{"id":89147,"date":"2026-05-07T16:47:15","date_gmt":"2026-05-07T23:47:15","guid":{"rendered":"https:\/\/phisonblog.com\/?p=89147"},"modified":"2026-05-08T12:23:05","modified_gmt":"2026-05-08T19:23:05","slug":"doing-more-ai-with-less-gpu-memory-how-pascari-aidaptiv-helps-navigate-todays-memory-crunch","status":"publish","type":"post","link":"https:\/\/phisonblog.com\/zh-tw\/doing-more-ai-with-less-gpu-memory-how-pascari-aidaptiv-helps-navigate-todays-memory-crunch\/","title":{"rendered":"\u7528\u66f4\u5c11\u7684GPU\u8a18\u61b6\u9ad4\u5be6\u73fe\u66f4\u591aAI\uff1aPascari aiDAPTIV\u2122\u5982\u4f55\u5e6b\u52a9\u61c9\u5c0d\u7576\u4eca\u7684\u8a18\u61b6\u9ad4\u77ed\u7f3a"},"content":{"rendered":"

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Extend effective GPU memory and run more-capable AI workloads on existing local systems by rethinking how memory is managed across the stack.<\/p>\n<\/blockquote>\n

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As AI adoption accelerates, so does pressure on the infrastructure that supports it. Over the past year, memory pricing has surged alongside demand for AI-capable systems. GPUs with high-bandwidth memory are harder to source. DRAM shortages continue to ripple through supply chains. Systems configured for AI workloads are commanding premium prices.<\/span>\u00a0<\/span><\/p>\n

For many organizations, the instinctive response has been to look at raw compute. More GPUs. Larger clusters. Higher-performance parts. Yet as teams deploy real models into production, a different constraint often surfaces first.<\/span>\u00a0<\/span><\/p>\n

AI workloads are increasingly memory bound.<\/span>\u00a0<\/span><\/p>\n

If you are planning AI initiatives for workstations, AI PCs, edge servers, or departmental systems, understanding that shift is critical. While compute still matters, memory capacity and memory efficiency are quickly becoming the primary scaling limit.<\/span>\u00a0<\/span><\/p>\n

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AI workloads are memory bound<\/h3>\n

Recent trends and developments in AI are driving the need for more memory capacity and greater efficiency during runtime. These include the ever-increasing size of modern AI models, the expansion of context windows, architectures such as mixture of experts (MoE) that keep more parameters accessible, and agentic and multistep inference workflows that keep state in memory longer.\u00a0<\/span>\u00a0<\/span><\/p>\n

In the past, many AI teams looked at memory bottlenecks as a GPU issue. On paper, GPUs offer immense compute throughput. In practice, however, GPU memory is often exhausted before the compute cores are fully utilized. On workstations, PCs, and small servers, this constraint shows up quickly. You may have sufficient compute headroom, but your model doesn\u2019t fit in memory. Or it fits only by aggressively trimming context length or reducing model capability.<\/span>\u00a0<\/span><\/p>\n

The problem of memory bottlenecks is not theoretical. It is operational.<\/span>\u00a0<\/span><\/p>\n

As AI expands from centralized hyperscale environments into enterprise departments and edge deployments, these constraints become more apparent. A local engineering team experimenting with a reasoning model may find that GPU memory fills long before performance goals are reached. A data science group running long context inference may see KV cache growth dominate available memory.<\/span>\u00a0<\/span><\/p>\n

When memory fills up, performance degrades or workloads fail outright. At that point, teams begin looking for ways to expand capacity.<\/span>\u00a0<\/span><\/p>\n

That leads directly to the next challenge.<\/span>\u00a0<\/span><\/p>\n

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Read:\u00a0Phison Showcases the Future of AI and Enterprise SSDs<\/div><\/div><\/div><\/div><\/div>\n

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GPU memory is fixed and expensive<\/h3>\n

Unlike system memory in a traditional server, GPU memory is integrated into the GPU itself. You cannot upgrade it independently.<\/span>\u00a0<\/span><\/p>\n

If your model requires more memory than your current GPU provides, the typical answer is to purchase a higher-memory GPU. Even if the compute capacity of your existing GPU is sufficient, you are forced to move to a larger and more expensive GPU simply to gain memory headroom.<\/span>\u00a0<\/span><\/p>\n

In the current market, that decision carries significant cost implications. Ongoing DRAM supply pressures have increased the price of GPUs and AI-configured systems. High-memory GPU models are particularly expensive and often more difficult to source. When you step up to a larger GPU, you are paying for both additional memory and additional compute whether you need it or not.<\/span>\u00a0<\/span><\/p>\n

This dynamic amplifies the pricing surge. As more organizations compete for memory-rich GPUs, supply tightens further. Prices climb. Procurement timelines extend. AI budgets expand faster than anticipated.<\/span>\u00a0<\/span><\/p>\n

