Qwen3-coder-30B-A3B-Instruct

CheckpointQwen/Qwen3-coder-30B-A3B-InstructModel card indexedModel card

Support summary

Qwen3-coder-30B-A3B-Instruct is a checkpoint published by Qwen, built on the Qwen3MoeForCausalLM architecture. vLLM 0.28.0, the newest indexed vLLM release, documents support. Official recipes or cookbooks cover SGLang, without pinning a stable release. TensorRT-LLM lists its architecture, but not this exact checkpoint.

vLLM documents support in 20 of 20 indexed releases, from 0.17.0 to 0.28.0.

Documented hardware
Intel GPU / XPU
Documented features
Tool calling
Configured context
262,144 tokens
License
apache-2.0

Hardware, quantization and features count only statements about this checkpoint; engine-wide capabilities are listed with each release.

Hugging Face model card
Publisher
Qwen
Task
text generation
License
apache-2.0
Library
transformers
Architecture
Qwen3MoeForCausalLM
Weight tensor elements
30.5B
Tensor types
BF16
Configured context
262,144 tokens
Weight access
Public
Repository created
2025-07-31
Source 1: Qwen/Qwen3-Coder-30B-A3B-Instruct · publisher model cardSource 2: Qwen/Qwen3-Coder-30B-A3B-Instruct · Hugging Face metadataSource 3: Qwen/Qwen3-Coder-30B-A3B-Instruct · publisher configurationReviewed
Model notes and sources

Engine support

3 engines

Engine support for Qwen3-coder-30B-A3B-Instruct
EngineReleaseCheckpoint supportEvidence scopeLast known supported
vLLM0.28.0Newest indexedCheckpoint evidenceSource: vLLM v0.28.0 · docs/models/hardware_supported_models/xpu.mdReviewed 0.28.0This release
Features, dependencies and evidence for vLLM 0.28.0
SGLangVersion not pinnedRecipe / cookbookDocumented recipe

Official recipe

Source 1: sglang · docs/cookbook/autoregressive/Qwen/Qwen3-Coder.mdxSource 2: sglang · docs/src/snippets/autoregressive/qwen3-coder-deployment.jsxReviewed
No supported release indexed
Features, dependencies and evidence for SGLang 0.5.19
TensorRT-LLM1.2.1Newest indexedArchitecture listed; exact checkpoint unknownNo supported release indexed
Features, dependencies and evidence for TensorRT-LLM 1.2.1

Recipes and cookbooks