The Memory Challenges Posed by Modern AI Workloads

Imagine your brain trying to juggle not just numbers, but thousands of entire books at once; that’s what modern AI workloads are asking from compute systems. The models keep growing larger and more complex: trillions of parameters, expanding context windows, and sparse activation models like “mixture of experts” that selectively wake up parts of the network, but still demand fast memory access everywhere.

Even the most powerful GPUs and AI accelerators, loaded with teraflops of compute, can sit idle if the memory subsystem lags. Memory bandwidth and latency have become the real choke points. This bottleneck is often referred to as the “memory wall”: compute capability (FLOPs) has been increasing at a far faster pace than DRAM and interconnect bandwidth.

And there’s more: power, cooling, and cost. High-performance memory like HBM isn’t just expensive; it’s energy-hungry. The energy required to move data around often exceeds the energy needed for actual computation. When you add in dataset shuffling, I/O transfers, and caching layers, it becomes obvious, in AI systems today, memory isn’t just a supporting character; it’s the main stage.

Key Technologies & Memory Types Meeting the Demand

Think of an AI system as a race car. The GPUs and TPUs are the engines, raw compute is the fuel, and memory is the road. Without wide enough roads (bandwidth) and smooth surfaces (low latency), your high-powered race car sputters, no matter how strong the engine is.

Here are the technologies carrying today’s AI workloads forward.

High-Bandwidth Memory (HBM3 / HBM3E)

HBM (High-Bandwidth Memory) places memory directly beside the compute using 3D stacking. This shortens the travel distance for data, cuts energy losses, and delivers massive throughput.

• HBM3: delivers ~819 GB/s per stack.
• HBM3E: pushes beyond 1.2 TB/s per stack, as seen in Micron’s 24 GB, 8-Hi products.

The benefit isn’t only speed; performance per watt improves dramatically compared with traditional DRAM. The tight integration also helps with thermals, although stacking multiple dies does create its own cooling challenges.

Best use case:

GPU and accelerator memory in AI training and inference, where bandwidth is the absolute bottleneck.

DRAM Evolution (DDR5, LPDDR, etc.)

Standard DRAM hasn’t stood still. DDR5 modules deliver ~38–51 GB/s each, scaling with clock speeds (DDR5-4800 to DDR5-6400). They’re cheaper per gigabyte than HBM, more abundant, and easier to integrate into general-purpose servers.

DDR5 also supports higher densities, improved channel efficiency, and in some cases, on-die ECC (error correction) for reliability. While DDR5 can’t match HBM bandwidth, it’s the workhorse memory layer that underpins most system architectures.

Best use case:

System memory, model offloading, and intermediate layers of the hierarchy.

Compute Express Link (CXL) & Disaggregated Memory

CXL (Compute Express Link) is a game-changer. It enables memory pooling and disaggregation, breaking free from fixed DIMM slots tied to a single CPU. With CXL, multiple hosts and accelerators can dynamically access and share a common pool of memory.

Vendors have demonstrated CXL-attached DDR5 modules (e.g., Niagara 2.0) that scale both bandwidth and capacity without requiring a full hardware redesign. This is a stepping stone to truly composable infrastructure.

Best use case:

Cloud and data centre environments that need elastic scaling of memory across CPUs, GPUs, and accelerators.

SSD / NVMe / Persistent Memory Layers

When the higher tiers max out, NVMe SSDs step in.

• PCIe Gen4 NVMe: ~7–7.5 GB/s.
• PCIe Gen5 NVMe: higher still (above 12 GB/s in some products).

Latency is orders of magnitude higher than DRAM or HBM, but NVMe provides immense capacity and streaming throughput. For datasets and large model weights, NVMe is the fallback layer, slower, but indispensable.

Best use case:

Storing large models, datasets, or batch streaming when DRAM/HBM capacity is insufficient.

Memory Type Comparison

Memory Type	Example Bandwidth / Spec	Relative Cost & Energy	Best Use Case
HBM3 / HBM3E	HBM3: ~819 GB/s per stack; HBM3E: >1.2 TB/s per stack	High cost per GB; highly energy efficient per bit; requires advanced cooling	Accelerator/GPU memory for AI training and high-throughput inference
DDR5 (Standard DRAM)	~38–51 GB/s per module	Lower cost per byte; higher latency vs HBM; widely available	Main system memory, offloading, memory hierarchy balance
NVMe SSD / Flash	Gen4: ~7–7.5 GB/s; Gen5: >12 GB/s	Very low cost per TB; relatively high latency; IO power draw is significant	Storage of model weights/datasets; cache and streaming

Market & Trend Insights (2024–2025 and Beyond)

The memory market is booming, with AI the biggest driver of demand.

