Solar-Open-100B-NVFP4A16
This repository contains a build of upstage/Solar-Open-100B that FuriosaAI quantized in-house to NVFP4A16, together with a Furiosa Executable Bundle (FXB) for running it on FuriosaAI RNGD with Furiosa-LLM. The NVFP4A16 weights in this repository also run directly on other frameworks (such as vLLM, SGLang, and Transformers); for the base model and general usage, see the upstream upstage/Solar-Open-100B model card.
Overview
Solar-Open-100B is a large-scale open-weight language model developed by Upstage. It is an auto-regressive Mixture-of-Experts (MoE) transformer that supports English and Korean, and it handles both reasoning and non-reasoning chat as well as tool (function) calling. Its intended use is the same as the upstream upstage/Solar-Open-100B, and it is released under the Upstage Solar License.
- Architecture: SolarOpen (Mixture-of-Experts)
- Input / Output: Text / Text
- Supported Inference Engine: Furiosa LLM
- Supported Hardware: FuriosaAI RNGD
Quantization
The weights are quantized to NVFP4 (4-bit floating point), while activations and the KV cache remain in 16-bit precision (NVFP4A16).
Features
- Reasoning. Solar-Open is a reasoning model. Launch the server with
--reasoning-parser solar_opento have the chain of thought returned in a separate field. The reasoning depth is controllable per request throughreasoning_effort("low","medium","high"; default"high"). - Tool calling. The model supports tool (function) calling through the
solar_opentool-call parser.
Parallelism Strategy
On RNGD, Solar-Open-100B-NVFP4A16 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
Serve the model with the solar_open reasoning parser so the chain of thought is
returned in a separate field:
furiosa-llm serve furiosa-ai/Solar-Open-100B-NVFP4A16 \
--reasoning-parser solar_open
To also enable tool (function) calling, add the solar_open tool-call parser;
keep --reasoning-parser solar_open so thinking is still parsed into its own
field:
furiosa-llm serve furiosa-ai/Solar-Open-100B-NVFP4A16 \
--reasoning-parser solar_open \
--enable-auto-tool-choice \
--tool-call-parser solar_open
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/Solar-Open-100B-NVFP4A16",
"messages": [{"role": "user", "content": "What is the capital of France?"}]
}' \
| python -m json.tool
With --reasoning-parser solar_open, Solar-Open returns its reasoning 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/Solar-Open-100B-NVFP4A16",
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)
Note: The
reasoningfield 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 anAttributeError.
Advanced Usage
Controlling reasoning effort. You can control how much the model reasons per
request with the reasoning_effort parameter ("low", "medium", or "high";
default "high"). Solar-Open's chat template maps this to its thinking behavior:
"high" and "medium" produce a chain of thought — with "high" allowed a larger
reasoning-token budget than "medium" — while "low" (and "minimal") prefill an
empty thinking block so the model skips reasoning and answers directly:
response = client.chat.completions.create(
model="furiosa-ai/Solar-Open-100B-NVFP4A16",
messages=[{"role": "user", "content": "How many r's are in 'strawberry'?"}],
extra_body={"reasoning_effort": "high"},
)
print("Reasoning:", response.choices[0].message.reasoning)
print("Answer:", response.choices[0].message.content)
Tool calling. With the server launched using
--enable-auto-tool-choice --tool-call-parser solar_open (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 - upstage/Solar-Open-100B — upstream model card