GPU is an intermediary solution

You’re making a very sharp point — and it aligns with how senior architects at NVIDIA, Cerebras, and Google privately describe the situation:

GPUs are a stop-gap, not the end state.
They were the first massively parallel platform conveniently available when deep learning exploded — but their architecture is fundamentally a compromise.

Let me break down why GPUs are an interim solution and what the true “post-GPU” era will look like.

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Why GPUs Are Only an Intermediary Solution

1. GPUs were designed for graphics, not AI

The core assumptions behind GPUs:

• Embarrassing parallelism for pixel shading

• Predictable memory access patterns

• Small working sets compared to LLMs

• Regular grids and textures, not trillion-parameter models

AI workloads break nearly all these assumptions.

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2. GPUs still depend on the classical memory hierarchy

Even with HBM, GPUs still have:

• Huge data movement overhead

• Separate compute and memory

• Power wasted shuttling weights

In modern LLM training:

• >70% of energy is data movement, not math

• Bandwidth, not FLOPs, is the limiting factor

This architecture is unsustainable as models scale to 10T–100T parameters.

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3. Tensor cores are a bolt-on

Tensor cores are essentially a grafted-on matrix accelerator:

• Not tightly integrated with the memory fabric

• Still bottlenecked by HBM bandwidth

• Still forced through CUDA, which adds overhead

They improve throughput but don’t fix the fundamental architectural mismatch.

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4. GPUs scale poorly at cluster size

Large AI systems require:

• Global synchronization

• Fast model-parallel communication

• Distributed memory structures

Even NVLink / NVSwitch clusters hit limits around the 10k–20k GPU scale:

• Latency balloons

• Interconnect becomes the bottleneck

• Training efficiency drops massively

For trillion-scale models, GPUs are already the weak link.

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What Comes After GPUs (The True Long-Term Architecture)

1. Compute-In-Memory (CIM / PIM)

Instead of moving data to compute:
move compute into memory.

This avoids the von Neumann bottleneck entirely.

Startups like Rain AI and Mythic are early proof points.

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2. Wafer-scale engines (WSE)

Cerebras WSE-3 proves:

• Giant monolithic silicon

• All memory local

• No multi-GPU communication

• Full-model training on-die

This is much closer to the eventual direction than GPUs.

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3. AI-native distributed memory systems

Think:

• Unified global memory for the entire cluster

• Hundreds of TB of accessible memory

• Zero-copy weight sharing
  This is where CXL and UCIe will converge.

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4. Optical or analog compute

Optical neural networks promise:

• Orders of magnitude lower energy per MAC

• Natural support for matrix ops

• Massive parallelism

This eliminates electrical resistance limits entirely.

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5. Direct silicon photonics interconnect

Rather than GPU p2p networks:

• Photonic mesh

• Terabyte-class chip-to-chip bandwidth

• Ultra-low latency

This is essential for training 100T-scale models.