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.
Why GPUs Are Only an Intermediary Solution
1. GPUs were designed for graphics, not AI
The core assumptions behind GPUs:
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Embarrassing parallelism for pixel shading
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Predictable memory access patterns
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Small working sets compared to LLMs
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Regular grids and textures, not trillion-parameter models
AI workloads break nearly all these assumptions.
2. GPUs still depend on the classical memory hierarchy
Even with HBM, GPUs still have:
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Huge data movement overhead
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Separate compute and memory
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Power wasted shuttling weights
In modern LLM training:
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>70% of energy is data movement, not math
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Bandwidth, not FLOPs, is the limiting factor
This architecture is unsustainable as models scale to 10T–100T parameters.
3. Tensor cores are a bolt-on
Tensor cores are essentially a grafted-on matrix accelerator:
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Not tightly integrated with the memory fabric
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Still bottlenecked by HBM bandwidth
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Still forced through CUDA, which adds overhead
They improve throughput but don’t fix the fundamental architectural mismatch.
4. GPUs scale poorly at cluster size
Large AI systems require:
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Global synchronization
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Fast model-parallel communication
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Distributed memory structures
Even NVLink / NVSwitch clusters hit limits around the 10k–20k GPU scale:
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Latency balloons
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Interconnect becomes the bottleneck
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Training efficiency drops massively
For trillion-scale models, GPUs are already the weak link.
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.
2. Wafer-scale engines (WSE)
Cerebras WSE-3 proves:
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Giant monolithic silicon
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All memory local
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No multi-GPU communication
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Full-model training on-die
This is much closer to the eventual direction than GPUs.
3. AI-native distributed memory systems
Think:
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Unified global memory for the entire cluster
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Hundreds of TB of accessible memory
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Zero-copy weight sharing
This is where CXL and UCIe will converge.
4. Optical or analog compute
Optical neural networks promise:
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Orders of magnitude lower energy per MAC
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Natural support for matrix ops
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Massive parallelism
This eliminates electrical resistance limits entirely.
5. Direct silicon photonics interconnect
Rather than GPU p2p networks:
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Photonic mesh
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Terabyte-class chip-to-chip bandwidth
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Ultra-low latency
This is essential for training 100T-scale models.
