Transformers, Chips, and the AI Architect’s Dilemma
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| Copyright: Sanjay Basu |
This is a two-part article, where I will explore and compare how HW-NAS (Hardware-Aware Neural Architecture Search) and NVIDIA Dynamo optimize Large Language Model Inference. Please refer to my NVIDIA Dynamo article [ Medium: https://medium.com/my-aiml/demystifying-nvidia-dynamo-inference-stack-2738d7de76f4 | Sanjaysays.com: https://www.sanjaysays.com/2025/03/demystifying-nvidia-dynamo-inference.html | LinkedIn Newsletter: https://www.linkedin.com/pulse/demystifying-nvidia-dynamo-inference-stack-sanjay-basu-phd-pgolc/ ]
Part one is all about Hardware-Aware Neural Architecture Search
A Deep Dive into Hardware-Aware NAS
How to teach your transformer to run like Usain Bolt on your GPU without torching your power bill — Yours truly!
An LLM that looks great on paper, but runs slower than a sloth doing calculus.
I’ve spent the better part of my career, since 2021, watching brilliant engineers build magnificent models. These architectural marvels would achieve state-of-the-art accuracy on benchmarks. They’d publish papers that made reviewers weep with joy. Then deployment day would arrive. The same model that sang in the lab would wheeze in production. Memory would spike. Latency would soar. The infrastructure team would send passive-aggressive Slack messages about the hyperscaler bills.
This pattern has become painfully familiar.
In the golden age of AI, we’ve gotten really good at designing ever-larger language models. Billions of parameters? No problem. Layers stacked like Jenga towers? Bring it on. But when it comes to deploying these brainchildren in the real world, on actual hardware, under actual constraints, we’re often left clutching our profiling tools and wondering, “Why is this thing so slow?”
The disconnect is staggering. I’ve witnessed teams celebrate a 2% accuracy improvement while ignoring the 300% increase in inference cost. They optimize for leaderboard glory while users abandon their apps because responses take twelve seconds. The irony cuts deep. We’re building intelligence that can write poetry and solve complex reasoning tasks, yet it can’t figure out how to run efficiently on the very machines we built it for.
Here’s the truth bomb: your model architecture isn’t just a neural design problem anymore. It’s a hardware economics problem.
This realization hit me during a consultation with a healthcare startup. They’d built an impressive diagnostic model. The accuracy was phenomenal. Medical experts praised its insights. There was just one problem. Running inference cost them $0.43 per patient query. At their projected scale, they’d burn through their Series A funding in six months just on compute costs. The model was brilliant but economically suicidal.
Welcome to the world of Hardware-Aware Neural Architecture Search, or as insiders call it with a mixture of reverence and PTSD: HW-NAS.
Because GPUs aren’t magic carpets! They have limits
The evolution has been remarkable to witness firsthand. Back in the quaint days of 2018, you could slap a BERT on a Tesla V100 and feel proud. Fast-forward to today, and the name of the game is scale and specialization. With models like LLaMA 4, GPT-5, and Gemini pushing the boundaries, the cost of running inference is now an operational line item CFOs actually care about.
I remember the first time a CFO called me directly about model costs. This wasn’t some technical review meeting. This was a panicked executive staring at a cloud bill that looked like a phone number. Their AI initiative had become their biggest operational expense after salaries. The conversation shifted from “How accurate is the model?” to “How can we make this sustainable?”
The hardware landscape itself has become a complex maze. Each new generation of accelerators brings unique characteristics (and I am not talking about the new GB200/300 or B200/300s). An H200 behaves differently from an A100, which behaves differently from a TPU v4. Memory hierarchies vary. Tensor core utilizations fluctuate. Even thermal throttling patterns differ between deployments.
Meanwhile, the deployment landscape has fragmented. You’re no longer just running inference in a cozy, overprovisioned datacenter. You’re targeting:
• On-device inferencing for mobile apps and edge AI.
• Token-by-token streaming with hard latency caps.
• Fine-tuned domain-specific transformers for low-power IoT.
• Massive batch inference on H100s and A100s in the cloud.
The complexity multiplies when you consider real-world constraints. A mobile deployment might have 2GB of available memory on a good day. An edge device could be running at 45°C ambient temperature in an industrial setting. Cloud deployments face noisy neighbor problems and varying network latencies.
Each of these environments has very different constraints — memory, latency, power, parallelism, even thermals.
