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Deploy Qwen 3.5 27B AWQ-4Bit or FP8 or FP16 in One Click (Production-Ready)

For a smoother start to your Qwen-3.5 deployment, remember that cold-start time depends on the GPU tier and model size.

Self-hosting an LLM shouldn’t mean fighting CUDA versions, broken wheels, flash-attn builds, OOMs, and “works locally, fails on the server”.

This page lets you deploy Qwen 3.5 27B on our GPU servers with a single click and get a production-ready, OpenAI-compatible API endpoint (with auth, logs, metrics, and sane defaults).

What you get

  • OpenAI-compatible endpoint (/v1/chat/completions, streaming supported)
  • Dedicated vLLM URL with HTTPS + API key for security
  • Optimisations for Context-length, Quantization, Concurrency
  • Observability: latency, logs, tokens/sec, GPU memory, error rate
Step-1: Choose your LLM for Deployment
Choose model Qwen 3.5 27B

Correctly set your LLM deployment options

  • Concurrency: Set how many requests the model should handle at the same time. Higher concurrency can improve throughput but may require more GPU memory.

  • Context length: Choose the maximum number of tokens the model can process in a single request. Longer context windows are useful for large documents or multi-turn chats but increase memory usage.

  • Quantization: Select the precision level for the model weights. Lower-bit quantization reduces GPU memory usage and can improve speed, while higher precision may preserve better output quality. Currently AWQ_4Bit and FP8 quantisations are supported.

Step-2: Choose the right GPU
Choose GPU for Qwen 3.5 27B

Qwen 3.5 27B is inference-friendly, but your experience depends on VRAM, context length, and precision.

  • Recommended minimum: 48 GB VRAM
  • Good baseline: 80 GB VRAM
  • High throughput / heavy batching: 80 GB + VRAM

Exact VRAM needs vary by runtime (vLLM/TGI), KV cache size, context length, and concurrency. That’s why our presets are designed to be safe by default.

Step-3: Choose Number of GPUs, Datacenter and Disk Size
Choose Number of GPUs, Datacenter and Disk Size

Number of GPUs

  • Choose the GPU count based on your model size and expected traffic. Larger models or higher concurrency usually need more GPUs.
  • Increasing GPUs can improve throughput and reduce latency, but it also increases deployment cost.

Datacenter

  • Select a datacenter close to your users to reduce network latency and improve response times.
  • Choose a region that meets your data residency, availability, and compliance requirements.

Disk size

  • Allocate enough disk space for model weights, tokenizer files, logs, and temporary runtime data.
  • Larger disks give more room for future model versions and checkpoints, but can increase storage cost.
Step-4: Choose pricing for your deployment
Choose pricing

Pricing example (simple mental model)

Your cost is basically:

GPU $/hour + storage + bandwidth + (optional) warm instances

Example scenarios:

  • Solo MVP: 1 GPU, no warm pool, low concurrency
  • Early startup: 1 GPU + warm instance + dashboards/logs
  • Production traffic: multiple replicas + autoscaling + tuned concurrency

If you share expected traffic (requests/day, tokens/request, target latency), we can recommend:

  • a preset (precision, context, concurrency)
  • expected tokens/sec and TTFT
  • an estimated $ / 1M tokens
Step-5 : Review and Hit Deploy!
Deploy

Currently our platform support LLM Deployment only through vLLM.

Check Deployment Logs
Deployment Logs
Check System Health
System Health
OpenAI-compatible endpoint snippet

Use your deployed Qwen-3.5 endpoint with OpenAI-style clients.

curl https://YOUR_ENDPOINT/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{ \
    "model": "qwen-3.5-27B", \
    "messages": [ \
      {"role": "system", "content": "You are a helpful assistant."}, \
      {"role": "user", "content": "Write a concise product description for my app."} \
    ], \
    "temperature": 0.7,
    "stream": true
  }'

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