Run AI Models Smarter, Faster, and Cheaper.
Run AI Models Smarter, Faster, and Cheaper.
Accelerated Inference. Intelligent Compute.
Trusted by some of the biggest companies
pip install agnitra | npm install agnitra
Capture telemetry.
Runtime latency, memory, kernel traces, and shapes are gathered skillfully using our telemetry_collector module.
Capture telemetry.
Runtime latency, memory, kernel traces, and shapes are gathered skillfully using our telemetry_collector module.
Extract IR graph.
torch.fx IR and annotated telemetry are processed automatically, streamlining the model pipeline.
Extract IR graph.
torch.fx IR and annotated telemetry are processed automatically, streamlining the model pipeline.
Extract IR graph.
torch.fx IR and annotated telemetry are processed automatically, streamlining the model pipeline.
Optimize & patch.
LLM + RL agents propose strategies; custom Triton/CUDA kernels boost runtime performance with zero code changes.
Optimize & patch.
LLM + RL agents propose strategies; custom Triton/CUDA kernels boost runtime performance with zero code changes.
Engineered for ROI
Agnitra unlocks AI performance without new hardware — making it a deflationary force in AI compute.
Engineered for ROI
Agnitra unlocks AI performance without new hardware — making it a deflationary force in AI compute.
+20–40% More Tokens/sec
RT telemetry -> optimized kernels
+20–40% More Tokens/sec
RT telemetry -> optimized kernels
+20–40% More Tokens/sec
RT telemetry -> optimized kernels
15–35% Lower Latency
Minimize tail latency for LLM
15–35% Lower Latency
Minimize tail latency for LLM
15–35% Lower Latency
Minimize tail latency for LLM
25–40% GPU Cost Reduction
fewer GPUs → instant cost savings
25–40% GPU Cost Reduction
fewer GPUs → instant cost savings
25–40% GPU Cost Reduction
fewer GPUs → instant cost savings
TELEMETRY ENGINE
Real-time model telemetry insights.
Agnitra automatically captures latency, memory, kernel timings, and runtime signals to build a complete optimization profile for every model you run.
Operator-level performance breakdown (matmul, layernorm, conv, etc.)
Operator-level performance breakdown (matmul, layernorm, conv, etc.)
Operator-level performance breakdown (matmul, layernorm, conv, etc.)
Hardware-aware telemetry for NVIDIA, AMD, and custom accelerators
Hardware-aware telemetry for NVIDIA, AMD, and custom accelerators
Hardware-aware telemetry for NVIDIA, AMD, and custom accelerators
Automatic bottleneck detection for slow or memory-heavy layers
Automatic bottleneck detection for slow or memory-heavy layers
Automatic bottleneck detection for slow or memory-heavy layers
Layer-wise latency and memory profiling
Layer-wise latency and memory profiling
Layer-wise latency and memory profiling
LLM OPTIMIZER
AI-driven optimization suggestions, instantly.
Agnitra’s LLM agent analyzes your telemetry and IR graph to recommend faster, smarter kernel strategies—no manual tuning required.
Auto-generated optimization hints for every layer
Auto-generated optimization hints for every layer
Auto-generated optimization hints for every layer
LLM-powered tiling, fusion, and memory strategy suggestions
LLM-powered tiling, fusion, and memory strategy suggestions
LLM-powered tiling, fusion, and memory strategy suggestions
Understands PyTorch, ONNX, and custom operator patterns
Understands PyTorch, ONNX, and custom operator patterns
Understands PyTorch, ONNX, and custom operator patterns
Improves with each model through feedback loops
Improves with each model through feedback loops
Improves with each model through feedback loops
RL AUTOTUNING ENGINE
Smarter performance through reinforcement learning.
Agnitra’s RL engine automatically experiments with tile sizes, fusion patterns, and kernel parameters to achieve the highest possible performance for your model on your hardware.
PPO-based performance tuning loops
PPO-based performance tuning loops
PPO-based performance tuning loops
Automatic search for optimal tile, block, and kernel parameters
Automatic search for optimal tile, block, and kernel parameters
Automatic search for optimal tile, block, and kernel parameters
Hardware-specific optimization for NVIDIA, AMD, and custom accelerators
Hardware-specific optimization for NVIDIA, AMD, and custom accelerators
Hardware-specific optimization for NVIDIA, AMD, and custom accelerators
Self-improving feedback loop that learns from every run
Self-improving feedback loop that learns from every run
Self-improving feedback loop that learns from every run
PERFORMANCE MONITORING
Deep visibility into every model's performance.