The thermal issue deserves special attention. I’ve seen perfectly good models fail in production because nobody considered heat dissipation. One automotive client discovered their model would throttle after three minutes of continuous inference. The car’s cabin temperature would rise on sunny days, pushing the edge processor beyond its thermal limits. The model would slow to a crawl just when the driver needed it most.
And guess what? Your transformer doesn’t care. It’ll happily demand 40 GB of memory, stall your pipeline, and throttle your GPU kernel until smoke rises.
The arrogance of models is almost charming. They assume infinite memory, perfect parallelism, and zero latency. They make demands like divas. More attention heads. Deeper layers. Wider hidden dimensions. They never ask whether the hardware can actually deliver.
So, unless you want to throw compute at the problem like it’s 2020, you need a new trick.
What is HW-NAS, Really?
Let’s peel back the acronym like a well-burned GPU layer:
NAS — Neural Architecture Search
This is the part where a meta-learning system (or some poor grad student) explores thousands of model blueprints to discover the best-performing neural net.
The process fascinates me every time I see it in action. It’s like evolution but for tensors. You start with a population of models, mutate their architectures (number of layers, kernel sizes, attention heads, etc.), and select those that perform best on a given task. Over time, you “evolve” an optimal model.
I’ve watched these searches run for weeks. The computational poetry of it is mesmerizing. Models breed and mutate. Architectural patterns emerge and disappear. Sometimes you’ll see the algorithm rediscover known techniques. Other times it invents bizarre configurations that somehow work brilliantly.
The search process itself has evolved significantly. Early approaches were naive and expensive. They’d train each candidate model from scratch. A single search could consume millions of GPU hours. The carbon footprint was embarrassing. The cost was prohibitive.
Modern approaches are more sophisticated. Weight sharing reduces training time. Early stopping prevents wasted computation. Predictive models estimate performance without full training.
You can do this with:
• Or plain old grid search if you’re feeling retro
Each approach has its personality. Reinforcement learning feels elegant but can be unstable. Evolutionary algorithms are robust but computationally hungry. Gradient-based methods are fast but can get stuck in local optima. Grid search is reliable but laughably inefficient for large search spaces.
HW — Hardware-Aware
Here’s where the magic (and money-saving) happens.
Traditional NAS optimizes for accuracy alone. It’s like designing a race car without considering the track. Sure, you might build something fast, but will it handle the turns? Will it fit through the tunnels? Will the tires grip in the rain?
Hardware-aware NAS brings the track into the design process.
Instead of just optimizing for accuracy, you add hardware constraints into the search objective. Think:
• Latency (in milliseconds, not “feels fast enough”)
• Throughput (tokens/sec or batches/sec)
• Memory footprint (can it fit on 1 H100? Or do we need a prayer circle?)
• Power usage (important for mobile, embedded, and on-prem deployments)
The latency specification deserves special attention. I’ve seen too many teams specify latency in vague terms. “Fast enough for users” isn’t a constraint. “Under 100ms P99 latency” is. The precision matters because hardware behavior is non-linear. A model might run at 95ms consistently, then spike to 500ms when certain attention patterns trigger.
Power usage often gets overlooked until it’s too late. One client deployed a model to thousands of retail locations. Each inference drew 150W. Multiply that by deployment scale and operating hours. Their monthly power bill increased by $200,000. The model was accurate, but the economics were devastating.
The goal? Find the neural network that performs well and runs efficiently on your target hardware. No more blind faith in FLOPs per token.
A Gentle Walk Through a Hardware-Aware NAS Workflow
Let me share a real example from my practice. The details are sanitized, but the lessons are authentic.
Let’s say you’re building a domain-specific LLM for a healthcare app that runs on-device on a Snapdragon chip.
The constraints were brutal. The client wanted medical-grade accuracy on a processor designed for playing mobile games. The initial model they’d prototyped needed 8GB of memory and a cooling fan. The target device had 1.2GB available and passive cooling.
Here’s what HW-NAS looks like in the wild:
Step 1: Define Constraints
• Max inference latency: 50ms
• Max memory: 1.2GB
• Power budget: 2W
• Accuracy floor: 85% on your medical QA dataset
These numbers weren’t arbitrary. The 50ms latency came from user experience research. Beyond that threshold, users perceived the app as sluggish. The memory limit reflected real device constraints after OS overhead. The power budget ensured the phone wouldn’t overheat during extended use. The accuracy floor was the minimum viable performance for medical safety.
