club-3090 LLM Serving

Skill by ara.so — Daily 2026 Skills collection.

Community recipes for serving modern LLMs on RTX 3090 (24 GB) hardware. Supports vLLM, llama.cpp, and SGLang engines with validated Docker Compose configs exposing an OpenAI-compatible API on localhost:8020. Currently ships Qwen3.6-27B configs for 1× and 2× cards.

Engine Decision Matrix

SGLang is currently blocked on Qwen3.6-27B — see models/qwen3.6-27b/sglang/README.md.

Prerequisites

- 1× or 2× NVIDIA RTX 3090 (24 GB each)
- Linux (Ubuntu 22.04+ recommended)
- Docker + NVIDIA Container Toolkit
- NVIDIA driver 580.x+
- ~30 GB free disk per model

Installation & Setup

1. Clone the repo

git clone https://github.com/noonghunna/club-3090.git
cd club-3090

2. Download and verify a model

# Downloads model weights, verifies SHA, clones Genesis patches
bash scripts/setup.sh qwen3.6-27b

3. Launch (interactive wizard)

bash scripts/launch.sh
# Wizard prompts: engine → card count → workload → boots compose → verifies

4. Launch (non-interactive)

# Single card, chat-optimized
bash scripts/launch.sh --variant vllm/default

# Dual card, 262K context + vision
bash scripts/launch.sh --variant vllm/dual

# Single card, 262K context, no prefill cliffs
bash scripts/launch.sh --variant llamacpp/default

# List all available variants
bash scripts/switch.sh --list

Key Scripts

Common script usage

# Switch variants without the wizard
bash scripts/switch.sh vllm/long-vision
bash scripts/switch.sh vllm/dual
bash scripts/switch.sh llamacpp/default

# Check runtime health
bash scripts/health.sh
# Output: KV cache %, MTP accept-length rate, recent TPS, error log tail

# Run canonical benchmark
bash scripts/bench.sh
# Runs narrative + code prompts, prints per-run TPS + averages

# Full functional verification after a switch
bash scripts/verify-full.sh

# Stress test (run before relying on long-context)
bash scripts/verify-stress.sh

Variant Names Reference

vllm/default          Single-card, chat-optimized (recommended first start)
vllm/dual             Dual-card, 262K ctx, vision, tools, MTP n=3
vllm/long-vision      Dual-card, long-context + vision workloads
vllm/turbo            Dual-card, 4 concurrent streams @ 262K
llamacpp/default      Single-card, full 262K, no prefill cliffs
llamacpp/65k          Single-card, 65K ctx (faster, more VRAM headroom)
llamacpp/dual         Dual-card llama.cpp recipe

API Usage (OpenAI-compatible, port 8020)

The server exposes a standard OpenAI-compatible API. Use the openai Python SDK pointed at localhost:8020.

Python — openai SDK

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8020/v1",
    api_key="ignored",  # local server, no auth needed
)

# Basic chat
response = client.chat.completions.create(
    model="qwen3.6-27b-autoround",
    messages=[{"role": "user", "content": "Explain KV cache in one paragraph."}],
    max_tokens=512,
)
print(response.choices[0].message.content)

Python — streaming

stream = client.chat.completions.create(
    model="qwen3.6-27b-autoround",
    messages=[{"role": "user", "content": "Write a Python quicksort."}],
    max_tokens=1024,
    stream=True,
)
for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)
print()

Python — raw requests (no SDK dependency)

import requests, json

payload = {
    "model": "qwen3.6-27b-autoround",
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"},
    ],
    "max_tokens": 200,
    "temperature": 0.7,
}

resp = requests.post(
    "http://localhost:8020/v1/chat/completions",
    headers={"Content-Type": "application/json"},
    json=payload,
    timeout=120,
)
resp.raise_for_status()
print(resp.json()["choices"][0]["message"]["content"])

