NVIDIA Dynamo vs HW-NAS: Part Two

 

Copyright: Sanjay Basu

My NVIDIA Dynamo article from few months back — 

Two Roads to Faster AI, One at Compile Time, One at Design Time

“If HW-NAS is the architect, Dynamo is the interior designer with a power drill and a tape measure.”

We’re in an era where “faster inference” isn’t just a nice-to-have. It’s survival. Whether you’re squeezing LLMs into GPUs with 40 GB memory or pushing them onto edge devices with one good core and a dream, the question is always the same:

How can we make these models run faster, cheaper, and more efficiently without losing accuracy?

Two major technologies tackle this, but from very different layers of the stack.

HW-NAS: Hardware-Aware Neural Architecture Search ← Discussed in Part One — https://medium.com/my-aiml/transformers-chips-and-the-ai-architects-dilemma-3b058c2c633d

NVIDIA Dynamo: Dynamic shape optimization via PyTorch compiler stack (part of TorchDynamo + TorchInductor) ← This week’s take

Let’s break them down, compare their goals, workflows, and trade-offs.

What They Are (in One Sentence)

HW-NAS

A meta-learning system that designs neural architectures tailored for specific hardware constraints during model creation time.

NVIDIA Dynamo

A PyTorch compiler runtime that optimizes Python-native model code into efficient kernels during model execution time.

High-Level Analogy

HW-NAS is like custom-building a racecar to win a specific track. You start from the blueprint stage, optimize the aerodynamics, weight, tires, etc. for that course and that weather.

Dynamo is like taking any car off the lot and super-tuning the engine on the fly, removing fluff, optimizing how fuel is injected, tweaking suspension, without changing the car’s overall shape.

Key Differences at a Glance

Copyright: Sanjay Basu

Under the Hood

HW-NAS

1. Uses AutoML techniques to search neural model designs.

2. Targets a specific hardware profile (e.g. H100, Jetson Orin, mobile NPUs).

3. Can reduce number of layers, attention heads, FFN widths, even use alternate blocks (e.g. MobileViT, tiny transformer variants).

4. Often trained on proxy tasks to reduce cost.

5. Returns a custom neural network optimized for:

– Accuracy

– Latency

– Memory

– Power

Example use: Designing a transformer small enough to run real-time on an autonomous drone’s chip, without needing post-hoc pruning or quantization.

NVIDIA Dynamo (with TorchInductor)

1. Part of PyTorch 2.x compiler stack.

2. Intercepts Python model code using tracing or static analysis.

3. Converts it to graph form, then compiles ahead of time using TorchInductor into fast kernels.

4. Supports:

– Dynamic shape handling

– Operator fusion

– Better memory layout

– Kernel scheduling for GPUs (CUDA)

Example use: Taking an off-the-shelf LLaMA-2 model and compiling it to run 2× faster on A100/H100 GPUs — — without modifying the model or code structure.

How Dynamo Actually Works

The magic of NVIDIA Dynamo lies in its multi-stage compilation pipeline that transforms eager PyTorch code into highly optimized kernels. Here’s what happens under the hood when you wrap your model with torch.compile():

Stage 1: Graph Capture Dynamo uses Python’s frame evaluation API to intercept bytecode execution. Unlike traditional tracing approaches that require running the model with sample inputs, Dynamo can capture the computation graph by analyzing the actual Python execution flow. This means it can handle complex control flow, dynamic shapes, and even data-dependent operations that would break traditional tracers.

Stage 2: Graph Analysis and Optimization Once captured, the graph undergoes several optimization passes:

1. Dead code elimination: Removes unused computations

2. Common subexpression elimination: Deduplicates repeated operations

3. Memory planning: Optimizes tensor lifetime and reuse

4. Layout optimization: Chooses optimal memory layouts for each operation

Stage 3: Backend Compilation TorchInductor, Dynamo’s default backend, generates highly optimized kernels:

1. Triton kernel generation: Creates custom GPU kernels for fused operations

2. Loop optimization: Vectorizes and parallelizes computation loops

3. Memory coalescing: Ensures optimal memory access patterns

4. Register allocation: Minimizes memory bandwidth requirements

This process happens at model load time, creating a compiled version that can be cached and reused across inference runs.

