Qwen 3.5 27B is Alibaba's latest mid-size model — competitive with models 2–4x larger on coding and reasoning benchmarks. It fits on a single 48GB GPU at AWQ quantization, making it one of the best quality-per-dollar models available today
Where is Qwen-3.5 27B useful for you:
Internal coding copilot — beats models 2× its size on code benchmarks at a fraction of the cost
RAG over private documents — strong instruction-following keeps answers grounded in retrieved context
Structured data extraction — reliable JSON schema adherence for invoices, forms, and product data
Multilingual support automation — handles 29 languages natively, no separate model per region
Multi-step agent workflows — reasoning strong enough to maintain coherence across tool calls and planning steps
That being said, deploying it on a GPU server shouldn’t mean fighting CUDA versions, broken wheels, flash-attention 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
Observability: latency, logs, tokens/sec, GPU memory, error rate
Visit: https://hexgrid.cloud/
Login and create a billing profile
Add some money to your wallet: Start with $10 credit
Step 2: Choose your LLM for Deployment
Correctly set your LLM deployment options
- Model Precision: Select the precision level for the model weights. Lower-bit precision reduces GPU memory usage and can improve speed, while higher precision may preserve better output quality.
| Precision |
VRAM needed |
Quality |
Speed |
When to use |
| 4-bit |
~20GB |
Good |
Fastest |
Cost-sensitive, high volume |
| 8-bit |
~28GB |
Better |
Fast |
Best quality/cost balance |
| 16-bit |
~54GB |
Best |
Slower |
Maximum quality |
Throughput Requirements: Set how many requests the model should handle at the same time per GPU. Higher concurrency can improve throughput but may require more GPU memory.
Request Sizing: Choose the maximum number of tokens you need to process in a single request. Higher context windows are useful for large documents or multi-turn chats but increase memory usage.
Step 3: Choose the right GPU
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
Step 4: Choose GPU count and Datacenter
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.
Step 5: Choose pricing for your deployment
Hit Deploy !
It takes some time to find the GPU resources and allocate them for you.
After that provisioning of the selected model and the API starts, which can further take 5-15 mins depending on the size of model.
At last, you will see a "Model Ready" indicator which indicates that it's ready for use.
Check Deployment Logs
Check 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
}'
Congratulations! You are ready with your LLM endpoint.
![Qwen3.6-27B-FP8 on One RTX 6000 Ada: Fast TTFT, 668 tok/s Peak Throughput [Benchmark]](/_next/image?url=https%3A%2F%2Fcdn.hashnode.com%2Fuploads%2Fcovers%2F6a22b1a041d5b05f16273b50%2F8fd36dcb-515c-4f77-8071-9a1aedc1c2ed.png&w=3840&q=75)
