Making the GPU vs TPU Decision

VHS won against Betamax not because it was better—Beta had superior quality. VHS won because of ecosystem: more movies, more stores, more players.

GPU vs TPU follows similar dynamics. TPUs might win on specs for certain workloads. GPUs win on ecosystem. The decision depends on what constraints you can live with.

The Hardware Comparison

class HardwareComparison:
    gpu_h100 = {
        "memory": "80 GB HBM3",
        "memory_bandwidth": "3.35 TB/s",
        "compute": "1,979 TFLOPS (FP8)",
        "interconnect": "NVLink 900 GB/s",
        "availability": "Multiple cloud providers",
        "ecosystem": "CUDA, PyTorch, everything",
    }

    tpu_v5e = {
        "memory": "16 GB HBM2e (per chip)",
        "memory_bandwidth": "819 GB/s (per chip)",
        "compute": "197 TFLOPS (BF16)",
        "interconnect": "ICI, optimized for pods",
        "availability": "GCP only",
        "ecosystem": "JAX, some TensorFlow",
    }

    # Key insight: TPU designed for throughput at scale
    # GPU designed for flexibility and single-node performance

When GPUs Win

def gpu_advantages() -> list:
    return [
        {
            "scenario": "Existing PyTorch codebase",
            "reason": "No conversion needed",
            "migration_cost": "Zero",
        },
        {
            "scenario": "Multi-cloud deployment",
            "reason": "GPUs available everywhere",
            "flexibility": "Switch providers easily",
        },
        {
            "scenario": "Low-latency inference",
            "reason": "Single GPU can serve requests",
            "architecture": "Simpler deployment",
        },
        {
            "scenario": "Rapid experimentation",
            "reason": "All models work out of box",
            "time_to_first_result": "Hours not weeks",
        },
        {
            "scenario": "Custom models",
            "reason": "Any architecture works",
            "flexibility": "Full control",
        },
    ]

When TPUs Win

def tpu_advantages() -> list:
    return [
        {
            "scenario": "Very high throughput",
            "reason": "TPU pods scale efficiently",
            "economics": "Cost per token can be lower",
        },
        {
            "scenario": "Already on GCP",
            "reason": "Deep integration",
            "simplicity": "Managed TPU VMs",
        },
        {
            "scenario": "Batch processing",
            "reason": "Optimized for large batches",
            "efficiency": "High utilization",
        },
        {
            "scenario": "JAX-native models",
            "reason": "First-class support",
            "performance": "Optimized kernels",
        },
        {
            "scenario": "Google model serving",
            "reason": "Gemini runs on TPU",
            "alignment": "Same hardware as training",
        },
    ]

The Ecosystem Reality

class EcosystemComparison:
    gpu = {
        "frameworks": ["PyTorch", "TensorFlow", "JAX", "everything"],
        "serving": ["vLLM", "TensorRT-LLM", "SGLang", "many more"],
        "models": "Every model on HuggingFace",
        "tutorials": "Abundant",
        "hiring": "Large talent pool",
        "debugging": "Mature tools",
    }

    tpu = {
        "frameworks": ["JAX (primary)", "TensorFlow (legacy)"],
        "serving": ["Jetstream", "Custom solutions"],
        "models": "Need JAX conversion for most",
        "tutorials": "Growing but limited",
        "hiring": "Smaller talent pool",
        "debugging": "Improving but harder",
    }

    conversion_reality = """
    Converting PyTorch model to TPU:
    - Simple model: 1-2 weeks
    - Complex model: 1-2 months
    - Maintaining both: Ongoing overhead

    Many teams underestimate this cost.
    """

The Decision Framework

def should_use_tpu(context: dict) -> str:
    # Strong yes for TPU
    if context.get("already_on_gcp"):
        if context.get("batch_inference_primary"):
            if context.get("have_jax_expertise"):
                return "TPU likely makes sense"

    # Strong no for TPU
    if context.get("need_multi_cloud"):
        return "GPU - TPU locks you to GCP"

    if context.get("real_time_serving"):
        if context.get("low_latency_critical"):
            return "GPU - simpler architecture"

    if context.get("team_knows_pytorch_only"):
        return "GPU - don't add learning curve"

    # Default
    return "GPU - ecosystem advantages outweigh cost savings"

Cost Comparison

def cost_analysis() -> dict:
    # Simplified comparison (actual varies significantly)
    return {
        "h100_on_demand": {
            "hourly": "$3.00-4.00",
            "throughput": "1000 tokens/sec",
            "cost_per_million": "$0.83-1.11",
        },
        "tpu_v5e": {
            "hourly": "$1.20 (per chip)",
            "throughput": "400 tokens/sec (per chip)",
            "cost_per_million": "$0.83",
            "note": "Need to scale pods for comparable throughput",
        },
        "reality": """
        Raw cost per token can favor TPU.
        But:
        - Conversion engineering time
        - Debugging complexity
        - Vendor lock-in risk
        - Ecosystem limitations

        Often erases the savings.
        """,
    }

The Practical Path

For most teams:

1. Start with GPUs: Faster time to production, full ecosystem
2. Measure actual costs: Don't assume, measure
3. Consider TPU only if: Already GCP, high volume, have JAX expertise
4. Hybrid rarely works: Complexity of maintaining both usually exceeds savings

The best hardware is the one your team can operate efficiently. A well-utilized GPU cluster often beats a poorly-optimized TPU setup that theoretically costs less.