# IFM/Jais-3-32B — vLLM serving recipe

> Dense long-context model for reasoning research and high-capacity serving

Configuration: hardware `h200` (8× H200) · strategy `single_node_tp` (Tensor Parallel) · variant `default` (BF16) · features `reasoning`, `tool_calling`

- Interactive page: https://vllm-recipes-git-preview-vllm-pr-60762-inferact-inc.vercel.app/IFM/Jais-3-32B
- This configuration as JSON: https://vllm-recipes-git-preview-vllm-pr-60762-inferact-inc.vercel.app/IFM/Jais-3-32B.json?hardware=h200

The commands below were rendered for exactly this configuration and take precedence over any example in the guide.

## Other configurations

Change the query string (the same parameters as the page URL) and fetch this URL again. Unknown or incompatible values fall back to the recipe default, so check the Configuration line.

- `hardware`: `h100`, `h200`, `b200`, `gb200`, `b300`, `gb300`, `mi300x`, `mi325x`, `mi350x`, `mi355x`
- `variant`: `default`
- `strategy`: `single_node_tp`, `multi_node_tp`, `multi_node_tp_pp` (multi-node strategies also need `nodes=<n>`)
- `features`: comma-separated list of `reasoning`, `tool_calling` (default: `reasoning`, `tool_calling`)

## Install

```bash
uv venv
source .venv/bin/activate
uv pip install -U vllm --torch-backend auto
```

Docker image: `vllm/vllm-openai:latest`

## Serve

```bash
vllm serve IFM/Jais-3-32B \
  --trust-remote-code \
  --chat-template-content-format string \
  --dtype bfloat16 \
  --tensor-parallel-size 2 \
  --reasoning-parser k2_horizon \
  --enable-auto-tool-choice \
  --tool-call-parser k2_horizon
```

Docker equivalent:

```bash
docker run --gpus all \
  --privileged --ipc=host -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm/vllm-openai:latest IFM/Jais-3-32B \
  --trust-remote-code \
  --chat-template-content-format string \
  --dtype bfloat16 \
  --tensor-parallel-size 2 \
  --reasoning-parser k2_horizon \
  --enable-auto-tool-choice \
  --tool-call-parser k2_horizon
```

## Guide

> Background reference. Commands in it are examples and may target another configuration — use the Serve section above.

### Overview

Jais-3-32B is a large dense model in the IFM Jais-3 family,
with 34.78B stored parameters including embeddings and support for up to
524,288 tokens of context.

Jais-3 is an open-weight IFM model family built for transparent
foundation-model research, staged checkpoint analysis, and practical
deployment. The series spans compact dense models for local experimentation,
dense mid-size and large models such as Jais-3-32B for high-quality
research workloads, and mixture-of-expert (MoE) models for higher-capacity
serving.

### Launch Commands

```bash
vllm serve IFM/Jais-3-32B \
  --tensor-parallel-size 2 \
  --trust-remote-code \
  --chat-template-content-format string \
  --dtype bfloat16 \
  --reasoning-parser k2_horizon \
  --enable-auto-tool-choice \
  --tool-call-parser k2_horizon
```

### Client usage

```python
from openai import OpenAI

client = OpenAI(
    api_key="EMPTY", base_url="http://localhost:8000/v1", timeout=3600
)

resp = client.chat.completions.create(
    model="IFM/Jais-3-32B",
    messages=[{"role": "user", "content": "لدينا عددان مجموعهما 50 وحاصل ضربهما 600. ما هما العددان؟ اشرح خطوات الحل."}],
    temperature=1.0, top_p=0.95, max_tokens=2048,
)
print(resp.choices[0].message.content)
```

#### Thinking modes

The model supports selectable thinking through `chat_template_kwargs`, per
request or server-wide via `--default-chat-template-kwargs`:

- `{"reasoning_effort": "high"}` — full thinking (default).
- `{"reasoning_effort": "low"}` — no thinking.

### References

- [IFM/Jais-3-32B on Hugging Face](https://huggingface.co/IFM/Jais-3-32B)
- [Jais 3 Collection](https://huggingface.co/collections/IFM/jais-3)