Deploy Gemma 4 31B-it in One Click: 4-bit, 8-bit, 16-bit Production API

Gemma 4 31B is Google’s high-quality 31B-class instruction model — designed to deliver strong reasoning, coding, multilingual understanding, and reliable instruction-following while remaining lighter and more cost-efficient than larger 70B-class models. With 4-bit or AWQ-style quantization, it can be deployed on modern GPUs with lower VRAM requirements, making it a strong quality-per-dollar choice for production AI applications.

Where is Gemma 4 31B useful for you?

Internal coding copilot — strong code generation, debugging, refactoring, and explanation capabilities for developer workflows.

RAG over private documents — good instruction-following helps keep answers grounded in retrieved context from your internal knowledge base.

Structured data extraction — reliable for JSON outputs, summaries, classifications, and schema-based extraction from documents, forms, invoices, and product data.

Multilingual support automation — useful for handling multilingual customer conversations, support tickets, and documentation workflows.

Multi-step agent workflows — capable of planning, tool calls, task decomposition, and workflow automation with lower serving costs than larger models.

Enterprise chat assistants — ideal for internal assistants, customer support bots, technical Q&A, documentation search, and domain-specific copilots.

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 Gemma 4 31B-it 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

Step 1: Register on platform

• 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

• On the Dashboard, click "Deploy Model"

• Select your model to deploy from the catalogue

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.

• 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

Gemma 4 31B is inference-friendly, but your experience depends on VRAM, context length, and precision.

• Recommended minimum: 32 GB VRAM

• Good baseline: 48 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

• On-Demand 15 : You get billed in increments of 15-minutes

• On-Demand 30 : You get billed in increments of 30-minutes. Choose this as it's cheaper on a per-minute basis.

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 Gemma 4 31B 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": "gemma-4-31b", \
    "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.