Supercharging AI Inference: Pure KVA Now Integrates with NVIDIA Dynamo for Scalable, Low-latency LLM Inference

In The Matrix, Neo didn’t spend years mastering kung fu. Instead, all that knowledge was injected directly into him, bypassing the learning process altogether.

That’s the same leap forward enterprise AI just took.

With Pure Key Value Accelerator (KVA) now integrated into NVIDIA Dynamo, large language models (LLMs) no longer need to “relearn” context or reprocess thousands of tokens to understand what comes next. Instead, precomputed knowledge—the KV cache—can be instantly injected into any node or model across the inference fleet.

By combining storage-backed caching with disaggregated inference serving, this integration allows organizations to deliver responses faster, scale workloads seamlessly, and share intelligence across GPU clusters. Enterprises can now transform inference from a repetitive process into a fluid, collaborative system of knowledge transfer.

Why This Integration Matters

Faster Reasoning and More Efficient GPU Utilization

In most inference systems today, every model still has to “relearn” the same information from scratch. Each prompt triggers a full reconstruction of its internal attention maps. It’s an expensive and repetitive process that burns GPU cycles and delays time-to-first-token (TTFT) response times. By eliminating redundant KV recomputation, Pure KVA ensures that GPUs don’t waste cycles reprocessing the same prompts or context.

Increased Productivity

That redundancy doesn’t just consume compute cycles; it constrains productivity. When every node has to rebuild context independently, organizations must either add more hardware or accept slower service to handle the same workload.

Pure KVA and NVIDIA Dynamo change that equation. With Pure KVA all-flash storage-backed cache persistence, the attention states a model generates once can be instantly shared and reused by any other node across the fleet. Each instance inherits what others have already computed, loading that knowledge directly into memory instead of relearning it. Contexts, embeddings, and conversation histories are retained and reused seamlessly, accelerating responses and maintaining continuity across multi-turn interactions and long-running sessions.

Smarter Resource Utilization

Meanwhile, NVIDIA Dynamo’s disaggregated prefill and decode workers orchestrate these workloads intelligently across GPUs, ensuring the right hardware performs the right task at the right time. Together, they create an inference fabric where knowledge flows as efficiently as data, helping organizations deliver faster responses while reducing their GPU footprint and overall infrastructure costs.

This isn’t just faster inference—it’s a new model for distributed intelligence: scalable, persistent, and continuously accelerating.

NVIDIA Dynamo Inline

How Pure KVA and NVIDIA Dynamo Work Together

1. vLLM Integration for Developer Flexibility

When we first introduced Pure KVA, we showed how it integrates with vLLM to deliver persistent KV caching and dramatically faster inference performance.

Now, we’re extending that integration to NVIDIA Dynamo, which supports vLLM as one of its primary backends for distributed inference. Together, these technologies bring scalable, storage-backed caching to production workloads without requiring developers to change their inference runtime or deployment configurations.

Once NVIDIA Dynamo, Pure KVA, and vLLM are launched together, developers can use the same endpoints, models, and workflows they already run with the standard vLLM inference engine enhanced with distributed performance and persistent KV cache reuse.

Developers can deploy inference pipelines that automatically:

  • Detect and reuse cached prompts across GPUs and storage
  • Coordinate workloads seamlessly across nodes without manual tuning
  • Fetch cached KV states directly from Everpure® FlashBlade ® via NFS, GPU Direct Storage, or S3
  • Scale seamlessly from single-node to multi-node GPU clusters

This unified workflow delivers enterprise-grade performance with zero code changes—simplifying deployment while improving throughput, consistency, and efficiency for large-scale inference workloads.

Figure 1: High-level architecture between NVIDIA Dynamo and Everpure Key Value Accelerator (Pure KVA).

2. Disaggregated Serving, Supercharged

NVIDIA Dynamo introduces a disaggregated inference architecture that splits prefill (input processing) and decode (token generation) phases across distinct GPUs. This improves throughput and utilization by tailoring GPU assignments to each phase’s unique computational profile—compute-bound for prefill, memory-bound for decode.