Setting these constraints requires deep collaboration. Engineers provide technical limits. Product managers define user requirements. Legal ensures compliance standards. Medical experts set safety thresholds.
Step 2: Set Search Space
The system can explore variations like:
• Transformer depth (6–12 layers)
• Width (hidden dim 256–1024)
• Attention head count
• FFN kernel types (GELU vs ReLU vs Snake oil)
• Activation quantization (int8 vs float16)
The search space definition is an art. Too narrow and you miss optimal solutions. Too broad and the search becomes intractable. I’ve learned to start conservative and expand based on initial results.
Certain choices have cascading effects. Reducing precision from float16 to int8 doesn’t just save memory. It changes numerical stability, affects gradient flow, and can trigger different kernel implementations. The interactions are complex and often surprising.
Step 3: Hardware Profiling Loop
Each candidate architecture is compiled and benchmarked on the target hardware. Either with:
• Simulation (proxy metrics using cost models)
• Actual inference runs on dev boards or virtualized accelerators
The profiling loop is where theory meets reality. Simulators are fast but approximate. Real hardware is accurate but slow. I typically use a hybrid approach. Rough filtering with simulation, then validation on actual devices.
The profiling revealed surprises. Some architectures that looked efficient on paper performed poorly in practice. Memory access patterns caused cache misses. Certain layer configurations triggered inefficient kernel implementations. One bizarre case showed a model running faster with more parameters because it better utilized the hardware’s parallel units.
Step 4: Multi-Objective Optimization
You score each candidate on a Pareto front: maximizing accuracy while minimizing latency and memory.
The Pareto front visualization is always revealing. You see the trade-offs laid bare. There’s usually a sweet spot where small accuracy sacrifices yield massive efficiency gains. Finding that spot requires judgment and domain expertise.
I’ve sat in meetings where we’ve debated 0.5% accuracy differences for hours. Meanwhile, that 0.5% could buy us 40% latency reduction. The business impact of faster responses often outweighs marginal accuracy improvements.
Step 5: Profit.
You now have a hardware-tuned, performance-optimal transformer that doesn’t set your phone on fire.
The final model was remarkable. It achieved 87% accuracy while meeting all hardware constraints. Inference ran at 35ms average latency. Power consumption stayed under 1.8W even during sustained use. The client launched successfully and scaled to millions of users.
What Makes HW-NAS Hard (and Cool)
1. Search Space Explosion
Transformers have millions of design choices:
• Should I reduce heads and increase FFN width?
• Can I use grouped convolutions instead of dense layers?
• Does sandwiching attention layers make sense on this hardware?
The combinatorial explosion is mind-bending. A modest search space might contain 10²⁰ possible architectures. Exploring even a tiny fraction requires intelligent navigation.
I’ve seen searches get lost in architectural dead ends. They’ll fixate on a particular pattern and miss better alternatives. The challenge is balancing exploration and exploitation. You need to search broadly enough to find novel solutions while focusing enough to refine promising candidates.
HW-NAS tools must navigate this space intelligently, not exhaustively.
2. Hardware Modeling Is Hard
Simulating GPU or NPU behavior is messy:
• Memory access patterns matter
• CUDA kernel launch overheads
• Matrix tiling and SM occupancy
Modern accelerators are incredibly complex. They have multiple levels of cache, various types of memory, specialized execution units. Predicting performance requires understanding all these components and their interactions.
I once debugged a model that ran 10x slower than predicted. The issue? It was triggering memory bank conflicts that the simulator didn’t model. The fix required reshaping tensors to avoid conflicting access patterns. No accuracy change, but 10x speedup.
You need accurate performance predictors trained on real device telemetry.
3. Transferability
What works on an H100 doesn’t work on an iPhone 15. Your NAS engine needs hardware-awareness baked in from the beginning.
The lack of transferability surprises newcomers. They’ll optimize for a V100 and assume it’ll work on an A100. The architectures are different. Memory hierarchies vary. Optimal configurations change.
I maintain hardware profiles for dozens of deployment targets. Each has its quirks and preferences. Some favor deep, narrow networks. Others prefer shallow, wide architectures. The differences can be dramatic.
