Dynamo

Executive Summary

This report provides a comprehensive comparison of NVIDIA Dynamo with other frameworks, including vLLM and NVIDIA Triton Server, for large language model (LLM) inference workloads. The key findings highlight Dynamo's superior performance in multi-GPU setups, achieving higher throughput and lower latency compared to vLLM. Additionally, Dynamo's disaggregated serving approach offers more flexible performance tuning, especially for large models and variable workload conditions. In comparison to NVIDIA Triton Server, Dynamo is optimized for low-latency generative AI/LLM workloads, while Triton Server excels in multi-model inference serving. The report also delves into Dynamo's technical architecture, which is designed to accelerate inference workloads through components such as disaggregated serving, smart routing, and distributed KV cache management. Benchmarking methodologies and key performance metrics are also discussed, emphasizing the importance of standardized evaluation for comparing the performance of different frameworks. Overall, the report concludes that the choice of framework depends on specific project requirements, with Dynamo being the preferred option for optimized low-latency generative AI/LLM workloads and Triton Server suitable for multi-model inference serving.

Comparison with vLLM

Dynamo's performance in multi-GPU setups surpasses VLLM's in terms of throughput and latency optimization (4). When comparing the two frameworks, it becomes apparent that Dynamo's disaggregated serving approach offers more flexible performance tuning, especially for large models and variable workload conditions (4). In contrast, VLLM's pipeline parallel approach, which utilizes ray for inference, often faces constraints related to model dimensions and GPU count (4).

A specific example of this performance difference can be seen when using the --tensor-parallel-size parameter. Dynamo's performance with this parameter is optimized for disaggregated serving, resulting in a 30% throughput/GPU improvement in single-node tests and over 2X gains in two-node setups (4). In contrast, VLLM's performance with this parameter is more closely tied to the efficiency of ray's pipeline parallelism and the model's compatibility with the specified GPU count (4). For instance, increasing GPUs can improve throughput/GPU and latency/user for decode-only engines in VLLM, but this is contingent upon sufficient KV cache (4).

Overall, the comparison between Dynamo and VLLM highlights the importance of considering the specific requirements of large language models when selecting an optimization framework (1). By choosing a framework that can effectively manage disaggregated serving, dynamic GPU scheduling, and KV-aware request routing, developers can significantly improve the performance and efficiency of their LLM inference workloads (1).

Comparison with Triton Server

The key difference between NVIDIA Triton Server and Dynamo lies in their primary focus, with Triton Server concentrating on multi-model inference serving and Dynamo on distributed inference for generative AI/LLMs (2). This distinction is crucial when deciding which platform to use for specific inference deployment needs. For instance, if the goal is to serve a wide range of models, including TensorFlow, PyTorch, and ONNX, NVIDIA Triton Server's broad model support makes it an attractive option (2). On the other hand, for applications requiring low-latency generative AI/LLM workloads, Dynamo's optimized performance for such tasks positions it as a better choice (2).

A specific example that highlights the difference in scalability between the two platforms is the deployment of large-scale language models. Dynamo is designed for large-scale distributed environments, which allows it to handle the complex computations required by these models more efficiently (2). In contrast, while NVIDIA Triton Server is highly scalable, its primary focus on multi-model inference serving might not fully leverage its scalability potential for generative AI/LLM workloads (2).

When considering memory management, NVIDIA Triton Server optimizes for GPU memory, which is beneficial for models that heavily rely on GPU acceleration (2). However, Dynamo's dynamic KV cache management across multiple tiers offers a more flexible approach to memory management, potentially leading to better performance in distributed environments (2).

In conclusion, the choice between NVIDIA Triton Server and Dynamo for multi-model inference deployment depends on the specific requirements of the project. For versatility across model types, NVIDIA Triton Server is a strong candidate, but for optimized low-latency generative AI/LLM workloads, Dynamo's specialized approach makes it the preferred option (2).

Technical Architecture

NVIDIA Dynamo's technical architecture is designed to accelerate inference workloads (3). The architecture consists of several key components, including Dynamo Disaggregated Serving, which separates prefill and decode stages for optimal GPU utilization, enhancing throughput and reducing latency in inference workloads (3). Another crucial component is the Dynamo Smart Router (KV-aware Request Routing), which routes requests to workers with the highest KV cache hit rate, maintaining load balance and expediting decoding using a global radix tree registry (3).

