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Evaluating Custom Inference Hardware
Specialized tools often beat general-purpose ones. A fish knife cuts fish better than a chef's knife. But you can't julienne vegetables with a fish knife. The question is always: how specialized is your need?
Custom inference chips make the same tradeoff. They can be dramatically faster for LLM inference. But they lack GPU flexibility, ecosystem, and escape routes.
The Custom Hardware Landscape
class CustomInferenceHardware:
groq = {
"architecture": "LPU (Language Processing Unit)",
"key_feature": "Deterministic latency, SRAM-based",
"claimed_speed": "~10x faster than GPU for inference",
"availability": "API only (currently)",
"models": "Limited model selection",
"pricing": "Per-token, competitive",
}
cerebras = {
"architecture": "Wafer-scale chip",
"key_feature": "Massive on-chip memory, no memory wall",
"claimed_speed": "Very high throughput",
"availability": "Cloud instances",
"models": "Standard architectures supported",
"pricing": "Premium",
}
google_tpu = {
"architecture": "Matrix multiply accelerator",
"key_feature": "High throughput at scale",
"availability": "GCP only",
"models": "JAX-compatible",
"pricing": "Competitive at volume",
}
nvidia_gpu = {
"architecture": "General-purpose GPU",
"key_feature": "Flexibility, ecosystem",
"availability": "Everywhere",
"models": "Everything works",
"pricing": "Market standard",
}Evaluation Framework
def evaluate_custom_hardware(requirements: dict) -> dict:
"""
Framework for evaluating specialized hardware
"""
criteria = {
"performance": {
"weight": 0.25,
"questions": [
"Actual benchmark vs claimed (independent)",
"Performance on YOUR workload, not synthetic",
"Latency distribution, not just average",
],
},
"cost": {
"weight": 0.25,
"questions": [
"Total cost including engineering time",
"Commitment required (contracts, minimums)",
"Price stability (can they raise prices?)",
],
},
"flexibility": {
"weight": 0.20,
"questions": [
"Can you run YOUR models or just their selection?",
"How hard to switch to different hardware?",
"Support for fine-tuned models?",
],
},
"ecosystem": {
"weight": 0.15,
"questions": [
"Tooling quality (debugging, profiling)",
"Community and support",
"Documentation and examples",
],
},
"risk": {
"weight": 0.15,
"questions": [
"Company funding and stability",
"Single-source dependency",
"Exit strategy if needed",
],
},
}
return criteriaThe Speed vs Lock-in Tradeoff
def lockin_analysis():
return {
"nvidia_gpu": {
"lock_in_level": "Low",
"can_switch_to": "Any cloud, on-prem, other GPUs",
"code_portability": "PyTorch works everywhere",
"exit_cost": "Minimal",
},
"groq": {
"lock_in_level": "High",
"can_switch_to": "Back to GPU with code changes",
"code_portability": "API compatible, backend locked",
"exit_cost": "Rebuild serving infrastructure",
},
"cerebras": {
"lock_in_level": "Medium-High",
"can_switch_to": "GPU with some effort",
"code_portability": "PyTorch-ish but not identical",
"exit_cost": "Moderate engineering effort",
},
"tpu": {
"lock_in_level": "Medium",
"can_switch_to": "GPU with JAX or conversion",
"code_portability": "JAX is portable, ecosystem isn't",
"exit_cost": "Rewrite if using JAX, bigger if custom TPU code",
},
}When Custom Hardware Makes Sense
def custom_hardware_scenarios():
return {
"makes_sense": [
{
"scenario": "Latency is everything",
"example": "Real-time trading signals",
"why_custom": "Groq's deterministic latency",
"accept": "Higher cost, limited models",
},
{
"scenario": "Massive throughput needed",
"example": "Process billions of documents",
"why_custom": "Cerebras throughput",
"accept": "Premium pricing, complexity",
},
{
"scenario": "Already GCP-native",
"example": "All infra on Google Cloud",
"why_custom": "TPU integration benefits",
"accept": "JAX learning curve",
},
],
"doesnt_make_sense": [
{
"scenario": "General-purpose serving",
"why_gpu": "Flexibility more valuable than speed",
},
{
"scenario": "Rapid model experimentation",
"why_gpu": "New models work immediately",
},
{
"scenario": "Multi-cloud requirement",
"why_gpu": "GPU available everywhere",
},
{
"scenario": "Small/medium scale",
"why_gpu": "Can't amortize custom hardware overhead",
},
],
}The Benchmarking Reality
def benchmark_skepticism():
"""
How to interpret custom hardware benchmarks
"""
return {
"claims_to_question": [
"10x faster - compared to what baseline?",
"Benchmark model may not match your use case",
"Latency vs throughput (often cherry-picked)",
"Optimal batch size may be impractical",
],
"what_to_demand": [
"Independent benchmarks, not just vendor",
"Your model, your data, your traffic pattern",
"P99 latency, not just average",
"Cost per token, not just speed",
"Sustained performance, not burst",
],
"red_flags": [
"Won't let you benchmark independently",
"Only show best-case scenarios",
"Avoid cost discussions",
"No production customers willing to reference",
],
}The Practical Path
def evaluation_process():
return [
{
"step": 1,
"action": "Establish GPU baseline",
"why": "Need comparison point on your workload",
},
{
"step": 2,
"action": "Get trial access",
"why": "Benchmark with your actual use case",
},
{
"step": 3,
"action": "Measure total cost",
"why": "Include engineering time, not just compute",
},
{
"step": 4,
"action": "Evaluate exit strategy",
"why": "Know how to leave before you commit",
},
{
"step": 5,
"action": "Start small",
"why": "Prove value before large commitment",
},
{
"step": 6,
"action": "Maintain GPU fallback",
"why": "Don't burn bridges",
},
]Custom hardware can be worth it. But "faster" alone isn't enough justification. The total equation includes flexibility, risk, and the cost of being wrong. GPUs' greatest advantage isn't speed—it's optionality.