Qwen3-30B-A3B-Instruct-2507-FP8
This repository contains Qwen/Qwen3-30B-A3B-Instruct-2507-FP8
together with a Furiosa Executable Bundle (FXB) for running it on
FuriosaAI RNGD with Furiosa-LLM.
The same model also runs on other frameworks (such as vLLM, SGLang, and
Transformers); for usage with those, see the upstream
Qwen/Qwen3-30B-A3B-Instruct-2507-FP8 model card.
Overview
Qwen3-30B-A3B-Instruct-2507 is the updated (2507) instruct variant of Qwen3-30B-A3B,
an auto-regressive Mixture-of-Experts (MoE) transformer with 30.5B total
parameters of which about 3.3B are activated per token. It operates in
non-thinking mode only and offers strong instruction following, reasoning over
text, multilingual coverage, and tool usage. Its intended use is the same as the
upstream Qwen/Qwen3-30B-A3B-Instruct-2507-FP8,
and it is released under the Apache 2.0 License.
- Architecture: Qwen3-MoE (Mixture-of-Experts)
- Input / Output: Text / Text
- Supported Inference Engine: Furiosa LLM
- Supported Hardware: FuriosaAI RNGD
Quantization
Weights are quantized to FP8 (static), following the upstream FP8 release,
and activations use dynamic FP8 quantization at runtime (per-token / per-block).
The KV cache stays in 16-bit precision.
Features
- Tool calling. The model supports tool (function) calling through the
hermes tool-call parser, the parser used by the Qwen3 series.
Parallelism Strategy
On RNGD, Qwen3-30B-A3B-Instruct-2507-FP8 runs with a tensor-parallel size of
32 PEs, which maps to four RNGD cards (8 PEs per card).
Usage
To run this model with Furiosa-LLM, follow the example commands below after
installing Furiosa-LLM and its prerequisites.
Launch the server
The simplest way to serve the model is:
To enable tool (function) calling, add the hermes tool-call parser (the parser
used by the Qwen3 series):
When the server is ready, you will see:
Basic Usage
The server exposes an OpenAI-compatible API. You can send a request with curl:
Advanced Usage
Tool calling. With the server launched using
--enable-auto-tool-choice --tool-call-parser hermes (see
Launch the server), pass tools in the request and let the
model decide when to call them. See the
Tool Calling guide
for a complete client example and details on tool-choice options.
Learn more