• Revenue & growth: The global memory market hit ~£129.37 billion in 2024 (DRAM ≈ £73.8 billion, NAND ≈ £51.8 billion). Projections put it close to £152.2 billion in 2025.
• HBM growth: SK hynix projects ~30% CAGR through 2030 for AI-specialised HBM. Some analyses suggest revenues could reach ~£74.6 billion by 2030
• Next-gen persistent/disaggregated memory: Expected to grow from £8.4 billion – £11.4 billion in 2025 to £30.4 billion – £34.3 billion by 2030.
• Supply and geopolitics: High-performance memory production is constrained by advanced process nodes, packaging complexity, and thermal design. Add export restrictions and localisation pushes, and supply chains stay volatile.
• Edge and inference trends: Unified memory designs (like Apple’s Mac Studio) are becoming common in AI PCs and workstations. Memory pooling via CXL is moving from labs to adoption, especially in hyperscale datacenters.

Designing Memory Architecture for AI Workloads

Designing an AI system isn’t just about raw GPU count; it’s about ensuring data flows smoothly through the memory hierarchy.

Memory Hierarchy & Tiering

Visualise it as a pyramid:

• On-chip caches (fastest, smallest).
• HBM (or future equivalents).
• DDR5/LPDDR.
• SSD/NVMe/persistent memory at the base (largest, slowest).

The key is to keep the hottest, most frequently accessed data in the highest tiers.

Latency vs Throughput Trade-offs

• Training workloads: Throughput is king - moving massive data batches in parallel.
• Inference workloads: Latency dominates - responses must be near-instant.
• Balanced systems: Sometimes sacrificing throughput for latency (e.g., edge AI) makes sense.

Data Movement & Architecture Patterns

• Caching & prefetching to keep compute pipelines fed.
• Pooling/disaggregation (CXL) to reduce idle capacity.
• Compression & quantisation (4- or 8-bit) to cut storage requirements and boost effective throughput.

Power, Thermal, and Cost Considerations

Memory subsystems can draw a large fraction of a system’s power. HBM stacks run hot; cooling costs mount. Total Cost of Ownership (TCO) must weigh upfront expense against downstream savings in energy and developer productivity.

Flexibility & Future-Proofing

• Modularity: Expansion-friendly chassis, CXL slots, upgradable DIMMs.
• Vendor diversity: Don’t bet everything on one supplier.
• Observability: Monitors bandwidth saturation and latency distributions in real workloads, not just benchmarks.

Get a better understanding with Real-World Examples

Google’s Ironwood TPU Supercomputer

Unveiled at Hot Chips 2025, Google’s Ironwood TPU (7th generation) packs:

• 192 GB HBM3E per chip
• ~7.4 TB/s bandwidth
• ~1.8 PB pooled memory per pod

This architecture is designed for large-scale inference, such as recommendation systems and serving LLMs at scale. The shared memory pool reduces latency and enables multi-workload concurrency.

Micron HBM3E

Micron’s HBM3E products deliver:

• 24 GB per stack
• >1.2 TB/s bandwidth
• Improved performance per watt

These devices reduce training time significantly, directly lowering operational costs for hyperscalers.

Enfabrica’s EMFASYS

Enfabrica (backed by Nvidia) developed EMFASYS, a smart interconnect fabric that links accelerators with DDR5 memory. This design enables cheaper, higher-capacity DRAM to supplement HBM, balancing cost and performance.

Elastics.cloud CXL Pooling

Elastics.cloud showcased symmetric CXL pooling, where two servers shared both local and remote CXL-attached memory in real time. This demonstrates the future of flexible, disaggregated memory architectures.

Implications & Recommendations for IT Decision-Makers

1. Plan for supply & cost volatility. HBM is scarce and premium-priced; diversify suppliers.
2. Balance performance and TCO. HBM may look costly, but it shortens training cycles and lowers energy bills. DDR5 with CXL pooling offers affordable scalability.
3. Think long-term architecture. Prioritise modular, composable designs over rigid DIMM slots.
4. Benchmark aggressively. Real workloads rarely behave like datasheet specs.
5. Consider sustainability. AI’s reputation for energy hunger is real; memory design is key to improving performance per watt.

Frequently Asked Questions (FAQs)

Why is memory so important for AI workloads?
Because AI models move enormous amounts of data. Without fast memory, even the most powerful GPUs stall.

What’s the difference between HBM and DDR5?
HBM is ultra-fast but expensive; DDR5 is slower but far cheaper and more capacious. Most AI systems use both.

Can SSDs or NVMe drives support AI memory needs?
Yes, but mainly for storage and caching. They’re too slow for active computation compared with DRAM or HBM.

How can organisations plan for future memory demands?
By choosing modular systems, mixing premium and standard memory, and benchmarking workloads regularly.

Conclusion

AI may grab headlines with flashy billion-parameter models, but the unsung hero behind it all is memory. From HBM3E stacks delivering terabytes per second, to CXL pooling that extends DDR5, to NVMe tiers providing cost-effective capacity, memory design is what determines whether workloads run smoothly or stall.

For IT leaders, the lesson is simple: treat memory as a strategic pillar, not an afterthought. Costs are high today, but the risks of under-investing are higher: stalled training runs, soaring energy bills, or infrastructure bottlenecks that strangle innovation.

The good news? The ecosystem now offers modular, scalable options. You’re building hyperscale datacenter clusters or AI-ready workstations, there’s a memory strategy to fit.

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