🚧 The KV Cache Bottleneck in Modern Inference #
As large language models (LLMs) grow in size and context length, inference—not training—has become the dominant scalability bottleneck. Central to this challenge is the Key-Value (KV) Cache, which stores intermediate attention data generated during the prefill phase of inference.
The KV Cache grows linearly with prompt length and must remain accessible during token generation. With context windows now extending to hundreds of thousands—or even millions—of tokens, KV Cache size can quickly overwhelm GPU memory. This is especially problematic in multi-turn conversations, agentic workflows, code generation, and research-style prompting, where cached context must persist across long sessions.
When GPU memory limits are reached, operators are forced into costly trade-offs: reducing context length, recomputing cached tokens, lowering concurrency, or adding more GPUs.
🧠 NVIDIA Dynamo: Offloading the KV Cache #
NVIDIA Dynamo addresses this constraint by enabling KV Cache offloading from GPU memory to more scalable and cost-effective storage tiers, including:
- CPU system memory
- Local NVMe SSDs
- Remote or network-attached storage
KV Cache blocks can be moved dynamically between GPU memory and external storage without interrupting inference, dramatically reducing GPU memory pressure while preserving performance.
This capability allows inference systems to support longer context windows, higher user concurrency, and lower infrastructure costs.
⚡ Low-Latency Transfers with NIXL #
At the heart of Dynamo’s offloading mechanism is NIXL, a low-latency data transfer library optimized for moving KV Cache blocks between GPU memory and external storage.
NIXL enables:
- High-throughput KV Cache movement
- Minimal impact on Time to First Token (TTFT)
- Asynchronous transfers that avoid stalling generation
Storage offload is most effective when the cost of data movement is outweighed by the savings from avoiding KV recomputation—a common scenario in long-context or multi-user workloads.
🧩 KV Block Manager (KVBM) #
Dynamo introduces the KV Block Manager (KVBM), which decouples memory management from specific inference engines.
KVBM:
- Coordinates KV Cache placement across GPU memory and storage
- Standardizes storage access across different backends
- Simplifies integration and scaling by separating memory policy from model execution
This abstraction allows compute and storage to evolve independently, enabling more flexible inference architectures.
🔓 Open Architecture and Ecosystem Integrations #
Dynamo is designed around an open, composable architecture rather than a closed stack.
Key integrations include:
- LMCache: An open-source KV caching layer that supports reuse, eviction, and retrieval across CPU memory, local SSDs, and remote storage
- vLLM: Dynamo integrates cleanly with vLLM, enabling KV reuse across sessions and users while maintaining high throughput
This approach allows teams to combine Dynamo’s built-in capabilities with third-party storage systems and inference engines.
📊 Real-World Performance Validation #
Partner testing has demonstrated that KV Cache offloading can deliver both high throughput and lower latency:
- Vast achieved 35 GB/s KV Cache throughput to a single H100 GPU using the GDS plugin, enabling persistent KV movement from storage
- Qwen3-32B tests with 130K-token prompts showed reduced TTFT when precomputed KV Cache was reused from storage
- WEKA demonstrated a zero-copy, RDMA-based data path streaming KV Cache at near-memory speeds
- In an eight-GPU DGX H100 system, read throughput reached 270 GB/s across GPUs
These results validate the feasibility of disaggregated inference, where memory and compute scale independently without introducing bottlenecks.
🗂️ KV Cache Offload Storage Options #
| Storage Backend | Characteristics | Typical Use Cases |
|---|---|---|
| CPU RAM | Low latency, moderate capacity | Long-context, multi-user inference |
| Local SSD | High capacity, cost-effective | Burst workloads, long sessions |
| Remote Storage | Massive scale, shared access | Large-scale, distributed inference |
🏗️ Implementation Overview #
Dynamo manages KV Cache movement through KVBM, using NIXL for transport and LMCache for reuse and eviction strategies. KV Cache blocks can be offloaded, retrieved, and reused across sessions and users, reducing recomputation and improving overall efficiency.
Operational tooling includes:
- Grafana dashboards for monitoring KV onboarding and offloading activity
- LMBenchmark guidance for comparing KVBM-enabled deployments against baseline vLLM configurations
This tooling helps teams quantify performance gains and tune storage policies for their workloads.
🎯 Why It Matters #
KV Cache offloading fundamentally changes the economics of LLM inference:
- Enables longer prompts and persistent context without proportional GPU scaling
- Increases user concurrency on existing GPU clusters
- Reduces cost per token by avoiding expensive recomputation
- Improves latency and responsiveness in real-world deployments
For developers and enterprises deploying large-context or agentic AI systems, NVIDIA Dynamo provides a practical, production-ready path to scale inference efficiently—without being constrained by GPU memory alone.
✅ Key Takeaways #
- KV Cache offloading reduces GPU memory pressure and unlocks longer context windows
- KVBM standardizes memory and storage management across engines
- LMCache and vLLM integrations enable reuse, eviction, and higher throughput
- Real-world tests show high transfer speeds and reduced latency
- An open architecture allows flexible, cost-efficient inference at scale