EXAONE-4.0-32B-FP8

This repository contains a pre-compiled build of LGAI-EXAONE/EXAONE-4.0-32B-FP8 for running it on FuriosaAI RNGD with Furiosa-LLM.

Overview

EXAONE 4.0 is LG AI Research's bilingual (English / Korean) large language model. The 32B variant is an auto-regressive dense transformer that unifies a non-reasoning mode for general instruction following with a reasoning mode for harder problems, and carries native support for tool calling and agentic use. Its intended use is the same as the upstream LGAI-EXAONE/EXAONE-4.0-32B-FP8, and it is released under the EXAONE AI Model License Agreement 1.2 - NC.

• Architecture: EXAONE 4.0 (dense)
• 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

• Reasoning. EXAONE 4.0 is a hybrid model that runs in non-reasoning mode by default; enable reasoning per request via enable_thinking. Launch the server with the exaone4 reasoning parser to have Furiosa-LLM parse the chain of thought into a separate field (see Advanced Usage below).
• Tool calling. The model supports tool (function) calling through the hermes tool-call parser, the parser used for the EXAONE-4.0 format.

Parallelism Strategy

On RNGD, EXAONE-4.0-32B-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:

# Launch the server, listening on port 8000 by default
furiosa-llm serve furiosa-ai/EXAONE-4.0-32B-FP8

EXAONE 4.0 is a hybrid model that runs in non-reasoning mode by default. To have the chain of thought returned in a separate field once reasoning is enabled, start the server with the exaone4 reasoning parser (see Advanced Usage for how to turn reasoning on):

furiosa-llm serve furiosa-ai/EXAONE-4.0-32B-FP8 \
  --reasoning-parser exaone4

To also enable tool (function) calling, add the hermes tool-call parser (the parser used for the EXAONE-4.0 format); keep --reasoning-parser exaone4 so reasoning, when enabled, is parsed into its own field:

furiosa-llm serve furiosa-ai/EXAONE-4.0-32B-FP8 \
  --reasoning-parser exaone4 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes

When the server is ready, you will see:

INFO:     Started server process [27507]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)

Basic Usage

The server exposes an OpenAI-compatible API. You can send a request with curl:

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "furiosa-ai/EXAONE-4.0-32B-FP8",
    "messages": [{"role": "user", "content": "What is the capital of France?"}]
    }' \
    | python -m json.tool

EXAONE 4.0 runs in non-reasoning mode by default. Once reasoning is enabled (see Advanced Usage) and the server is started with --reasoning-parser exaone4, the chain of thought is returned separately from the final answer:

• response.choices[].message.reasoning (non-streaming)
• response.choices[].delta.reasoning (streaming)

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="furiosa-ai/EXAONE-4.0-32B-FP8",
    messages=[{"role": "user", "content": "How many r's are in 'strawberry'?"}],
)

print("Reasoning:", response.choices[0].message.reasoning)
print("Answer:", response.choices[0].message.content)

By default the response carries no reasoning content, so read only message.content; message.reasoning is populated once you enable reasoning.

Note: The reasoning field is not part of the OpenAI API specification but is a widely followed convention (the OpenAI Agents SDK, vLLM, and others). It appears only in responses that contain reasoning content; accessing it otherwise raises an AttributeError.

Advanced Usage

Enabling reasoning. EXAONE 4.0 runs in non-reasoning mode by default. Enable reasoning for a single request by passing enable_thinking through chat_template_kwargs; with --reasoning-parser exaone4 the chain of thought is then returned separately from the final answer:

# Enable reasoning for a single request
response = client.chat.completions.create(
    model="furiosa-ai/EXAONE-4.0-32B-FP8",
    messages=[{"role": "user", "content": "How many r's are in 'strawberry'?"}],
    extra_body={"chat_template_kwargs": {"enable_thinking": True}},
)

print("Reasoning:", response.choices[0].message.reasoning)
print("Answer:", response.choices[0].message.content)

To make every request reason, launch the server with --default-chat-template-kwargs (a request can still override with its own chat_template_kwargs):

furiosa-llm serve furiosa-ai/EXAONE-4.0-32B-FP8 \
  --reasoning-parser exaone4 \
  --default-chat-template-kwargs '{"enable_thinking": true}'

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

• Tool Calling — parsers, tool-choice options, and more examples
• Furiosa-LLM Server (furiosa-llm serve) — full OpenAI-compatible API reference and serving options
• LGAI-EXAONE/EXAONE-4.0-32B-FP8 — upstream model card