Lightning-FastLLM Serving
Production-grade inference infrastructure for any LLM. Deploy at scale with sub-100ms latency and 99.9% uptime.
Why Inference Infrastructure Matters
You have trained a great model. Now you need to serve it in production. This is where most teams struggle.
Slow Response Times
Users abandon slow AI apps. Every 100ms matters for user experience.
Our optimized inference delivers < 100ms latency
High Infrastructure Costs
Unoptimized serving wastes GPU resources and inflates cloud bills.
We reduce costs by 70-90% through optimization
Scaling Nightmares
Traffic spikes crash your service. Manual scaling is too slow.
Auto-scaling handles 0 to 10K+ requests/second
Complex Setup
Setting up production inference requires deep ML engineering expertise.
We handle all infrastructure and optimization
Production-Grade Components
Every layer of the stack optimized for performance, reliability, and scale.
Load Balancer
Distribute requests across multiple model instances for high availability
Model Server
Optimized inference engine with batching, caching, and quantization
Auto-Scaler
Dynamically scale model instances based on traffic and latency metrics
Cache Layer
Redis-based caching for repeated queries to reduce costs and latency
Monitoring Stack
Real-time observability with logs, metrics, and distributed tracing
API Gateway
Secure API endpoint with authentication, rate limiting, and usage tracking
What You Get
10x Faster Inference
We apply cutting-edge optimization techniques to make your models blazing fast without sacrificing accuracy.
TensorRT
NVIDIA optimized runtime with kernel fusion and precision calibration
Quantization
INT8/INT4 inference with minimal accuracy loss
Flash Attention
Memory-efficient attention mechanism for long sequences
Dynamic Batching
Automatically batch requests to maximize GPU utilization
KV Caching
Cache key-value pairs for autoregressive generation
Speculative Decoding
Use smaller model to predict tokens, verify with large model
Sub-100ms Latency
Optimized for speed at every layer
Scale to Millions of Requests
Handle traffic spikes automatically without manual intervention or downtime.
Horizontal Scaling
Add more GPU instances as traffic increases. Scale from 1 to 100+ instances automatically.
Vertical Scaling
Upgrade to more powerful GPUs (T4 → A100 → H100) based on workload demands.
Predictive Scaling
ML-based traffic prediction scales infrastructure before spikes hit.
Global Distribution
Deploy across multiple regions for low latency and high availability worldwide.
Scaling Metrics
Reduce Costs by 80%
Every optimization technique applied to minimize your infrastructure spend without sacrificing performance.
Dynamic Batching
Group multiple requests together to maximize GPU utilization and reduce idle time.
Quantization
Use INT8/INT4 precision for faster inference with 2-4x lower memory and compute costs.
KV Caching
Cache computed key-value pairs to avoid redundant computation in autoregressive generation.
Smart Routing
Route simple queries to smaller, cheaper models. Use large models only when needed.
Cost Comparison
Unoptimized Serving
- ✗No batching
- ✗Full precision (FP32)
- ✗No caching
Optimized with TensorBlue
- ✓Dynamic batching
- ✓INT8 quantization
- ✓KV caching enabled
Deploy Anywhere
Cloud, on-premise, or hybrid - we support all deployment models
Cloud Managed
Fully managed inference on AWS, GCP, or Azure with auto-scaling and zero-ops
- →No infrastructure management
- →Pay-per-request pricing
- →Auto-scaling included
- →Multi-region deployment
On-Premise
Deploy in your own data center with full control over hardware and data
- →Complete data privacy
- →Custom hardware
- →No data leaves premises
- →One-time setup cost
Hybrid
Combine cloud and on-premise for best of both worlds with intelligent routing
- →Flexible workload distribution
- →Disaster recovery
- →Cost optimization
- →Burst to cloud capability
All Model Formats Supported
Bring your model in any format - we optimize it for production
Enterprise GPU Infrastructure
Latest NVIDIA GPUs for maximum performance
NVIDIA H100
NVIDIA A100
NVIDIA L4
NVIDIA T4
24/7 Monitoring & Alerts
Full visibility into your inference infrastructure
Enterprise-Grade Security
SOC 2, HIPAA, and GDPR compliant infrastructure
Data Encryption
TLS 1.3 in transit, AES-256 at rest
API Authentication
API keys, OAuth 2.0, JWT tokens
Rate Limiting
Per-key limits, DDoS protection
Audit Logs
Full request logging and tracing
VPC Deployment
Isolated network environments
Compliance
SOC 2, HIPAA, GDPR ready
Real Performance Data
Benchmarks from actual deployments
Why TensorBlue?
How we compare to other solutions
Popular Use Cases
What you can build with fast inference
Chatbots & Virtual Assistants
Real-time conversational AI
Content Generation
Automated writing and creation
Code Completion
Developer productivity tools
Document Analysis
Extract insights from text
API Services
LLM-powered APIs
Batch Processing
High-throughput workloads
Flexible Pricing
Pay per request or monthly fixed pricing
Pay-Per-Request
- ✓No minimums
- ✓Auto-scaling
- ✓99.9% uptime SLA
Fixed Monthly
- ✓Dedicated GPUs
- ✓Priority support
- ✓Custom SLAs
Scalable Architecture
Built for reliability and performance
99.9% Uptime SLA
Production-grade reliability guarantee
Client Success
What our clients say
Reduced our inference costs by 75% while improving latency. Game changer for our AI product.
TensorBlue handles all infrastructure so we can focus on building features. Best decision we made.
Common Questions
What models do you support?
All major LLMs including GPT, Llama, Mistral, Falcon, and custom models in PyTorch, TensorFlow, or ONNX formats.
How fast can you scale?
We can scale from 1 to 100+ GPU instances in under 30 seconds with zero downtime.
What is the pricing model?
Choose between pay-per-request ($0.001 per 1K tokens) or fixed monthly pricing starting at $5K. Custom enterprise pricing available.
Do you offer SLAs?
Yes, we guarantee 99.9% uptime and P95 latency under 100ms for all production deployments.
Deploy Your Model at Scale
Fast, reliable, cost-effective inference