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
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 |
~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
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
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 Llama-3.3 70B 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": "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.
![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)