When paired with Pure KVA persistent KV caching, these phases can now reuse cached attention states across distributed nodes, dramatically reducing prefill overhead. For workloads like chatbots, agentic AI, or retrieval-augmented generation (RAG)—where system prompts or repeated instructions can be long—this means models don’t have to “rethink” each prompt—they can resume reasoning instantly.

Figure 2: Integration of Pure KVA with NVIDIA Dynamo. Pure KVA handles the KV cache transfers between prefill and decode workers.

3. Intelligent Routing with KV Cache Awareness

NVIDIA Dynamo’s Smart Router tracks KV cache locations across GPUs and routes requests to minimize recomputation costs. By integrating the storage-backed persistence layer of Pure KVA, the router can now extend cache awareness beyond GPU memory—checking whether a cached state exists in FlashBlade via NFS or S3, retrieving it instantly when needed.

The result is a breakthrough in inference continuity: Any GPU in the cluster can now access shared caches from NFS or S3, even if those caches were created minutes, days, or weeks earlier. This persistent, system-wide cache awareness transforms inference workloads from being memory-bound to fully scalable across time and infrastructure, ensuring faster startup, greater efficiency, and seamless reuse across sessions and deployments.

4. Distributed KV Cache Management Meets Persistent Storage

NVIDIA Dynamo’s Distributed KV Cache Manager dynamically offloads less frequently used caches from GPU to lower-cost memory or storage. With Pure KVA integrated, those caches are automatically persisted on Everpure FlashBlade, ensuring low-latency retrieval and massively parallel I/O even under peak load.

In essence, NVIDIA Dynamo manages where the cache lives, while Pure KVA ensures it’s instantly accessible. Together, they create a unified caching system where GPU memory delivers peak-speed access for active sessions, and FlashBlade provides scale-out, high-performance persistence that can also act as a hot tier for cached prompts. With built-in support for both NFS and S3, FlashBlade gives enterprises the flexibility to maintain fast, durable cache persistence, keeping your AI inference pipelines fast, consistent, and efficient.

The Bigger the Context Window, the More Value Pure KVA Provides

Pure KVA, combined with FlashBlade, supports both NFS and S3 protocols for flexible, high-performance cache persistence within on-prem environments. As your AI fleet expands, caches can be reused across all inference nodes—whether they’re serving local or distributed workloads. This capability ensures that Pure KVA and FlashBlade grow alongside your infrastructure, enabling consistent, fast inference no matter how large or complex your deployments become.

And, you can serve more concurrent requests with the same GPU footprint, translating to greater efficiency and lower operational costs without altering existing workflows.

Architectural Synergy

The architectural integration between Pure KVA and NVIDIA Dynamo combines disaggregated inference, persistent caching, and scale-out performance—unlocking new levels of efficiency for large model serving.

NVIDIA Dynamo Component Pure KVA Contribution Outcome
Distributed KV Cache Manager Persistent KV caching via FlashBlade (NFS/S3) Eliminates GPU memory constraints and ensures caches persist across sessions
Smart Router Extends cache awareness beyond GPU memory to storage-backed persistence on FlashBlade Enables any GPU in the cluster to retrieve and reuse cached prompts—whether minutes or weeks old
Prefill/Decode Workers Seamlessly connected to storage-backed KV caches managed by Pure KVA Optimized disaggregated serving with faster startup, balanced GPU load, and consistent throughput
vLLM Runtime Native Pure KVA integration with NVIDIA Dynamo’s vLLM backend Faster TTFT and higher throughput for production workloads—no code changes or retraining required

Conclusion

AI inference no longer has to be a trade-off between performance and cost. The integration of Pure KVA and NVIDIA Dynamo delivers a breakthrough architecture for low-latency, high-efficiency inferencing at scale. Together, they redefine how organizations deploy, serve, and scale the world’s most advanced AI models—quickly, intelligently, and sustainably.