LLM Inferenz Server — Qwen3.5-35B-A3B

Self-hosted LLM inference for ~15 concurrent students using Qwen3.5-35B-A3B (MoE, 35B total / 3B active per token), served via vLLM inside an Apptainer container on a GPU server.

Architecture

Students (OpenAI SDK / curl)
        │
        ▼
  ┌──────────────────────────────┐
  │  silicon.fhgr.ch:7080       │
  │  OpenAI-compatible API      │
  ├──────────────────────────────┤
  │  vLLM Server (nightly)      │
  │  Apptainer container (.sif) │
  ├──────────────────────────────┤
  │  Qwen3.5-35B-A3B weights    │
  │  (bind-mounted from host)   │
  ├──────────────────────────────┤
  │  2× NVIDIA L40S (46 GB ea.) │
  │  Tensor Parallel = 2        │
  └──────────────────────────────┘

Hardware

The server silicon.fhgr.ch has 4× NVIDIA L40S GPUs (46 GB VRAM each). The inference server uses 2 GPUs with tensor parallelism, leaving 2 GPUs free.

Component	Value
GPUs used	2× NVIDIA L40S
VRAM used	~92 GB total
Model size (BF16)	~67 GB
Active params/token	3B (MoE)
Context length	32,768 tokens
Port	7080

Prerequisites

• Apptainer (formerly Singularity) installed on the server
• NVIDIA drivers with GPU passthrough support (--nv flag)
• ~80 GB disk for model weights + ~8 GB for the container image
• Network access to Hugging Face (for model download) and Docker Hub (for container build)

Note

: No pip or python is needed on the host — everything runs inside the Apptainer container.

---

Step-by-Step Setup

Step 0: SSH into the Server

ssh herzogfloria@silicon.fhgr.ch

Step 1: Clone the Repository

git clone https://gitea.fhgr.ch/herzogfloria/LLM_Inferenz_Server_1.git ~/LLM_local
cd ~/LLM_local
chmod +x *.sh

Step 2: Check GPU and Environment

nvidia-smi
apptainer --version
df -h ~

Step 3: Build the Apptainer Container

bash 01_build_container.sh

Pulls the vllm/vllm-openai:latest Docker image, upgrades vLLM to nightly (required for Qwen3.5 support), installs latest transformers from source, and packages everything into vllm_qwen.sif (~8 GB). Takes 15-20 minutes.

Step 4: Download the Model (~67 GB)

bash 02_download_model.sh

Downloads Qwen3.5-35B-A3B weights using huggingface-cli inside the container. Stored at ~/models/Qwen3.5-35B-A3B. Takes 5-30 minutes depending on bandwidth.

Step 5: Start the Server

Interactive (foreground) — recommended with tmux:

tmux new -s llm
bash 03_start_server.sh
# Ctrl+B, then D to detach

Background with logging:

bash 04_start_server_background.sh
tail -f logs/vllm_server_*.log

The model takes 2-5 minutes to load into GPU memory. It's ready when you see:

INFO:     Uvicorn running on http://0.0.0.0:7080

Step 6: Test the Server

From another terminal on the server:

curl http://localhost:7080/v1/models

Or run the full test (uses openai SDK inside the container):

apptainer exec --writable-tmpfs vllm_qwen.sif python3 test_server.py

Step 7: Share with Students

Distribute STUDENT_GUIDE.md with connection details:

• Base URL: http://silicon.fhgr.ch:7080/v1
• Model name: qwen3.5-35b-a3b

---

Configuration

All configuration is via environment variables passed to 03_start_server.sh:

Variable	Default	Description
MODEL_DIR	~/models/Qwen3.5-35B-A3B	Path to model weights
PORT	7080	HTTP port
MAX_MODEL_LEN	32768	Max context length (tokens)
GPU_MEM_UTIL	0.92	Fraction of GPU memory to use
API_KEY	(empty = no auth)	API key for authentication
TENSOR_PARALLEL	2	Number of GPUs

Examples

# Increase context length
MAX_MODEL_LEN=65536 bash 03_start_server.sh

# Add API key authentication
API_KEY="your-secret-key" bash 03_start_server.sh

# Use all 4 GPUs (more KV cache headroom)
TENSOR_PARALLEL=4 bash 03_start_server.sh

---

Server Management

# Start in background
bash 04_start_server_background.sh

# Check if running
curl -s http://localhost:7080/v1/models | python3 -m json.tool

# View logs
tail -f logs/vllm_server_*.log

# Stop
bash 05_stop_server.sh

# Monitor GPU usage
watch -n 2 nvidia-smi

# Reconnect to tmux session
tmux attach -t llm

---

Files Overview

File	Purpose
vllm_qwen.def	Apptainer container definition (vLLM nightly + deps)
01_build_container.sh	Builds the Apptainer .sif image
02_download_model.sh	Downloads model weights (runs inside container)
03_start_server.sh	Starts vLLM server (foreground)
04_start_server_background.sh	Starts server in background with logging
05_stop_server.sh	Stops the background server
test_server.py	Tests the running server
STUDENT_GUIDE.md	Instructions for students

---

Troubleshooting

"CUDA out of memory"

• Reduce MAX_MODEL_LEN (e.g., 16384)
• Reduce GPU_MEM_UTIL (e.g., 0.85)

Container build fails

• Ensure internet access and sufficient disk space (~20 GB for build cache)
• Try pulling manually first: apptainer pull docker://vllm/vllm-openai:latest

"No NVIDIA GPU detected"

• Verify nvidia-smi works on the host
• Ensure --nv flag is present (already in scripts)
• Test: apptainer exec --nv vllm_qwen.sif nvidia-smi

"Model type qwen3_5_moe not recognized"

• The container needs vLLM nightly and latest transformers
• Rebuild the container: rm vllm_qwen.sif && bash 01_build_container.sh

Students can't connect

• Check firewall: ports 7080-7090 must be open
• Verify the server binds to 0.0.0.0 (not just localhost)
• Students must be on the university network or VPN

Slow generation with many users

• Expected — vLLM batches requests but throughput is finite
• The MoE architecture (3B active) helps with per-token speed
• Monitor: curl http://localhost:7080/metrics