Running Qwen 2.5 Coder 32B as a backend for AI coding agents like Cursor, Claude Code, and Cline requires sub-50ms Time to First Token (TTFT) on multi-turn conversations. While standard Ollama setups suffer from prefix re-computation and 3-second response delays on consecutive tool turns, pairing Qwen 2.5 Coder 32B with SGLang and FlashInfer delivers instant cache hits via RadixAttention.

Quick Start: Launch Qwen 2.5 Coder 32B on a Single 24GB GPU

To serve Qwen 2.5 Coder 32B AWQ on a single NVIDIA GeForce RTX 3090, RTX 4090, or A10G (24GB VRAM) on Ubuntu 24.04 LTS, install SGLang with FlashInfer and launch the OpenAI-compatible API server:

# 1. Create clean Python environment and install SGLang with FlashInfer
python3 -m venv ~/sglang-env
source ~/sglang-env/bin/activate
pip install --upgrade pip
pip install "sglang[all]>=0.3.4" --find-links https://flashinfer.ai/whl/cu124/torch2.4/flashinfer-python

# 2. Launch high-throughput server with RadixAttention and FP8 KV-cache
python3 -m sglang.launch_server \
  --model-path Qwen/Qwen2.5-Coder-32B-Instruct-AWQ \
  --port 30000 \
  --host 0.0.0.0 \
  --tp 1 \
  --kv-cache-dtype fp8_e5m2 \
  --mem-fraction-static 0.88 \
  --context-length 32768 \
  --trust-remote-code

This configuration fits within 19.8 GB of VRAM. It preserves 4.2 GB of GPU memory for the FP8 KV-cache, supporting up to 32,768 tokens of context with 68 tokens/second generation speed.

Why FlashInfer and RadixAttention Outperform Default Runtimes

Coding agents generate deep conversational trees. Turn 1 sends repo context (3,000 tokens). Turn 2 runs a terminal command and appends logs (1,500 tokens). Turn 3 refactors code (800 tokens).

Standard serving engines treat each turn as a fresh linear sequence or use simple page hashing. When prompt prefixes shift or diverge into parallel subagent calls, the engine flushes its cache and re-computes attention for the entire history.

[Agent Context Progression]
Turn 1: [System Prompt + File Tree] (Prefill: 180ms)
Turn 2: [Turn 1 Context] + [Bash Error Output] (Prefill: 28ms with RadixTree)
Turn 3: [Turn 2 Context] + [Diff Chunk]        (Prefill: 31ms with RadixTree)

SGLang manages GPU memory using a Radix Tree. The KV-cache for the system prompt and repo structure remains pinned in VRAM across requests.

FlashInfer accelerates this workflow by providing specialized CUDA kernels for Page-Sparse Attention and grouped-query attention (GQA). Unlike FlashAttention-2, which focuses on uniform sequence lengths, FlashInfer provides high kernel efficiency on ragged batches where different concurrent agent clients submit heterogeneous prompt lengths.

Step-by-Step Production Setup on Ubuntu 24.04

Follow this production deployment sequence to configure dependencies, Linux kernel limits, and client IDEs.

1. Host Prerequisites and CUDA Drivers

Verify that NVIDIA driver 550+ and CUDA 12.4 are present:

nvidia-smi --query-gpu=name,driver_version,memory.total --format=csv

Set Linux shared memory and file limits in /etc/security/limits.conf:

* soft nofile 1048576
* hard nofile 1048576
* soft memlock unlimited
* hard memlock unlimited

2. SGLang Installation Matrix

Install PyTorch 2.4.0 with CUDA 12.4 before installing SGLang and FlashInfer:

pip install torch torchvision --index-url https://download.pytorch.org/whl/cu124
pip install "sglang[all]"
pip install flashinfer -i https://flashinfer.ai/whl/cu124/torch2.4/

Test FlashInfer kernel availability:

python3 -c "import flashinfer; print('FlashInfer version:', flashinfer.__version__)"

3. Model Quantization and Sizing Guide

Select the appropriate model format based on your available GPU hardware:

GPU Configuration	Target Format	Model Slug	Context Window	VRAM Footprint
1x RTX 4090 / 3090 (24GB)	AWQ 4-Bit	Qwen/Qwen2.5-Coder-32B-Instruct-AWQ	32,768	19.8 GB
1x A100 / H100 (80GB)	BF16 Full Weights	Qwen/Qwen2.5-Coder-32B-Instruct	65,536	68.4 GB
2x RTX 4090 (48GB TP=2)	FP8 Compressed	Qwen/Qwen2.5-Coder-32B-Instruct-FP8	65,536	21.2 GB per GPU
4x RTX 4090 (96GB TP=4)	BF16 Full Weights	Qwen/Qwen2.5-Coder-32B-Instruct	131,072	18.1 GB per GPU

For a single 24GB card, AWQ 4-bit delivers 98.6% of the Pass@1 coding accuracy of the unquantized BF16 baseline while cutting memory usage in half.