Real-World Performance Impact

Recent benchmarks from NVIDIA and the PyTorch team show impressive gains across different model types:

Large Language Models:

1. GPT-4 style models: 1.8–2.3× speedup on H100/H200 (still benchmaking B200/300)

2. T5-based models: 1.5–2.1× speedup with dynamic batching

3. BERT inference: 2.2–2.8× speedup on shorter sequences

Computer Vision Models:

1. ResNet variants: 1.4–1.9× speedup with optimized convolutions

2. Vision Transformers: 1.7–2.4× speedup through attention fusion

3. Object detection models: 1.3–1.8× speedup with NMS optimization

Generative Models:

1. Stable Diffusion: 1.6–2.2× speedup through U-Net optimization

2. GANs: 1.4–2.0× speedup with generator-discriminator fusion

These performance gains come with minimal engineering overhead. Often just a single line of code change.

Dynamic Shape Handling a.k.a. Dynamo’s Secret Weapon

One of Dynamo’s most powerful features is its ability to handle dynamic input shapes efficiently. Traditional compilation approaches either require fixed shapes or suffer significant overhead when shapes change. Dynamo solves this through several innovative techniques:

Shape Specialization: Dynamo automatically creates specialized kernels for common shape patterns. For example, if your model frequently processes sequences of lengths 128, 256, and 512, Dynamo will generate optimized kernels for each length while maintaining a fallback for arbitrary shapes.

Symbolic Shape Tracking: Instead of requiring concrete shape values, Dynamo can work with symbolic dimensions. This allows it to compile models that work with variable batch sizes, sequence lengths, or image dimensions without recompilation overhead.

Guard-Based Recompilation: When input shapes change significantly, Dynamo’s guard system triggers selective recompilation of only the affected subgraphs. This minimizes compilation overhead while maintaining optimal performance for new shape patterns.

Before and After Dynamo

Here’s what the performance transformation looks like in practice:

Before Dynamo (Standard PyTorch):

import torch
import torch.nn as nn
import time
class SimpleTransformer(nn.Module):
def __init__(self, d_model=512, nhead=8, num_layers=6):
super().__init__()
self.transformer = nn.Transformer(d_model, nhead, num_layers)
def forward(self, src, tgt):
return self.transformer(src, tgt)
model = SimpleTransformer()
src = torch.randn(100, 32, 512) # seq_len, batch, d_model
tgt = torch.randn(50, 32, 512)
# Timing eager execution
start = time.time()
for _ in range(100):
output = model(src, tgt)
eager_time = time.time() - start
print(f"Eager execution: {eager_time:.3f}s")

After Dynamo (Compiled):

# Same model definition…
# Compile with Dynamo
compiled_model = torch.compile(model, backend="inductor", mode="max-autotune")
# Timing compiled execution
start = time.time()
for _ in range(100):
output = compiled_model(src, tgt)
compiled_time = time.time() - start
print(f"Compiled execution: {compiled_time:.3f}s")
print(f"Speedup: {eager_time/compiled_time:.2f}×")

Typical results show 1.8–2.5× speedup for this transformer pattern on modern GPUs.

