Mistral NeMo#
Mistral NeMo is a 12B instruction-tuned, auto-regressive dense transformer developed jointly by Mistral AI and NVIDIA for assistant-style text generation.
Furiosa-LLM runs Mistral NeMo in FP8 using RedHatAI’s quantized release
(static FP8 weights with dynamic FP8 activation quantization; the KV cache stays
in 16-bit precision). FuriosaAI publishes a pre-compiled build under the
furiosa-ai organization on the Hugging Face Hub,
shipping a Furiosa Executable Bundle (FXB) for running it on
FuriosaAI RNGD with Furiosa-LLM. The same weights also run
on other frameworks (such as vLLM, SGLang, and Transformers); for usage with
those, see the upstream model card linked below.
Available Models#
Model |
Quantization |
RNGD cards |
Notes |
|---|---|---|---|
FP8 |
2 |
12B instruction-tuned |
Architecture: Mistral NeMo (dense),
MistralForCausalLMInput / Output: Text / Text
Quantization: The linear (Transformer block) weights are quantized to FP8 (static), and activations use dynamic FP8 quantization at runtime (per-token, without offline calibration). The KV cache stays in 16-bit precision.
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/Mistral-Nemo-Instruct-2407-FP8
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/Mistral-Nemo-Instruct-2407-FP8",
"messages": [{"role": "user", "content": "What is the capital of France?"}]
}' \
| python -m json.tool
Advanced Usage#
Mistral-Nemo-Instruct-2407-FP8 is a non-reasoning chat model and does not require a reasoning parser or model-specific chat-template flags. For general serving options, see the Furiosa-LLM Server guide.
Learn more#
Tool Calling — supported parsers, tool-choice options, and examples
Furiosa-LLM Server (
furiosa-llm serve) — full OpenAI-compatible API reference and serving optionsUpstream model card: RedHatAI/Mistral-Nemo-Instruct-2407-FP8