Agnitra gives you real-time, layer-level insights into latency, memory usage, and kernel execution so you can understand exactly where your models slow down.
Live latency and memory tracking for every layer
Live latency and memory tracking for every layer
Live latency and memory tracking for every layer
Bottleneck detection with visual IR heatmaps
Bottleneck detection with visual IR heatmaps
Bottleneck detection with visual IR heatmaps
Before/after performance comparison and benchmarking
Before/after performance comparison and benchmarking
Before/after performance comparison and benchmarking
GPU utilization, runtime traces, and kernel-level analytics
GPU utilization, runtime traces, and kernel-level analytics
GPU utilization, runtime traces, and kernel-level analytics
One-click model optimization
Instantly boost performance with a single CLI or SDK command. No manual tuning required.
One-click model optimization
Instantly boost performance with a single CLI or SDK command. No manual tuning required.
One-click model optimization
Instantly boost performance with a single CLI or SDK command. No manual tuning required.
Automatic graph cleanup
Agnitra removes redundant ops, unnecessary casts, and sub-optimal patterns before generating kernels.
Automatic graph cleanup
Agnitra removes redundant ops, unnecessary casts, and sub-optimal patterns before generating kernels.
Automatic graph cleanup
Agnitra removes redundant ops, unnecessary casts, and sub-optimal patterns before generating kernels.
Safe fallback execution
Automatically reverts to baseline kernels if an optimization doesn’t pass correctness checks.
Safe fallback execution
Automatically reverts to baseline kernels if an optimization doesn’t pass correctness checks.
Safe fallback execution
Automatically reverts to baseline kernels if an optimization doesn’t pass correctness checks.
Hardware-specific tuning
Agnitra adapts optimizations to each GPU architecture—A100, H100, MI250, Tenstorrent, and more.
Hardware-specific tuning
Agnitra adapts optimizations to each GPU architecture—A100, H100, MI250, Tenstorrent, and more.
Hardware-specific tuning
Agnitra adapts optimizations to each GPU architecture—A100, H100, MI250, Tenstorrent, and more.
Customers see instant results.
“Agnitra represents the next evolution of AI infrastructure. The idea that runtime optimization can be adaptive, telemetry-driven, and model-agnostic is transformative. Any team serious about scaling LLMs should be using this.”
Dr. Adrian Wu
Former Distinguished Engineer, Google DeepMind
“Switching to Agnitra gave us a 28% speedup on Llama-3 overnight. No model rewrites, no refactoring — just pure performance uplift.”
Jason Reed
Principal ML Engineer, CloudMind
“We’ve experimented with custom kernels for years — Agnitra generated better ones in minutes. This is the new standard for model performance.”
Sarah Ito
Director of Research Engineering, QuantML
“Switching to Agnitra gave us a 28% speedup on Llama-3 overnight. No model rewrites, no refactoring — just pure performance uplift.”
Jason Reed
Principal ML Engineer, CloudMind
“Agnitra’s RL tuning engine found kernel configurations our team never would’ve tried. The performance gain paid for itself within the first week.”
Elena García
GPU Optimization Lead, VisionForge AI
“Agnitra is the first tool that understands hardware diversity. The fact that it optimizes for H100, MI250, and Tenstorrent without any friction is game-changing.”
Raj Kulkarni
Compiler Architect, TensorCompute
“Agnitra’s RL tuning engine found kernel configurations our team never would’ve tried. The performance gain paid for itself within the first week.”
Elena García
GPU Optimization Lead, VisionForge AI
“Our inference bill dropped by 22%. Agnitra is now a default part of our deployment pipeline across all clusters.”
Michael Tan
Head of AI Infrastructure, Horizon Robotics
“This is the future of performance engineering. Our models run faster, cheaper, and more reliably — Agnitra does the heavy lifting for us.”
Lena Wavrik
CTO, Sigmoid Systems
“Our inference bill dropped by 22%. Agnitra is now a default part of our deployment pipeline across all clusters.”
Michael Tan
Head of AI Infrastructure, Horizon Robotics
“Switching to Agnitra gave us a 28% speedup on Llama-3 overnight. No model rewrites, no refactoring — just pure performance uplift.”
Jason Reed
Principal ML Engineer, CloudMind
“We’ve experimented with custom kernels for years — Agnitra generated better ones in minutes. This is the new standard for model performance.”
Sarah Ito
Director of Research Engineering, QuantML
“Switching to Agnitra gave us a 28% speedup on Llama-3 overnight. No model rewrites, no refactoring — just pure performance uplift.”
