Meta's Llama 4 represents the next evolution of the most widely deployed open-source language model family. With variants expected to range from 8B to 405B+ parameters, Llama 4 builds on the Llama 3 foundation with improved reasoning, longer context windows, and better multilingual capabilities. Deploying Llama 4 efficiently on cloud GPUs requires understanding its hardware requirements, choosing the right serving framework, and optimizing for your specific use case.
io.net provides the most cost-effective path to Llama 4 deployment. With H100 80GB GPUs at approximately $2.49/hr --- 40-60% less than AWS, GCP, or Azure --- you can serve Llama 4 at scale without the hyperscaler markup.
This guide covers deployment for every Llama 4 variant, from the lightweight 8B model to the massive 405B, including quantization strategies, serving framework selection, and production optimization.
Llama 4 Model Variants and GPU Requirements
Hardware Requirements by Model Size
| Model | Parameters | FP16 Size | Min VRAM | Recommended GPU (io.net) | Cost/hr |
|---|---|---|---|---|---|
| Llama 4 8B | 8B | 16 GB | 20 GB | 1x A100 40GB | $1.29 |
| Llama 4 8B | 8B (INT4) | 4 GB | 8 GB | 1x RTX 4090 | $0.49 |
| Llama 4 70B | 70B | 140 GB | 160 GB | 2x H100 80GB | $4.98 |
| Llama 4 70B | 70B (INT4) | 35 GB | 45 GB | 1x H100 80GB | $2.49 |
| Llama 4 405B | 405B | 810 GB | 900 GB | 12x H100 80GB | $29.88 |
| Llama 4 405B | 405B (INT4) | 203 GB | 250 GB | 4x H100 80GB | $9.96 |
Context Window Requirements
Llama 4 supports context windows up to 128K tokens (with some variants supporting 256K). KV cache memory grows linearly with context length:
| Context Length | KV Cache (70B, FP16) | Additional VRAM Needed |
|---|---|---|
| 4K tokens | ~2.5 GB | Minimal |
| 16K tokens | ~10 GB | Moderate |
| 64K tokens | ~40 GB | Significant |
| 128K tokens | ~80 GB | Requires extra GPU |
For long-context deployments, plan for additional GPU memory beyond the model weights.
Step-by-Step Deployment Guide
Option 1: vLLM (Recommended for Most Use Cases)
vLLM provides the best combination of ease of use, performance, and features for Llama 4 deployment.
# Install vLLM.7.0
pip install vllm>=0# Deploy Llama 4 70B on 2x H100 with io.net
python -m vllm.entrypoints.openai.api_server \
--model meta-llama/Llama-4-70B-Instruct \
--tensor-parallel-size 2 \
--max-model-len 32768 \
--gpu-memory-utilization 0.90 \
--enable-chunked-prefill \
--enable-prefix-caching \
--port 8000
Test with:
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "meta-llama/Llama-4-70B-Instruct",
"messages": [{"role": "user", "content": "Explain transformer architectures."}],
"max_tokens": 512,
"temperature": 0.7
}'
Option 2: TensorRT-LLM (Maximum Throughput)
For production deployments requiring maximum throughput:
# Build TensorRT engine for Llama 4 70Bfloat16
trtllm-build \
--model_dir meta-llama/Llama-4-70B-Instruct \
--output_dir ./engine_outputs \
--tp_size 2 \
--max_batch_size 64 \
--max_input_len 4096 \
--max_seq_len 8192 \
--dtype # Run inference server
python run.py --engine_dir ./engine_outputs --port 8000
TensorRT-LLM typically delivers 10-30% higher throughput than vLLM but requires a compilation step and has less flexibility.
Option 3: SGLang (Structured Generation)
For applications needing structured output (JSON, function calling):
python -m sglang.launch_server \
--model meta-llama/Llama-4-70B-Instruct \
--tp 2 \
--port 8000
Deploy on io.net
H100 GPUs at $2.49/hr. A100s at $1.89/hr. No commitments. Scale instantly.
