# Deploy Llama-3.3 70B in One Click: 4-bit, 8-bit, 16-bit Production API


Llama 3.3 70B is Meta’s latest high-quality 70B-class instruction model — designed to deliver strong reasoning, coding, multilingual understanding, and tool-use performance while remaining much more cost-efficient than larger frontier models. With 4-bit or AWQ-style quantization, it can be deployed on modern high-memory GPUs, making it a strong quality-per-dollar choice for production AI applications.

## **Where is Llama 3.3 70B useful for you?**

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

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

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

**Multilingual support automation** — handles multilingual customer conversations, support tickets, and documentation workflows without requiring a separate model per language.

**Multi-step agent workflows** — strong reasoning and instruction-following make it suitable for planning, tool calls, task decomposition, and workflow automation.

**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 **Llama 3.3 70B** 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/](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
    

![Choose model Llama 3.3 70B](https://cdn.hashnode.com/uploads/covers/6a22b1a041d5b05f16273b50/7c8108ea-96b4-451e-9b10-e291eeb5d829.png align="center")

### **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 | ~48GB | Good | Fastest | Cost-sensitive, high volume |
| 8-bit | ~80GB | Better | Fast | Best quality/cost balance |
| 16-bit | ~140GB | 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**

![Choose GPU for Llama 3.3 70B](https://docs.hexgrid.cloud:9443/deploy/common/step-2.png align="center")

> Llama-3.3 70B 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**

![](https://cdn.hashnode.com/uploads/covers/6a22b1a041d5b05f16273b50/d5dc5378-f781-4780-8eb5-1dfe69c38dcb.png align="center")

**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**

![](https://cdn.hashnode.com/uploads/covers/6a22b1a041d5b05f16273b50/23905180-791f-4058-9acc-3b939853da96.png align="center")

*   **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**

![Deployment Logs](https://docs.hexgrid.cloud:9443/deploy/common/logs.png align="center")

## **Check System Health**

![System Health](https://docs.hexgrid.cloud:9443/deploy/common/sys-health.png align="center")

## **OpenAI-compatible endpoint snippet**

Use your deployed Llama-3.3 70B endpoint with OpenAI-style clients.

```python
curl https://YOUR_ENDPOINT/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{ \
    "model": "llama-3.3-70b", \
    "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.