Tools of the Trade
HW-NAS isn’t a one-size-fits-all solution. But a few frameworks stand out:
FBNet / Once-for-All — Pioneered hardware-aware NAS for mobile
TVM / Ansor / AutoTVM — Auto-tuning compiler stacks with HW constraints
NVIDIA TensorRT-LLM — Not HW-NAS per se, but allows fine-tuned inference graph optimization
Google Vizier + TPU Compiler — Internal tools used for NAS on TPUs
NASBench / HW-NASBench — Benchmarks for reproducible NAS experiments
Apache NNI / AutoKeras — Open-source AutoML/NAS platforms with early HW-awareness support
Each tool has its strengths. FBNet excels at mobile optimization. TVM provides fine-grained control over compilation. TensorRT-LLM offers exceptional performance on NVIDIA hardware.
There’s also EdgeNAS, DetNAS, and other domain-specific flavors.
The ecosystem is fragmented but rapidly maturing. Integration between tools is improving. Standards are emerging. The barrier to entry is lowering.
HW-NAS + LLMs: The Gold Rush Combo
Let’s zoom out.
Today’s LLM landscape looks like this:
• Models are getting bigger (70B → 175B → 270 → 450 → ??)
• Budgets are getting tighter
• Latency expectations are stricter
• Deployment surfaces are broader (phones, edge, browser)
The tension is palpable. Everyone wants GPT-4 capabilities at GPT-2 costs. The math doesn’t work without fundamental changes to how we design and deploy models.
HW-NAS steps in like a ruthless performance engineer. It says:
“What if we could redesign the transformer itself, not just compress it, also to fit your use case on your chip?”
This mindset shift is profound. Instead of taking models as given and struggling to deploy them, we co-design models with deployment in mind. The results can be dramatic.
This leads to:
• Mobile LLMs that don’t suck (thanks to HW-NAS + quantization)
• Domain-specific small LLMs that beat general-purpose giants in accuracy and latency
• Cost-optimized inference graphs for GPU-heavy inference platforms
I’ve seen domain-specific models with 100M parameters outperform 7B parameter general models on specialized tasks. The focused architecture and hardware optimization create remarkable efficiency.
And perhaps most importantly:
Models that run where they’re needed, not just where they were trained.
HW-NAS Isn’t Just for Transformers
The principles extend beyond language models.
You can apply HW-NAS to:
• Vision transformers
• Speech models
• Diffusion models for image generation
• Even multimodal agents where real-time latency is non-negotiable
I recently applied HW-NAS to a diffusion model for real-time video processing. The original model required a cluster of GPUs. The optimized version ran on a single edge device. Same visual quality, 100x cost reduction.
Anywhere there’s a trade-off between accuracy and resource efficiency, HW-NAS has a role to play.
It’s not about “smaller is better”. It’s about “smarter is faster.”
The philosophy extends beyond AI. Any computational system with flexibility in design and constraints in deployment can benefit from hardware-aware optimization.
Designing Transformers for the Real World
After years in this field, I’ve developed strong opinions about model deployment.
In a world obsessed with parameter counts and benchmark scores, HW-NAS reminds us of a more grounded truth:
The best AI model isn’t the biggest one. It’s the one that runs best on your hardware, within your budget, for your task.
That’s not a sexy leaderboard metric. But it is the difference between a proof-of-concept and a product.
I’ve watched too many promising AI startups fail because they couldn’t bridge the gap between research and production. They had brilliant models that nobody could afford to run. They had state-of-the-art accuracy that took 30 seconds per inference. They had revolutionary capabilities trapped in economically unviable deployments.
So the next time someone shows off a shiny new transformer, ask:
• “What chip is it running on?”
• “How fast does it run?”
• “What’s the cost per 1,000 tokens?”
• And most importantly: “Was it born hardware-aware?”
These questions separate serious practitioners from benchmark chasers. They reveal whether someone understands the full lifecycle of AI deployment or just the training phase.
The future belongs to those who can navigate both worlds. Who understand attention mechanisms and memory bandwidth. Who can discuss perplexity scores and power consumption in the same breath. Who recognize that the elegance of an algorithm means nothing if it can’t run where it’s needed.
Because in the new age of efficient AI, architecture is destiny. But hardware is the kingmaker.
The revolution won’t be in larger models. It’ll be in smarter ones. Models designed from the ground up to thrive on real hardware, under real constraints, solving real problems.
That’s the promise of HW-NAS. That’s why I’m excited about where we’re heading. And that’s why, despite all the challenges and complexity, I believe the best is yet to come.
Next week Part-two ~ Comparing with NVIDIA Dynamo
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