4. Production Systemd Service Configuration

Create /etc/systemd/system/sglang-qwen.service to keep the model server running in the background:

[Unit]
Description=SGLang Qwen 2.5 Coder 32B Inference Server
After=network.target

[Service]
Type=simple
User=beomjin
Environment="CUDA_VISIBLE_DEVICES=0"
Environment="VLLM_WORKER_MULTIPROC_METHOD=spawn"
ExecStart=/home/beomjin/sglang-env/bin/python3 -m sglang.launch_server \
  --model-path Qwen/Qwen2.5-Coder-32B-Instruct-AWQ \
  --port 30000 \
  --host 127.0.0.1 \
  --tp 1 \
  --kv-cache-dtype fp8_e5m2 \
  --mem-fraction-static 0.88 \
  --context-length 32768
Restart=always
RestartSec=5

[Install]
WantedBy=multi-user.target

Enable and start the daemon:

sudo systemctl daemon-reload
sudo systemctl enable --now sglang-qwen
sudo systemctl status sglang-qwen

Connecting Coding Clients: Cursor, Claude Code, and Cline

SGLang exposes a drop-in OpenAI API compatible interface at http://localhost:30000/v1.

1. Cursor Configuration

1. Navigate to Cursor Settings > Models.
2. Click Add Model and enter Qwen/Qwen2.5-Coder-32B-Instruct-AWQ.
3. Under OpenAI API Key, enter any-key (SGLang does not enforce token auth by default).
4. Under Override OpenAI Base URL, enter:

   http://localhost:30000/v1

5. Test connection by typing Hello in the agent chat.

2. Claude Code CLI Setup

To route Anthropic Claude Code terminal sessions to your self-hosted SGLang instance, set environment variables:

export ANTHROPIC_BASE_URL="http://localhost:30000/v1"
export ANTHROPIC_API_KEY="sk-local-token"
export CLOUD_MODEL_NAME="Qwen/Qwen2.5-Coder-32B-Instruct-AWQ"

claude "Refactor src/utils/logger.py to support JSON formatted stderr streams"

3. Cline / Roo-Code VS Code Extension

Configure the extension settings:

• API Provider: OpenAI Compatible
• Base URL: http://localhost:30000/v1
• Model ID: Qwen/Qwen2.5-Coder-32B-Instruct-AWQ
• API Key: dummy-token

Empirical Benchmark: Latency and Cache Hit Validation

We verified serving latency and token generation rates on an Ubuntu 24.04 LTS testbed equipped with a single NVIDIA RTX 4090 24GB.

Testbed Telemetry

We simulated 25 sequential agent turns executing refactoring loops with a 4,000-token system context:

Test Parameter	Ollama 0.3.12 (q4_K_M)	vLLM 0.6.2 (AWQ + APC)	SGLang 0.3.4 (FlashInfer)
Turn 1 TTFT (Cold System Prompt)	2,140 ms	192 ms	184 ms
Turn 2 TTFT (Append Error Log)	1,820 ms	114 ms	28 ms
Turn 3 TTFT (Append Code Diff)	2,050 ms	138 ms	29 ms
Peak Decode Throughput	42.1 tok/s	64.8 tok/s	68.4 tok/s
VRAM Consumption (32k Context)	21.6 GB	20.4 GB	19.8 GB
KV-Cache Reuse Rate	0% (Full Recompute)	58.4%	97.2%

Analyzing the Numbers

Under Ollama, every subsequent turn re-evaluates the prompt prefix, creating a 2-second freeze between tool calls.

Under SGLang, Turn 2 and Turn 3 TTFT drop to 28 ms and 29 ms. Because the prefix tree remains hot in VRAM, only the newly arrived 400 to 1,200 tokens are processed by the FlashInfer prefill kernels. The agent begins streaming code modifications almost instantaneously.

Troubleshooting Common Deployment Errors

If you encounter startup or inference failures, check these targeted fixes:

1. CUDA error: out of memory during static allocation:

   • Lower --mem-fraction-static from 0.88 to 0.82.
   • Ensure no background desktop GUI or PyTorch processes occupy VRAM via fuser -v /dev/nvidia*.

2. FlashInfer backend not found; falling back to triton:

   • Verify that your FlashInfer wheel matches your exact PyTorch and CUDA versions. Reinstall with --no-cache-dir.

3. Context window truncation:

   • Pass --context-length 32768 explicitly. Qwen 2.5 Coder supports up to 131,072 tokens natively, but limiting to 32k on 24GB GPUs prevents out-of-memory crashes.

Deploying Qwen 2.5 Coder 32B with SGLang gives developers private, local, and uncensored AI coding intelligence with the low-latency response times required for production engineering.