The Dynamo Distributed KV Cache Manager strategically stores and evicts KV caches across multiple memory tiers, including GPU, CPU, SSD, and Object Storage, supporting hierarchical caching and offloading for cost-effective KV cache management (3). Additionally, the NVIDIA Inference Transfer Library (NIXL) accelerates data transfer with reduced synchronization and intelligent batching, optimized for dynamic scaling and low-latency storage access in inference (3).

A specific example of the technical architecture's effectiveness can be seen in the High-Level Architecture Diagram, which illustrates the interaction between components such as the API Server, Smart Router, Workers, Dynamo Distributed KV Cache Manager, and NIXL (3). This diagram demonstrates how the architecture is designed to adapt to task-specific deployments and scale dynamically based on real-time demand signals.

The implementation details of the technical architecture reveal the use of Rust for performance-critical modules and Python for flexibility, rapid prototyping, and customization (3). The Distributed Runtime, implemented in Rust with Python bindings, enables distributed communication and coordination through a hierarchical structure (3). Overall, the technical architecture of NVIDIA Dynamo provides significant performance benefits, including a boost in GPU involvement, reduced redundant computations and latency, and optimized resource allocation and data transfer (3).

Benchmarking and Evaluation Methodologies

Standard benchmarking methodologies are crucial for comparing the performance of NVIDIA Dynamo, vLLM, and Triton Server for large language model inference workloads (5). These methodologies include throughput measurement, latency analysis, resource utilization monitoring, scalability testing, and accuracy verification. Throughput measurement involves evaluating requests per second (RPS) under varying concurrency levels (5). Latency analysis, on the other hand, focuses on Time-to-First-Token (TTFT) and Inter-Token Latency (ITL) at different percentile levels, such as 95th and 99th percentiles (5).

In terms of key performance metrics, NVIDIA Dynamo, vLLM, and Triton Server have distinct characteristics (6). For instance, NVIDIA Dynamo's throughput is optimized with dynamic scheduling, while vLLM's throughput is dependent on Ray's pipeline parallelism (6). Triton Server's throughput, however, is configurable via batch size (6). Additionally, latency is a critical metric, with NVIDIA Dynamo reducing TTFT via KV cache offloading, and Triton Server optimizing TTFT with CUDA graphs (6).

Resource utilization monitoring is also essential, as it helps evaluate the efficiency of GPU, CPU, and memory usage across different batch sizes and model sizes (5). Scalability testing, which involves evaluating horizontal (multi-node) and vertical (multi-GPU) scaling efficiency, is another crucial aspect of benchmarking (5). Finally, accuracy verification ensures that inference outputs match across platforms for the same inputs, which is vital for reliable performance comparison (6). By using these benchmarking methodologies and evaluating key performance metrics, developers and researchers can make informed decisions when choosing between NVIDIA Dynamo, vLLM, and Triton Server for their large language model inference workloads (5).

Sources

Source 1

Query: NVIDIA Dynamo optimization techniques for LLM inference

Answer: NVIDIA Dynamo Optimization Techniques for LLM Inference

Disaggregated Serving:
- Separate prefill and decode stages to maximize GPU throughput and balance latency.
- Example: For Llama 405b w8a8 on H100, use TP8 on a single node or TP8PP2 on two nodes.
Dynamic GPU Scheduling:
- Allocate GPUs based on real-time demand to prevent under/over-provisioning.
- Automatically scale prefill workers in response to increased demand (e.g., long input sequences).
KV-Aware Request Routing:
- Route requests to workers with the highest KV cache hit rate for reduced latency.
- Achieves up to 3x improvement in Time-To-First-Token (TTFT) and 2x reduction in average request latency.
Distributed KV Cache Manager:
- Offload less frequently accessed KV cache to cost-effective memory hierarchies (CPU, SSD, object storage).
- Enables storing petabytes of KV cache data at a fraction of GPU memory costs.
- Example: 40% improvement in TTFT with system memory offloading in multi-turn conversations.
Accelerated Data Transfer with NIXL:
- Simplifies synchronization and batching for low-latency data transfer across heterogeneous memory.
- Essential for minimal latency in disaggregated serving (prefill to decode worker transfers).
Performance Tuning for Disaggregation:
- Engine Knobs: Optimize parallelization mapping (e.g., TP within node, PP across nodes).
- Disaggregated Router Knobs: Adjust based on load and SLA requirements.
- Engine Count: Dynamically adjust prefill and decode engine numbers based on workload (low, medium, high load conditions).
  - Low Load: Single monolithic engine.
  - Medium Load: Disaggregation for better ITL and TTFT.
  - High Load: Scale engines to meet KV cache demand and queue reduction.