Python — tool calling

tools = [
    {
        "type": "function",
        "function": {
            "name": "search_web",
            "description": "Search the web for recent information",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "Search query"},
                },
                "required": ["query"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="qwen3.6-27b-autoround",
    messages=[{"role": "user", "content": "What's the latest news on CUDA 13?"}],
    tools=tools,
    tool_choice="auto",
    max_tokens=512,
)

msg = response.choices[0].message
if msg.tool_calls:
    for call in msg.tool_calls:
        print(f"Tool: {call.function.name}")
        print(f"Args: {call.function.arguments}")

Python — long context (262K, use with llamacpp/default or vllm/dual)

# Load a large document
with open("large_codebase.txt") as f:
    document = f.read()

response = client.chat.completions.create(
    model="qwen3.6-27b-autoround",
    messages=[
        {"role": "user", "content": f"Summarize the architecture:\n\n{document}"},
    ],
    max_tokens=1024,
)
print(response.choices[0].message.content)

TypeScript / Node

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "http://localhost:8020/v1",
  apiKey: "ignored",
});

async function chat(prompt: string): Promise<string> {
  const response = await client.chat.completions.create({
    model: "qwen3.6-27b-autoround",
    messages: [{ role: "user", content: prompt }],
    max_tokens: 512,
  });
  return response.choices[0].message.content ?? "";
}

// Streaming in Node
async function streamChat(prompt: string): Promise<void> {
  const stream = await client.chat.completions.create({
    model: "qwen3.6-27b-autoround",
    messages: [{ role: "user", content: prompt }],
    max_tokens: 1024,
    stream: true,
  });
  for await (const chunk of stream) {
    process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
  }
  console.log();
}

curl — quick sanity check

curl -sf http://localhost:8020/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.6-27b-autoround",
    "messages": [{"role": "user", "content": "Capital of France?"}],
    "max_tokens": 200
  }' | jq '.choices[0].message.content'

curl — list available models

curl -sf http://localhost:8020/v1/models | jq '.data[].id'

Docker Compose Structure

Configs live under models/qwen3.6-27b/vllm/compose/. Example structure of a single-card compose:

# models/qwen3.6-27b/vllm/compose/default.yml (representative structure)
services:
  vllm:
    image: vllm/vllm-openai:v0.20.1rc1.dev16+g7a1eb8ac2
    runtime: nvidia
    environment:
      - NVIDIA_VISIBLE_DEVICES=0
      - CUDA_VISIBLE_DEVICES=0
    ports:
      - "8020:8000"
    volumes:
      - ${MODEL_PATH}:/models/qwen3.6-27b
      - ${PATCH_PATH}:/patches
    command: >
      --model /models/qwen3.6-27b
      --served-model-name qwen3.6-27b-autoround
      --tensor-parallel-size 1
      --max-model-len 65536
      --kv-cache-dtype fp8
      --speculative-model /models/qwen3.6-27b/mtp_head
      --num-speculative-tokens 3
      --port 8000

For dual-card, tensor-parallel-size 2 and NVIDIA_VISIBLE_DEVICES=0,1 are set, and max-model-len extends to 262144.

Connecting External Clients

Open WebUI

API Base URL: http://localhost:8020/v1
API Key:      (leave blank or type anything)
Model:        qwen3.6-27b-autoround

Cline / Cursor / Copilot-compatible tools

{
  "openai.baseURL": "http://localhost:8020/v1",
  "openai.apiKey": "local",
  "openai.model": "qwen3.6-27b-autoround"
}

LiteLLM proxy passthrough

# litellm_config.yaml
model_list:
  - model_name: qwen3.6-27b
    litellm_params:
      model: openai/qwen3.6-27b-autoround
      api_base: http://localhost:8020/v1
      api_key: ignored

