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Edited by | Wen Shuqi
Large model inference is slamming into a brand-new wall.
As context windows stretch longer, multi-turn conversations become the norm, and Agent applications explode, the sheer volume of data being processed during inference is skyrocketing. The old playbook of just throwing moreGPU horsepower at the problem is starting to crack: you might have enough compute, but your memory is getting choked out byKV Cache (key-value cache) first.
KV Cache used to be the unsung hero of inference acceleration—think of it as the model’s “scratchpad for short-term memory.” As the model processes context, it jots down the already-computedKey andValue vectors, then reuses them when generating newTokens, dodging redundant computation. But here’s the kicker: the longer the context, the moreKV Cache itself morphs into a costly bottleneck that drags everything down.
Shi Jing, Assistant to the President of Sugon’s Beijing branch and General Manager of its Distributed Storage Product Division, told us in an interview that the industry has already rallied around a “trade storage for compute” consensus. The idea is simple: stash those pre-computedKV Cache entries and just pull them out for reuse on follow-up requests, eliminating all that redundant number-crunching.
But here’s the rub—traditionalKV Cache only kills duplicate computation within a single conversation or a single node. In sprawling, cross-conversation, cross-node cluster environments, its magic fades fast. Think aboutAI agents or knowledge-base Q&A systems that constantly hammer the same context: those foundationalTokens get recalculated thousands upon thousands of times, andGPU cycles go up in smoke.
On August 28th, Sugon unveiled itsParaCache solution to take this problem head-on. The approach builds a distributed sharedKV Cache pool that seamlessly bridges four tiers of memory—GPU HBM,CPU DRAM,SSD, and distributed storage. Data of varying “temperatures” gets routed to the right storage layer, all while enablingKV Cache reuse across GPUs and across nodes.
In plain English, Sugon’s brainchild is to take those compute intermediates that models used to toss away and turn them into a cluster-wide, long-term collective memory. Compute once, store it, and let anyone grab it on demand—no need to redo the math. Shi Jing notes thatParaCache shines brightest in scenarios with heavy long-context usage, tons of shared repetitive context, and serious concurrency pressure. If you’re just firing off one-off short requests, the cache-reuse payoff is pretty slim.
According to Sugon’s internal benchmark data, in high-concurrency scenarios,Token throughput can be jacked up by as much as27-fold. In multi-turn conversation scenarios, Time-To-First-Token (TTFT) latency drops by over80%. And in real-world deployments, one bank’s credit card center saw concurrent throughput jump roughly3 times in its intelligent customer service setup, while an education and research knowledge-base Q&A platform boosted concurrency by15 to20 times.
Shi Jing also shared that in long-context, multi-turn, and agent-heavy workloads,ParaCache can help customers shave off about one-third of their hardware procurement costs on memory andSSD configurations.
We’ve also learned that a major Chinese internet giant has already teamed up with Sugon to fine-tuneKV Cache management for its online inference workloads.
This shift signals a deeper transformation in how AI infrastructure gets built: the era of mindlessly “stacking GPUs” is giving way to a storage-compute synergy approach. Shi Jing points out that the old mindset was to just pile on more GPUs whenever you hit a wall. But GPUs are pricey, supply is constrained, and the wasted computation and extra data shuffling along the pipeline are actively throttling GPU efficiency. “Storage-compute synergy isn’t a quick technical patch—it’s the inevitable trajectory for large-scale training and inference production.”
In Shi Jing’s view, AI is driving seismic structural changes in data infrastructure, and that’s opening up a treasure trove of opportunities for domestic players.
Sugon is banking on its deep roots in the storage space to seize this moment. ItsParaStor distributed storage andFlashNexus centralized storage are its two flagship product lines, already widely deployed across AI scenarios like large model training, autonomous driving, embodied intelligence, and intelligent computing centers. Just this July, the country’s first fully domestic 100,000-cardAI supercluster, “Sugon8000,” went live and plugged into the National Supercomputing Internet—andParaCache was battle-tested right there in that massive cluster.
What’s even more intriguing is thatParaCache isn’t just an engineering tweak—it represents a paradigm shift in how we architect large model inference.
“In the old world, the GPU sat at the center of everything, andKV Cache was just transient state,” Shi Jing explains. “In this new architecture,KV Cache is becoming a bona fide data asset, and GPUs should orbit around that data, deliveringPrefill (processing user prompts) andDecode (generating tokens one by one) compute power.” Going forward, the ability to manageKV Cache could very well dictate inference service costs, response speeds, and even how farAgent capabilities can scale.
Zooming out to the industry level, this structural overhaul of infrastructure is rewriting the very blueprint of compute strategy.AI cluster planning is pivoting from a GPU-obsessed mindset to a holistic view that weighs compute, storage, networking, and caching together—with Total Cost of Ownership (TCO) as the ultimate yardstick. Storage’s role is also evolving, from a passive data vault to an active orchestrator of data scheduling, cache reuse, and compute offloading.
Bottom line: managing memory well is becoming just as critical as cranking up raw compute. In the second half of the large model race, it’s not just about who has the biggest GPU arsenal—it’s about who stores their data smarter.