Advanced Dynamo Features

Custom Backends: While TorchInductor is the default backend, Dynamo’s architecture allows for custom backends targeting specific hardware or optimization strategies:

1. TensorRT backend: For maximum NVIDIA GPU performance

2. OpenVINO backend: For Intel hardware optimization

3. ONNX backend: For cross-platform deployment

4. Custom backends: For specialized accelerators or research

Compilation Modes: Dynamo offers several compilation modes to balance compilation time and runtime performance:

1. default: Standard optimizations with reasonable compile time

2. reduce-overhead: Focus on reducing Python overhead for small models

3. max-autotune: Aggressive optimization for maximum performance

4. max-autotune-no-cudagraphs: Maximum optimization without CUDA graphs

Memory Optimization: Advanced memory management features include:

1. Gradient checkpointing integration: Automatic checkpoint placement

2. Memory planning: Optimal tensor lifecycle management

3. Activation recomputation: Trading computation for memory in memory-constrained scenarios

When to Use Which?

Copyright: Sanjay Basu

Dynamo-Specific Use Cases

Production LLM Serving: Companies serving large language models at scale see immediate benefits from Dynamo compilation. A typical deployment scenario involves:

1. Models with varying sequence lengths (chat, completion, code generation)

2. Batch sizes that change based on load

3. Need for sub-second response times

4. Cost optimization through higher throughput per GPU

Research and Experimentation: Researchers benefit from Dynamo’s ability to accelerate model iteration:

1. Faster training loops for architecture experiments

2. Reduced compute costs during hyperparameter sweeps

3. Quick performance validation of model variants

4. Seamless integration with existing PyTorch workflows

Edge Deployment with Dynamic Requirements: Even in edge scenarios, Dynamo provides value when:

1. Input shapes vary based on sensor data or user interaction
2. Models need to adapt to different quality/speed tradeoffs
3. Deployment targets support PyTorch compilation (e.g., NVIDIA Jetson with recent PyTorch)

Dynamo Best Practices and Troubleshooting

Compilation Strategy:

# Start conservative, then optimize
model = torch.compile(model) # Default mode first
# If stable, try aggressive optimization
model = torch.compile(model, mode="max-autotune")
# For dynamic shapes, consider
model = torch.compile(model, dynamic=True)

Common Pitfalls:

1. Graph breaks: Avoid operations that can’t be traced (certain Python control flow)

2. Memory pressure: Compilation itself uses additional memory during model loading

3. Cold start: First few iterations may be slower due to kernel optimization

4. Debugging complexity: Compiled models are harder to debug and profile

Performance Profiling:

# Profile compilation overhead
with torch.profiler.profile() as prof:
compiled_model(input_data)
# Analyze kernel efficiency
torch._dynamo.config.log_level = logging.DEBUG

The Future of Dynamo

Upcoming Features:

1.  Improved dynamic shape support: Better handling of highly variable input dimensions
2. Cross-backend optimization: Seamless switching between compilation targets
3. Quantization integration: Native support for INT8/FP16 optimizations
4. Distributed compilation: Optimizations for multi-GPU and multi-node scenarios

Ecosystem Integration: The PyTorch ecosystem is rapidly evolving to take advantage of Dynamo:

1. HuggingFace Transformers: Native compilation support in recent versions
2. Lightning: Automatic Dynamo integration for training acceleration
3. TorchServe: Production serving with compiled models
4. Ray: Distributed inference with Dynamo-compiled models

Can They Work Together?

Absolutely.

Think of HW-NAS as selecting what model to use, and Dynamo as tuning how to execute that model efficiently.

A best-practice pipeline might look like:

1. Use HW-NAS to generate an efficient transformer optimized for your hardware (e.g., 12-layer, 6-head, 768-dim model).
2. Train the model.
3. Use NVIDIA Dynamo + TorchInductor to compile the inference code for fast deployment.
4. Apply quantization or other lightweight optimizations after.

In a sense, HW-NAS gets you the best ingredients, and Dynamo makes the kitchen prep faster.

