The Qwen3 30B A3B Instruct 2507 is an instruction-aligned Mixture-of-Experts (MoE) Causal Language Model built on a 48-layer transformer architecture with 29.9B non-embedding parameters, designed for efficient large-scale generation under sparse activation. It activates approximately 3.3B parameters per forward pass from a total parameter count of 30.5B, enabling efficient inference while retaining strong general-purpose performance. With native support for a 262,144-token context window, it is well-suited for long-document processing, extended conversational contexts, and large-context instruction workloads.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (Qwen/Qwen3-30B-A3B-Instruct-2507). Released under Apache 2.0. Further reading: Paper · Blog
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Benchmarks measured by Vultr on B200 · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.