Quantization Strategies for Llama 4
Performance vs Quality Trade-offs
| Quantization | Size (70B) | Throughput | Quality (MMLU) | Best For |
|---|---|---|---|---|
| FP16 | 140 GB | 1.0x baseline | 86.5 | Quality-critical applications |
| FP8 (H100 native) | 70 GB | 1.8x | 86.2 | Production inference |
| INT8 (GPTQ) | 70 GB | 1.7x | 86.0 | Good balance |
| INT4 (AWQ) | 35 GB | 2.8x | 84.5 | Cost-optimized serving |
| INT4 (GPTQ) | 35 GB | 2.5x | 84.1 | Wide framework support |
Applying AWQ Quantization
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
model = AutoAWQForCausalLM.from_pretrained("meta-llama/Llama-4-70B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-4-70B-Instruct")
quant_config = {"zero_point": True, "q_group_size": 128, "w_bit": 4}
model.quantize(tokenizer, quant_config=quant_config)
model.save_quantized("Llama-4-70B-AWQ")
Production Configuration
Recommended Configurations by Use Case
| Use Case | Model | Config | io.net Cost/hr | Throughput |
|---|---|---|---|---|
| Chat application | 70B INT4 | 1x H100 | $2.49 | ~3,500 tok/s |
| RAG pipeline | 70B FP16 | 2x H100 | $4.98 | ~2,800 tok/s |
| Code generation | 405B INT4 | 4x H100 | $9.96 | ~1,200 tok/s |
| Agentic workflow | 8B FP16 | 1x A100 | $1.89 | ~8,000 tok/s |
| Batch processing | 70B INT4 | 1x H100 | $2.49 | ~5,000 tok/s |
Scaling for Production Traffic
# Kubernetes deployment for auto-scaling Llama 4
apiVersion: apps/v1
kind: Deployment
metadata:
name: llama4-inference
spec:
replicas: 2
template:
spec:
containers:
- name: vllm
image: vllm/vllm-openai:latest
args:
- "--model"
- "meta-llama/Llama-4-70B-Instruct"
- "--tensor-parallel-size"
- "2"
- "--max-model-len"
- "16384"
resources:
limits:
nvidia.com/gpu: 2
Cost Comparison: Deploying Llama 4 Across Providers
Monthly Cost for Serving Llama 4 70B (24/7)
| Provider | Configuration | Monthly Cost | vs. io.net |
|---|---|---|---|
| io.net | 2x H100 80GB | $3,586 | Baseline |
| AWS SageMaker | ml.p5.48xlarge | $11,840 | +230% |
| Google Vertex AI | a3-highgpu-8g | $11,270 | +214% |
| Azure ML | ND H100 v5 | $11,880 | +231% |
| Together AI (API) | Per-token pricing | ~$8,000-$15,000 | Variable |
Self-Hosted (io.net) vs API Comparison
| Approach | Monthly Cost (1M requests) | Latency Control | Model Control |
|---|---|---|---|
| io.net self-hosted | $3,586 | Full | Full |
| OpenAI API (GPT-4o) | ~$15,000 | None | None |
| Together AI API | ~$8,000 | Limited | Limited |
| Anthropic API | ~$12,000 | None | None |
Self-hosting on io.net gives you full control over model configuration, fine-tuning, latency optimization, and data privacy at the lowest cost.
Fine-Tuning Llama 4 on io.net
LoRA Fine-Tuning Configuration
from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-4-70B-Instruct",
torch_dtype=torch.bfloat16,
device_map="auto",
)
lora_config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
model = get_peft_model(model, lora_config)
# Trainable params: ~0.1% of total
# GPU requirement: 2x H100 80GB ($4.98/hr on io.net)
Frequently Asked Questions
What is the cheapest way to deploy Llama 4 70B?
INT4 quantization on a single H100 80GB on io.net: $2.49/hr. This serves most production use cases with minimal quality loss.
How does Llama 4 compare to GPT-4o?
Llama 4 70B is competitive with GPT-4o on many benchmarks. The 405B variant exceeds GPT-4o on several tasks. The key advantage: you host it yourself, controlling cost, latency, and data privacy.
Can I fine-tune Llama 4 on io.net?
Yes. LoRA fine-tuning of Llama 4 70B requires 2x H100 ($4.98/hr). Full fine-tuning requires 8x H100 ($19.92/hr). Fine-tuning completes in hours to days depending on dataset size.
What context length should I configure?
Set max_model_len to the maximum you actually need, not the model maximum. Shorter context = more concurrent users per GPU.
Which serving framework should I use?
vLLM for most use cases. TensorRT-LLM for maximum throughput. SGLang for structured output. All work on io.net.
How do I handle model updates?
Deploy new model versions alongside existing ones. Route a percentage of traffic to the new version. Validate quality, then cut over.
Conclusion
Llama 4 deployment on io.net provides the best combination of cost, performance, and flexibility. Whether you are serving a chat application on a single H100 or running the 405B model across 4+ GPUs, io.net's pricing delivers 40-60% savings over hyperscalers with identical hardware.
Start with the 70B INT4 configuration on a single H100 ($2.49/hr) and scale from there based on your quality and throughput requirements.
Deploy Llama 4 on io.net today. Create your account and launch your first inference endpoint in minutes.