Citations: architecture.pdf,architecture.pdf,dynamo_run.pdf,kv_cache_manager.pdf,dynamo_run.pdf,architecture.pdf,disagg_perf_tuning.pdf,architecture.pdf,disagg_perf_tuning.pdf,architecture.pdf

Source 2

Query: NVIDIA Triton Server vs Dynamo for multi-model inference deployment

Answer: Comparison: NVIDIA Triton Server vs Dynamo for Multi-Model Inference Deployment

Feature	NVIDIA Triton Server	Dynamo
Primary Focus	Multi-Model Inference Serving	Distributed Inference for Generative AI/LLMs
Model Support	Broad (TensorFlow, PyTorch, ONNX, etc.)	Specialized (TRT-LLM, vLLM, SGLang, etc.)
Scalability	Highly Scalable	Designed for Large-Scale Distributed Environments
Memory Management	Optimized for GPU Memory	Dynamic KV Cache Management Across Multiple Tiers
Data Transfer	Utilizes Standard Networking Libraries	Features NIXL for Optimized Inference Data Transfer
Open-Source	Open-Source	Fully Open-Source with OSS-First Approach
Programming Languages	C++, Python, etc.	Rust (Performance) & Python (Extensibility)
Deployment	Supports Various Environments	Includes Dynamo Cloud for Kubernetes Deployment
Key Strength	Versatility Across Model Types	Optimized for Low-Latency Generative AI/LLM Workloads

Citations: architecture.pdf,architecture.pdf,architecture.pdf,architecture.pdf,architecture.pdf,README (1).pdf,dynamo_run.pdf,kv_cache_manager.pdf,dynamo_run.pdf,dynamo_cloud.pdf

Source 3

Query: Technical architecture of NVIDIA Dynamo for accelerated inference

Answer: NVIDIA Dynamo Technical Architecture for Accelerated Inference

Key Components

Dynamo Disaggregated Serving
- Separates prefill and decode stages for optimal GPU utilization.
- Enhances throughput and reduces latency in inference workloads.
Dynamo Smart Router (KV-aware Request Routing)
- Routes requests to workers with the highest KV cache hit rate.
- Maintains load balance and expedites decoding using a global radix tree registry.
Dynamo Distributed KV Cache Manager
- Strategically stores and evicts KV caches across multiple memory tiers (GPU, CPU, SSD, Object Storage).
- Supports hierarchical caching and offloading for cost-effective KV cache management.
NVIDIA Inference Transfer Library (NIXL)
- Accelerates data transfer with reduced synchronization and intelligent batching.
- Optimized for dynamic scaling and low-latency storage access in inference.

High-Level Architecture Diagram (Referenced but Not Provided in Text)

API Server: Adapts to task-specific deployments.
Smart Router: Processes user requests for optimal worker allocation.
Workers (Prefill, Decode, etc.): Scale dynamically based on real-time demand signals via an Event Plane.
Dynamo Distributed KV Cache Manager: Manages KV caches across the system.
NIXL: Facilitates efficient data transfer between components.

Implementation Details

Programming Languages:
- Rust: Used for performance-critical modules (speed, memory safety, concurrency).
- Python: Utilized for flexibility, rapid prototyping, and customization.
Distributed Runtime:
- Implemented in Rust with Python bindings.
- Hierarchical structure: DistributedRuntime > Namespace > Component > Endpoint.
- Enables distributed communication and coordination.

Performance Benefits

Disaggregated Serving: Significant performance boost with increased GPU involvement.
KV-aware Routing & Distributed KV Cache Manager: Reduces redundant computations and latency.
Dynamic GPU Scheduling & NIXL: Optimizes resource allocation and accelerates data transfer.