Jason Reed
Principal ML Engineer, CloudMind
“Agnitra’s RL tuning engine found kernel configurations our team never would’ve tried. The performance gain paid for itself within the first week.”
Elena García
GPU Optimization Lead, VisionForge AI
“Agnitra is the first tool that understands hardware diversity. The fact that it optimizes for H100, MI250, and Tenstorrent without any friction is game-changing.”
Raj Kulkarni
Compiler Architect, TensorCompute
“Agnitra’s RL tuning engine found kernel configurations our team never would’ve tried. The performance gain paid for itself within the first week.”
Elena García
GPU Optimization Lead, VisionForge AI
“Our inference bill dropped by 22%. Agnitra is now a default part of our deployment pipeline across all clusters.”
Michael Tan
Head of AI Infrastructure, Horizon Robotics
“This is the future of performance engineering. Our models run faster, cheaper, and more reliably — Agnitra does the heavy lifting for us.”
Lena Wavrik
CTO, Sigmoid Systems
“Our inference bill dropped by 22%. Agnitra is now a default part of our deployment pipeline across all clusters.”
Michael Tan
Head of AI Infrastructure, Horizon Robotics
“Switching to Agnitra gave us a 28% speedup on Llama-3 overnight. No model rewrites, no refactoring — just pure performance uplift.”
Jason Reed
Principal ML Engineer, CloudMind
“We’ve experimented with custom kernels for years — Agnitra generated better ones in minutes. This is the new standard for model performance.”
Sarah Ito
Director of Research Engineering, QuantML
“Switching to Agnitra gave us a 28% speedup on Llama-3 overnight. No model rewrites, no refactoring — just pure performance uplift.”
Jason Reed
Principal ML Engineer, CloudMind
“Agnitra’s RL tuning engine found kernel configurations our team never would’ve tried. The performance gain paid for itself within the first week.”
Elena García
GPU Optimization Lead, VisionForge AI
“Agnitra is the first tool that understands hardware diversity. The fact that it optimizes for H100, MI250, and Tenstorrent without any friction is game-changing.”
Raj Kulkarni
Compiler Architect, TensorCompute
“Agnitra’s RL tuning engine found kernel configurations our team never would’ve tried. The performance gain paid for itself within the first week.”
Elena García
GPU Optimization Lead, VisionForge AI
“Our inference bill dropped by 22%. Agnitra is now a default part of our deployment pipeline across all clusters.”
Michael Tan
Head of AI Infrastructure, Horizon Robotics
“This is the future of performance engineering. Our models run faster, cheaper, and more reliably — Agnitra does the heavy lifting for us.”
Lena Wavrik
CTO, Sigmoid Systems
“Our inference bill dropped by 22%. Agnitra is now a default part of our deployment pipeline across all clusters.”
Michael Tan
Head of AI Infrastructure, Horizon Robotics
Frequently asked questions.
Frequently asked questions.
What is Agnitra AI?
How do I integrate Agnitra into my workflow?
Which frameworks and hardware does Agnitra support?
Will I need to rewrite my model or restructure my code?
What kind of performance improvements should I expect?
How does the RL tuner work behind the scenes?
What about data privacy and model confidentiality?
How does Agnitra differ from traditional compilers (like TensorRT or XLA)?
What is the pricing structure for Agnitra?
Where can I find more technical documentation and guides?
What is Agnitra AI?
How do I integrate Agnitra into my workflow?
Which frameworks and hardware does Agnitra support?
Will I need to rewrite my model or restructure my code?
What kind of performance improvements should I expect?
How does the RL tuner work behind the scenes?
What about data privacy and model confidentiality?
How does Agnitra differ from traditional compilers (like TensorRT or XLA)?
What is the pricing structure for Agnitra?
Where can I find more technical documentation and guides?
What is Agnitra AI?
How do I integrate Agnitra into my workflow?
Which frameworks and hardware does Agnitra support?
Will I need to rewrite my model or restructure my code?
What kind of performance improvements should I expect?
How does the RL tuner work behind the scenes?
What about data privacy and model confidentiality?
How does Agnitra differ from traditional compilers (like TensorRT or XLA)?
What is the pricing structure for Agnitra?
Where can I find more technical documentation and guides?
Make Your Models Perform. Run at their full potential.
Join waitlist
Make Your Models Perform. Run at their full potential.
Join waitlist
Make Your Models Perform. Run at their full potential.
Join waitlist