Yes. io.net is optimized for large language model (LLM) inference at scale, supporting deployment from 1 to 100+ GPUs with auto-scaling capabilities. The platform runs vLLM, TensorRT-LLM, and batched inference frameworks out of the box, delivering 75% lower costs compared to AWS while maintaining comparable latency and throughput.

For production LLM inference workloads, io.net offers several deployment options: single-GPU endpoints for small models (RTX 4090 at $0.18/hr for 7B-13B models), multi-GPU clusters for large models (8x A100 for 70B+ models), and auto-scaling clusters that spin up additional capacity based on request volume. All deployments support popular serving frameworks including vLLM for high-throughput batching, TensorRT-LLM for optimized NVIDIA inference, and Text Generation Inference (TGI) from HuggingFace.

The cost advantage is substantial: serving a Llama 3 70B model on io.net using 4x A100 GPUs costs approximately $4.40/hour compared to AWS SageMaker's $14-16/hour for similar performance. For high-traffic applications processing millions of tokens per day, this translates to $7,000-10,000 in monthly savings per deployment.

Technical Specifications

Supported Models:
- GPT-style models (Llama 3, Mixtral, Qwen)
- Code models (StarCoder, CodeLlama)
- Vision-language models (LLaVA, Qwen-VL)
- Custom fine-tuned models

Performance Characteristics:
- Latency: 50-100ms time-to-first-token, 20-40 tokens/sec throughput
- Batching: Automatic continuous batching with vLLM
- Concurrency: Handle 100+ concurrent requests per GPU
- Quantization: Support for INT8, INT4, GPTQ, AWQ

Scaling Options:
- Horizontal: Add GPUs to increase throughput (linear scaling up to 8+ GPUs)
- Auto-scaling: Configure min/max replicas based on request queue depth
- Load balancing: Built-in request distribution across GPU nodes
- Region selection: Deploy in multiple regions for global low-latency serving

Why io.net for LLM Inference

  1. Cost-Efficient Serving: RTX 4090 GPUs at $0.18/hr provide excellent price/performance for 7B-13B models, while H100 GPUs at $1.49-2.20/hr offer the fastest inference for 70B+ models—all 50-70% cheaper than hyperscaler pricing.
  2. Instant Availability: No waitlists or capacity reservations. Spin up inference endpoints in under 2 minutes, scale from 1 to 100+ GPUs on demand.
  3. Production-Ready Infrastructure: Built-in monitoring, automatic health checks, GPU failover, and persistent storage for model weights.
  4. Framework Flexibility: Bring your own inference stack (vLLM, TensorRT-LLM, TGI, LiteLLM) or use pre-configured Docker images.

Deployment Example

# Deploy Llama 3 70B with vLLM on 4x A100
io deploy --image vllm/vllm-openai:latest \
  --gpu A100 --count 4 \
  --env MODEL=meta-llama/Meta-Llama-3-70B-Instruct \
  --port 8000

# Auto-scaling configuration
io cluster create --name llm-prod \
  --gpu A100 --min-replicas 2 --max-replicas 10 \
  --scale-metric request-queue-depth \
  --scale-threshold 50

Ready to deploy LLM inference at scale? Start on io.net  and see 75% cost savings vs. AWS.