For enterprise teams that are building local AI capabilities, the economics become difficult to ignore. You may have already invested in capable GPUs. Yet to run a slightly larger model or enable longer context, you are pushed toward a full hardware refresh.<\/span>\u00a0<\/span><\/p>\n

At this point, many organizations consider adding more GPUs instead of replacing them.<\/span>\u00a0<\/span><\/p>\n

That approach seems logical. It also introduces its own limitations.<\/span>\u00a0<\/span><\/p>\n

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Why adding GPUs doesn\u2019t always solve the problem<\/h3>\n

Adding GPUs can improve throughput in many scenarios. For multiuser applications, distributing sessions across several GPUs is straightforward. It can increase overall system capacity and reduce wait times for concurrent workloads.<\/span>\u00a0<\/span><\/p>\n

However, many inference workloads operate on a single GPU per session. A single user running a large model may be limited by the memory available on the device. Adding additional GPUs increases the number of sessions you can handle simultaneously. It does not increase the usable memory available to a single model instance.<\/span>\u00a0<\/span><\/p>\n

To combine GPUs into a single larger memory pool requires sophisticated parallelism strategies. You must shard the model, coordinate communication across devices, and manage synchronization overhead. These approaches can introduce additional latency and require specialized software stacks. They also increase operational complexity.<\/span>\u00a0<\/span><\/p>\n

There are certain use cases where you might see little benefit from simply adding more GPUs. These include single-session inference with large models, long-context workloads where KV cache dominates memory usage, and agentic workflows that maintain state across turns.\u00a0<\/span>\u00a0<\/span><\/p>\n

MoE models add another layer. Even though only a subset of experts may be active for a given token, the total expert memory footprint can exceed the capacity of a single GPU. Without careful memory management, much of that capacity must reside in memory even if it is not actively used at every step.<\/span>\u00a0<\/span><\/p>\n

In each of these cases, the core issue persists. The effective memory available to the workload remains limited by the physical memory on a single GPU. Adding more devices increases cost and complexity, yet it does not fundamentally address the bottleneck.<\/span>\u00a0<\/span><\/p>\n

If compute is not the only lever, and adding GPUs is not always efficient, the question becomes clear. How can you extend effective memory without redesigning your entire system?<\/span>\u00a0<\/span><\/p>\n

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Accelerate Your AI Deployment with Phison's Pascari aiDAPTIV <\/div><\/div><\/div><\/div><\/div>\n

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How Pascari aiDAPTIV addresses the real problem<\/h3>\n

aiDAPTIV<\/span><\/a> is a purpose-built Pascari solution that enables organizations to run larger and more demanding AI workloads on local systems by extending memory with an additional flash tier. And it approaches today\u2019s memory challenges from a different angle, rather than simply adding costly GPU resources.\u00a0<\/span>\u00a0<\/span><\/p>\n

Instead of treating GPU memory as a rigid boundary, aiDAPTIV coordinates GPU memory, system memory, and high-performance flash as a unified memory system. In this model, frequently accessed data remains close to the GPU. Less-active data can be staged and recalled dynamically. By intelligently managing where data resides and when it is moved, aiDAPTIV extends effective GPU memory capacity.<\/span>\u00a0<\/span><\/p>\n

This architecture reduces the need to keep all model components permanently resident in GPU memory. For MoE models, for example, experts can be loaded on demand rather than occupying space continuously. And for long-running or conversational inference, KV cache state can be preserved to avoid costly recomputation.<\/span>\u00a0<\/span><\/p>\n

The result is a system where GPUs spend more time performing useful computation and less time idling due to memory pressure. Rather than forcing you to upgrade to a larger GPU SKU, aiDAPTIV helps you make better use of the memory resources already present in your system.<\/span>\u00a0<\/span><\/p>\n

Importantly, this approach avoids the need for complex multi-GPU pooling or cluster-style parallelism. It works within realistic enterprise deployments such as workstations, AI PCs, and small servers. That matters for organizations that want AI capabilities at the edge, in departments, or within constrained environments.<\/span>\u00a0<\/span><\/p>\n

By reducing memory bottlenecks, aiDAPTIV directly addresses the economic pressures created by the current pricing surge. When you can run larger models on existing hardware, you reduce the need to compete for scarce high-memory GPUs.<\/span>\u00a0<\/span><\/p>\n

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Read: Agentic AI Is Becoming Practical: Local Systems Still Need More Memory<\/div><\/div><\/div><\/div><\/div>\n

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What aiDAPTIV enables for enterprise AI<\/h3>\n

When memory efficiency improves, several practical benefits follow. It enables you to:<\/span>\u00a0<\/span><\/p>\n