Model Serving
LLM Inference Servers
High-throughput LLM serving engine with PagedAttention for efficient memory management and continuous batching for optimal GPU utilization.
- PagedAttention for KV cache management
- Continuous batching for throughput
- Tensor and pipeline parallelism
- OpenAI-compatible API
- Support for quantization (AWQ, GPTQ)
- High-throughput LLM inference
- Production chatbot backends
- Large language model APIs
- Multi-user serving
Hugging Face's optimized inference server for LLMs with streaming, token streaming, and production-ready features.
- Token streaming support
- Quantization (bitsandbytes, GPTQ)
- Tensor parallelism
- Flash Attention integration
- Safetensors weight loading
- Hugging Face model deployment
- Streaming text generation
- Production LLM serving
- Multi-GPU inference
NVIDIA's high-performance LLM inference optimization SDK with custom kernels for maximum GPU efficiency.
- Custom CUDA kernels
- INT4/INT8/FP8 quantization
- In-flight batching
- Multi-GPU/multi-node support
- KV cache optimization
- Maximum performance inference
- NVIDIA GPU deployments
- Low-latency applications
- Cost-optimized serving
Local LLM serving made easy with bundled models, automatic quantization, and simple API for running models on consumer hardware.
- One-command model deployment
- Automatic quantization
- CPU and GPU support
- OpenAI-compatible API
- Model library with popular LLMs
- Local development
- Privacy-sensitive applications
- Offline inference
- Consumer hardware deployment
General ML Serving Frameworks
NVIDIA's production-ready inference server supporting multiple frameworks with dynamic batching and concurrent model execution.
- Multi-framework support (PyTorch, TensorFlow, ONNX)
- Dynamic batching
- Model ensembles
- Multi-model concurrent execution
- HTTP/gRPC/C++ APIs
- Multi-model serving
- High-throughput inference
- Computer vision pipelines
- Production ML systems
PyTorch's official model serving framework with built-in support for multi-model deployment and A/B testing.
- Native PyTorch integration
- Multi-model management
- Model versioning
- Custom preprocessing/postprocessing
- Metrics and logging
- PyTorch model deployment
- Computer vision serving
- NLP model APIs
- Research model deployment
Kubernetes-native serverless ML inference platform with auto-scaling, canary rollouts, and explainability.
- Serverless autoscaling
- Canary and blue/green deployments
- Explainability integration
- Multi-framework support
- Istio-based traffic management
- Kubernetes ML deployments
- Serverless inference
- Enterprise ML platforms
- Cloud-native applications
Scalable model serving framework built on Ray with Python-first API and support for model composition and business logic.
- Python-first API
- Model composition and chaining
- Dynamic request batching
- Distributed serving
- Integration with Ray ecosystem
- Multi-model pipelines
- Complex inference workflows
- Python-native deployments
- Distributed ML serving
Cloud Managed Inference
Fully managed deployment service with auto-scaling, A/B testing, and multi-model endpoints for production ML inference.
- Managed infrastructure
- Auto-scaling
- Multi-model endpoints
- Shadow testing
- Built-in monitoring
- Production model deployment on AWS
- Scalable inference endpoints
- Multi-variant testing
- Enterprise ML on AWS
Google Cloud's managed prediction service with custom containers, auto-scaling, and integrated monitoring.
- Custom container support
- Online and batch predictions
- Automatic scaling
- Model monitoring
- Explainable AI
- GCP ML deployments
- Scalable predictions
- Batch inference jobs
- AutoML deployments
Azure's managed endpoints for real-time and batch inference with MLflow integration and managed online endpoints.
- Managed online endpoints
- Batch endpoints
- MLflow integration
- Blue/green deployments
- Kubernetes integration
- Azure ML deployments
- Enterprise inference
- Batch scoring
- MLOps on Azure
Fully managed service for foundation models with serverless API access to models from Anthropic, AI21, Stability AI, and more.
- Serverless foundation model access
- No infrastructure management
- Custom model fine-tuning
- Guardrails and safety filters
- Pay-per-use pricing
- LLM applications
- Serverless AI
- Foundation model access
- Quick AI prototyping
Inference Serving Patterns
| Pattern | Description | Latency | Cost | Use Case |
|---|---|---|---|---|
| Real-time (Synchronous) | Immediate response to API request | Low (ms) | High | Chatbots, user-facing apps |
| Batch Inference | Process multiple requests together | High (minutes) | Low | Bulk processing, analytics |
| Streaming | Process data as it arrives | Medium | Medium | Real-time analytics, IoT |
| Asynchronous | Queue-based processing | Medium (seconds) | Medium | Background jobs, non-urgent |
| Edge Inference | On-device model execution | Very Low | Very Low | Mobile apps, IoT devices |
Inference Optimization Techniques
Model Optimization
- Quantization: Reduce precision (INT8, INT4)
- Pruning: Remove unnecessary weights
- Distillation: Train smaller model from larger
- Model Compression: GPTQ, AWQ, GGUF formats
Serving Optimization
- Batching: Process multiple requests together
- Caching: Cache frequent responses/embeddings
- Request Coalescing: Merge similar requests
- KV Cache: Reuse key-value calculations
Hardware Optimization
- GPU Acceleration: CUDA, TensorRT optimization
- Multi-GPU: Tensor/pipeline parallelism
- Specialized Hardware: TPUs, Inferentia, Trainium
- Flash Attention: Memory-efficient attention
Architecture Patterns
- Auto-scaling: Dynamic replica adjustment
- Load Balancing: Distribute requests efficiently
- Model Routing: Route to appropriate model size
- Canary Deployment: Gradual rollout