reasoning, vision-language understanding, and scalable agentic workflows. It contains 122B total parameters with 10B active parameters, activating 8 routed experts plus 1 shared expert from 256 experts per token. The model employs a 48-layer hybrid architecture combining Gated DeltaNet and full attention layers, with 32 attention heads and a 3,072 hidden size. It supports a 262K token context window, extendable to ~1M, and integrates a 27-layer vision encoder for unified multimodal processing.
About this model
Qwen3.5 122B A10B is a multimodal Mixture-of-Experts (MoE) large language model optimized for long-context reasoning, vision-language understanding, and scalable agentic workflows. It contains 122B total parameters with 10B active parameters, activating 8 routed experts plus 1 shared expert from 256 experts per token. The model employs a 48-layer hybrid architecture combining Gated DeltaNet and full attention layers, with 32 attention heads and a 3,072 hidden size. It supports a 262K token context window, extendable to ~1M, and integrates a 27-layer vision encoder for unified multimodal processing.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (Qwen/Qwen3.5-122B-A10B). 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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