Real-World Integration Pipeline

Step 1: Architecture Search with Hardware Constraints

# HW-NAS generates efficient architecture
hw_nas_config = {
"num_layers": 8, # Reduced from standard 12
"hidden_dim": 512, # Optimized for target GPU memory
"num_heads": 8, # Balanced for compute/memory
"ffn_ratio": 2.67, # Non-standard ratio for efficiency
}

Step 2: Model Training and Validation

# Train the HW-NAS optimized model
model = create_model_from_nas_config(hw_nas_config)
trained_model = train_model(model, training_data)

Step 3: Dynamo Compilation for Deployment

# Compile for production serving
production_model = torch.compile(
trained_model,
mode="max-autotune",
backend="inductor"
)

Step 4: Performance Validation

# Measure end-to-end performance
latency_metrics = benchmark_model(production_model, test_inputs)
memory_usage = profile_memory(production_model, test_inputs)

This combined approach often yields 3–5× total speedup compared to using standard architectures with eager execution.

Shared Philosophy, Different Layers

What ties them together is the philosophy of moving from static model design to dynamic, hardware-aware execution.

Both represent a larger shift in the AI world:

We’re no longer just asking “Can this model learn?”

We’re now asking “Can this model run well here, under these constraints?”

And that question is increasingly answered by a blend of:

1. Design-time intelligence (HW-NAS)
2. Compile-time optimization (Dynamo)
3. Runtime adaptation (dynamic batching, kernel caching, etc.)

The Convergence Trend

The boundary between design-time and compile-time optimization is becoming increasingly blurred. Future systems will likely feature:

Adaptive Architecture Selection: Models that can dynamically adjust their architecture based on runtime constraints, combining NAS principles with Dynamo’s compilation flexibility.

Hardware-Aware Compilation: Dynamo backends that consider not just performance but also power consumption, thermal constraints, and memory bandwidth limitations.

Cross-Layer Optimization: Systems that simultaneously optimize model architecture, quantization strategies, and execution kernels as a unified problem.

Continuous Learning Systems: Deployments that continuously refine both model architecture and compilation strategies based on real-world usage patterns.

Industry Adoption Patterns

Cloud Providers:

1. OCI, AWS, GCP, and Azure are integrating Dynamo compilation into their ML serving platforms. NeoClouds like CoreWeave, are also adopting NVIDIA Dynamo
2. Auto-scaling systems that consider compiled model performance characteristics
3. Cost optimization through improved GPU utilization

Model Serving Companies:

1. Inference services like Hugging Face Inference Endpoints adopting Dynamo by default
2. Custom backends for specialized hardware (TPUs, custom ASICs)
3. Integration with model optimization pipelines

Enterprise AI Teams:

1. Internal tooling for automatic model compilation and deployment
2. Performance monitoring systems that track compilation effectiveness
3. Cost optimization through reduced compute requirements

TL;DR

Copyright: Sanjay Basu

The Real Cost of Ignoring Either

If you’re shipping LLMs without Dynamo, you’re probably leaving 30–60% latency gains on the table.

If you’re designing new models without HW-NAS, you’re probably reinventing architectures that could be 3× smaller and just as accurate.

In 2025 and beyond, the smartest AI teams aren’t the ones with the biggest models. They’re the ones who understand:

Performance isn’t a layer. It’s a culture — — spanning design, compilation, and execution.

HW-NAS is your design-phase architect.

Dynamo is your execution-phase compiler.

Use both. Build fast. Run lean.

The Dynamo Imperative

For teams already committed to PyTorch, adopting Dynamo isn’t just an optimization. It’s a competitive necessity. The technology has matured from experimental to production-ready, with major tech companies reporting significant cost savings and performance improvements.

The Bottom Line:

1. If you’re running PyTorch models in production, you should be using torch.compile()
2. If you’re designing new models, consider how compilation will affect your architecture choices
3. If you’re optimizing for specific hardware, combine both approaches for maximum impact

The future of AI deployment isn’t about choosing between design-time and compile-time optimization. It’s about orchestrating both to create systems that are not just accurate, but blazingly fast and efficient.

Start with Dynamo today. Your GPUs (and your infrastructure budget) will thank you.