Long-running AI agents can pause to use tools, resume later and compete with other sessions for limited GPU memory. VAST’s approach is to move inference KV cache through faster and slower storage tiers, then restore it when a session needs to continue. That can reduce the need to recompute context—but KV cache is not the same as an agent’s durable, searchable memory, and the performance benefit depends on the workload and data path.
What “agent memory” means here
Agents can use long-term memory to retrieve past conversations or interactions. VAST co-founder and CTO Alon Horev described that broader idea in an interview with SiliconANGLE, noting that agents may look back at prior experiences. The material at the center of VAST’s inference examples, however, is the KV cache: a record of model attention state that lets a serving system reuse work already done on a conversation’s context.
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Keeping KV cache does not, by itself, decide which past facts matter, summarize a conversation, or give an agent durable understanding. It is an inference optimization: preserve computation so a resumed session can reuse it rather than rebuild the same context from scratch.
How the storage tiers work
KV cache is valuable but can consume scarce GPU high-bandwidth memory (HBM). VAST’s hierarchy moves cache through tiers with different capacity and access characteristics. SiliconANGLE’s October 6, 2026 report describes the sequence as GPU memory, CPU memory on the same machine, then persistent storage for larger cache capacity, with NVIDIA Dynamo orchestrating movement and scheduling.
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VAST’s technical guide gives a more detailed hierarchy:
- GPU HBM and local tiers: Keep the cache closest to the accelerator for fast access.
- Node-local SSD (G3): Provide additional cache capacity within a server.
- Pod-level CMX flash (G3.5): A proposed Ethernet-attached flash pool shared at pod scale.
- Durable shared storage (G4): Retain cache blocks beyond the capacity of local tiers and make them available across machines.
When a session resumes, the serving stack can fetch preserved blocks into a usable tier. Shared storage can also help move sessions between GPUs or machines. The key question is whether retrieving the saved cache takes less time than recomputing it; network, storage path, framework support and access pattern all affect that trade-off.
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What the reported benchmark demonstrates
VAST Data and Lablup reported a coding-agent benchmark using eight H100 GPUs, VAST AI OS v5.4, Backend.AI 26.4, vLLM 0.20.0 and LMCache. The test used a 140K-token base context, ten distinct agent contexts, five turns per context and a 100 Gbps fabric. With cache offload, total wall-clock time was 601.41 seconds versus 1,177.97 seconds without offload, or 1.96× faster in that configuration. Average time to first token (TTFT) was 10,573 ms versus 22,104 ms, or 2.09× faster. Per-token decode time remained 14.5 ms in both cases.
Those results point to reduced waiting before a resumed context begins producing output, not faster token generation once decoding is underway. They are specific to the stated workload and software/network stack, and the report is from vendors involved in the solution—not an independent, general performance guarantee.
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When offloading is a good fit
The strongest fit described by VAST and Lablup is a shared inference server handling repeated, long and distinct agent contexts, such as coding agents that pause and resume across tool calls. In that pattern, retaining cache can avoid repeatedly rebuilding large contexts while other sessions compete for GPU memory.
- Context and concurrency: Long contexts and many distinct sessions competing for HBM increase the value of moving less-active cache out of GPU memory.
- Reuse pattern: Offload is useful when a session returns to the same cached context. A mostly stable shared prefix may already benefit from prefix caching.
- Restore cost: The transfer and restore path must be faster than recomputation for the relevant blocks.
- Network and storage: The VAST/Lablup report says its fast configuration used RDMA and GPUDirect Storage. It warns that ordinary TCP NFS can make retrieval slower than recomputing.
- Serving-stack support: The inference framework and cache software need to support the tiers and scheduling behavior being used.
- Mobility and controls: Shared state may need to move across GPUs or nodes; deployments also need appropriate tenant isolation, retention and access controls.
A one-shot chatbot with little context reuse is a poor match for this configuration: there may be little saved work to recover, while moving cache still adds overhead.
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CMX status and deployment considerations
VAST describes NVIDIA CMX as a G3.5 tier: a pod-level, Ethernet-attached flash pool between node-local SSD and durable shared storage. VAST’s guide says its support for G3.5 is coming in upcoming Dynamo and VAST releases. That is a vendor-stated integration roadmap, not evidence that the tier is available in every current deployment; availability can change with releases.
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What the performance figures do—and do not—say
A separate VAST report on AMD Instinct MI355X describes a test using an 800 Gbps NFS/RDMA network, with 9× TTFT speedup and 9.7× throughput from KV-cache offload. VAST says AMD did not independently verify those performance and cost claims and that results may not be typical. This is another vendor-reported result under a particular configuration, not a basis for predicting gains on other hardware or workloads.
Across both examples, the defensible conclusion is narrow: tiered storage can make retained inference context available without keeping every cache block in GPU memory, and it may reduce waiting or repeated computation when sessions reuse large contexts. Whether it helps in a given deployment depends on cache reuse and the cost of restoring it.
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