Repo Layout Quick Reference

club-3090/
├── scripts/               Shared model-aware scripts (setup, launch, bench, health)
├── models/
│   └── qwen3.6-27b/
│       ├── vllm/
│       │   ├── compose/   Docker Compose files (all variants)
│       │   └── patches/   tolist_cudagraph, Marlin pad, Genesis pointer
│       ├── llama-cpp/
│       │   └── recipes/   Single-card 65K / 262K-max / dual recipes
│       └── sglang/        Blocked — watch list only
└── docs/
    ├── SINGLE_CARD.md     1× 3090 workload → config guide
    ├── DUAL_CARD.md       2× 3090 workload → config guide
    ├── HARDWARE.md        PCIe vs NVLink, power draw, card compatibility
    ├── GLOSSARY.md        TPS / KV / MTP / TP / prefill cliff definitions
    ├── CLIFFS.md          Prefill cliff root causes and fix landscape
    ├── COMPARISONS.md     Self-host vs cloud cost crossover analysis
    ├── UPSTREAM.md        Tracked upstream issues and PRs
    └── engines/           Per-engine deep dives (vLLM / llama.cpp / SGLang)

Troubleshooting

Server won't start — CUDA/driver error

# Check driver version (need 580.x+)
nvidia-smi --query-gpu=driver_version --format=csv,noheader

# Check NVIDIA Container Toolkit
docker run --rm --gpus all nvidia/cuda:12.0-base nvidia-smi

# Check GPU visibility
nvidia-smi -L

Out of VRAM / OOM on prefill

# Check current KV cache usage
bash scripts/health.sh

# Switch to a config with smaller max-model-len
bash scripts/switch.sh llamacpp/65k       # 65K ctx, more headroom
bash scripts/switch.sh llamacpp/default   # 262K but manages prefill correctly

Prefill cliff (vLLM hangs or errors on large prompts)

This is a known DeltaNet architecture issue on Qwen3.6-27B with vLLM. The llama.cpp route avoids it entirely:

bash scripts/switch.sh llamacpp/default
# Stress-test it:
bash scripts/verify-stress.sh

For vLLM workarounds, see models/qwen3.6-27b/INTERNALS.md and docs/CLIFFS.md.

MTP / speculative decoding not accepting tokens

bash scripts/health.sh
# Look for "MTP AL:" (accept-length) — should be > 1.0
# If AL ~= 1.0, speculative head may not be loaded correctly
# Check that Genesis patches were applied:
bash scripts/setup.sh qwen3.6-27b   # re-runs patch verification

Tool call returns 25K+ tokens and hangs

Known failure mode on vLLM with very large tool responses. Use llama.cpp:

bash scripts/switch.sh llamacpp/default
# llama.cpp handles 25K-token tool returns cleanly (stress-tested)

Switching variants leaves old container running

# switch.sh handles this, but if you ran docker compose manually:
docker compose -f models/qwen3.6-27b/vllm/compose/default.yml down
bash scripts/switch.sh vllm/dual

Check what variant is currently running

docker ps --format "table {{.Names}}\t{{.Image}}\t{{.Ports}}"

Performance Reference (Qwen3.6-27B)

Benchmark substrate: vLLM nightly 0.20.1rc1.dev16+g7a1eb8ac2 + Genesis v7.65 dev, llama.cpp 0d0764dfd, RTX 3090 sm_86 PCIe @ 230 W. Full per-run numbers in models/qwen3.6-27b/CHANGELOG.md.

Adding a New Model

# The repo structure is model-agnostic.
# New models follow the same pattern under models/<name>/:
mkdir -p models/glm-4.6/{vllm/compose,vllm/patches,llama-cpp/recipes,sglang}
# Add README.md, INTERNALS.md, CHANGELOG.md following qwen3.6-27b/ as template
# setup.sh and launch.sh are model-aware — add the model slug to their dispatch

bash scripts/setup.sh glm-4.6   # once scripts updated

Key Links

• docs/SINGLE_CARD.md — 1× 3090 workload → config → quick start
• docs/DUAL_CARD.md — 2× 3090 workload → config → quick start
• docs/HARDWARE.md — 4090/A6000 compatibility, NVLink notes
• docs/GLOSSARY.md — TPS, KV cache, MTP, TP, prefill cliff explained
• docs/COMPARISONS.md — self-host vs cloud cost crossover
• docs/CLIFFS.md — prefill cliff deep dive
• docs/UPSTREAM.md — upstream issues/PRs being tracked
• CONTRIBUTING.md — adding numbers, bug repros, new variants