Dynamo Cloud (Deployment Platform)

Components:
- Dynamo Operator: Manages inference graph lifecycles in Kubernetes.
- API Store: Stores and manages service configurations.
- Custom Resources: For defining and managing Dynamo services in Kubernetes.
Prerequisites for Deployment:
- Kubernetes cluster (v1.24+)
- Earthly, Docker, Helm, and kubectl configured.
- Access to a container registry.

Citations: architecture.pdf,architecture.pdf,architecture.pdf,architecture.pdf,dynamo_cloud.pdf,architecture.pdf,kv_cache_manager.pdf,kv_cache_manager.pdf,distributed_runtime.pdf,kv_cache_manager.pdf

Source 4

Query: How does Dynamo's performance in multi-GPU setups compare to VLLM's, especially with the --tensor-parallel-size parameter?

Answer: Dynamo's performance in multi-GPU setups with the --tensor-parallel-size parameter is optimized for disaggregated serving, showing a 30% throughput/GPU improvement in single-node tests and over 2X gains in two-node setups. VLLM, on the other hand, uses ray for pipeline parallel inference, with --tensor-parallel-size defining the total GPUs in the cluster, often constrained by model dimensions.

For VLLM, increasing GPUs can improve throughput/GPU and latency/user for decode-only engines, given sufficient KV cache. In contrast, Dynamo's disaggregated approach separates prefill and decode, maximizing GPU utilization and allowing for tailored performance optimization via worker allocation.

Dynamo's --tensor-parallel-size directly impacts throughput and latency, with optimal settings dependent on the number of GPUs, model size, and specific inference requirements. VLLM's performance with this parameter is more closely tied to the efficiency of ray's pipeline parallelism and the model's compatibility with the specified GPU count.

In summary, while both frameworks leverage --tensor-parallel-size for multi-GPU optimization, Dynamo's disaggregated architecture offers more flexible performance tuning, especially for large models and variable workload conditions, compared to VLLM's pipeline parallel approach.

Citations: dynamo_run.pdf,dynamo_run.pdf,architecture.pdf,architecture.pdf,disagg_perf_tuning.pdf,architecture.pdf,architecture.pdf,disagg_perf_tuning.pdf,kv_cache_manager.pdf,dynamo_run.pdf

Source 5

Query: What are the standard benchmarking methodologies and key performance metrics used to compare NVIDIA Dynamo, vLLM, and Triton Server for large language model inference workloads?

Answer: Standard Benchmarking Methodologies:

Throughput Measurement: Requests per second (RPS) under varying concurrency levels.
Latency Analysis: Time-to-First-Token (TTFT) and Inter-Token Latency (ITL) at different percentile levels (e.g., 95th, 99th).
Resource Utilization Monitoring: GPU memory usage, CPU utilization, and network bandwidth consumption.
Scalability Testing: Horizontal (multi-node) and vertical (multi-GPU) scaling efficiency.
Accuracy Verification: Ensuring inference outputs match expected results across frameworks.

Key Performance Metrics for Comparison:

Metric	NVIDIA Dynamo	vLLM	Triton Server
Throughput (RPS)	Optimized with NIXL and disaggregated serving	Dependent on Ray's pipeline parallelism	Leveraging NVIDIA's TensorRT for acceleration
TTFT (ms)	Reduced via KV-aware routing and GPU scheduling	Influenced by pre-processing and model size	Optimized through model optimization in TensorRT
ITL (ms)	Dynamic scheduling for low latency	Affected by token generation speed in the model	Enhanced by TensorRT's engine optimization
GPU Utilization (%)	High through disaggregated prefill/decode	Variable, depends on model and batch size	Optimized for TensorRT-optimized models
Scalability (Nodes/GPUs)	Designed for multi-node, multi-GPU	Supported via Ray for distributed inference	Supports both with Triton's distributed features
Accuracy (%)	Framework-agnostic, depends on the model	Model-dependent, with potential for Ray's impact	Model-dependent, optimized through TensorRT

Citations: architecture.pdf,architecture.pdf,dynamo_run.pdf,architecture.pdf,dynamo_run.pdf,kv_cache_manager.pdf,architecture.pdf,architecture.pdf,dynamo_run.pdf,dynamo_run.pdf

Source 6