Enterprise AI Analysis
Debunking the CUDA Myth Towards GPU-based AI Systems
This comprehensive analysis evaluates Intel Gaudi NPUs as a viable alternative to NVIDIA GPUs for AI model serving. Despite NVIDIA's strong software ecosystem, Gaudi NPUs demonstrate competitive performance in key AI operations, particularly in matrix multiplications and large language model (LLM) serving. With strategic software optimizations and effective integration into high-level AI frameworks, Gaudi NPUs show significant potential to challenge NVIDIA's market dominance, offering comparable energy efficiency for LLMs and room for growth in areas like vector gather-scatter operations.
Executive Impact & Key Findings
Intel Gaudi-2 NPUs present a compelling alternative to NVIDIA A100 GPUs for AI workloads. Our microbenchmarking reveals Gaudi-2 achieves superior absolute GEMM performance and compute utilization, with 40% higher throughput for BF16 matrix operations and an average 4.5% higher utilization. For large language models (LLMs), Gaudi-2 delivers an average 1.47x speedup and 48% higher energy-efficiency over A100 in single-device serving, increasing to 1.35x speedup for 8-device serving. While Gaudi-2 exhibits competitive memory bandwidth for streaming access patterns, it shows a 2.4x drop in memory performance for fine-grained random memory accesses (e.g., vector gather-scatter operations with <256-byte vector sizes). Collective communication performance is strong when all devices are utilized, but gradually declines with fewer devices due to its P2P link architecture, contrasting with A100's NVSwitch-enabled flexible bandwidth. Significant software optimizations were crucial to unlock Gaudi's potential, such as custom TPC-C kernels for embedding layers achieving 95% of A100's throughput for large vectors and an optimized vLLM implementation achieving competitive end-to-end LLM performance.
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Gaudi-2 GEMM Throughput Advantage (BF16)
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Gaudi-2 LLM Serving Speedup (Single Device)
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Gaudi-2 LLM Energy Efficiency (Single Device)
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Gaudi-2 Memory Performance Drop (Small Vectors)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
This section evaluates the computational prowess of Intel Gaudi NPUs, focusing on Matrix Multiplication Engines (MMEs) for GEMM operations and Tensor Processing Cores (TPCs) for non-GEMM vector operations. It compares Gaudi-2 against NVIDIA A100, highlighting throughput, utilization, and architectural advantages like MME reconfigurability.
40%
Higher BF16 GEMM Throughput
Gaudi-2's MME delivers 40% higher BF16 GEMM throughput than NVIDIA A100's Tensor Cores, contributing to superior absolute performance.
| Feature | Gaudi-2 (Intel) | A100 (NVIDIA) |
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| BF16 TFLOPS (Matrix) |
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| BF16 TFLOPS (Vector) |
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| MME Reconfigurability |
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| Compute Utilization (GEMM) |
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Gaudi MME Reconfigurability Impact
The dynamic reconfigurability of Gaudi-2's MME systolic array is a key architectural advantage. This allows the array to adapt its geometry (height and width) to better align with the input GEMM shapes (M,K,N), significantly enhancing MAC unit utilization. For instance, this feature provides up to 15% improvement in compute utilization compared to a non-configurable, output-stationary systolic array, directly translating to higher efficiency and performance for diverse AI workloads.
This section delves into Gaudi-2's memory system performance, including HBM bandwidth and on-chip SRAM, as well as its inter-chip communication capabilities. It contrasts Gaudi-2's approach to random memory access and collective communication with NVIDIA A100's, highlighting strengths and areas for improvement.
2.4x
Memory Performance Drop for Small Vectors
Gaudi-2 experiences a 2.4x drop in memory throughput for vector gather-scatter operations with data transfer sizes smaller than 256 bytes, compared to A100.
| Feature | Gaudi-2 (Intel) | A100 (NVIDIA) |
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| HBM Bandwidth |
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| On-chip SRAM |
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| Random Memory Access (small vectors) |
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| Collective Communication |
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Communication Bottlenecks in Gaudi-2 Clusters
Intel's HLS-Gaudi-2 server connects chips via P2P RoCEv2 links, meaning effective bandwidth scales proportionally with the number of devices. This contrasts with NVIDIA DGX A100's NVSwitch, which offers full aggregate NVLink bandwidth regardless of the number of communicating GPUs. This difference causes Gaudi-2's collective communication performance to decline almost linearly as fewer devices are used, whereas A100's remains stable. This architectural choice limits Gaudi-2's flexibility in exploiting intra-node network bandwidth, particularly for workloads not fully utilizing all available chips.
This section assesses the programmability of Intel Gaudi NPUs, examining the TPC-C programming model, the Gaudi SDK, and the role of the graph compiler. It discusses software-level optimization strategies demonstrated through case studies of FBGEMM and vLLM implementations.
95%
FBGEMM Throughput vs. A100
Our custom TPC-C kernel for embedding layers achieved 95% of A100's throughput for large embedding vectors, showcasing programmability potential.
DLRM Embedding Layer Optimization Flow
This flowchart illustrates the key steps taken to optimize the DLRM embedding layer on Intel Gaudi-2 using TPC-C, highlighting how kernel batching and memory-level parallelism were leveraged.
Initial Gaudi SDK (SingleTable)
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Identify Kernel Launch Overhead
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Implement BatchedTable (TPC-C)
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Unroll Loops for MLP
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Maximize Memory-Level Parallelism
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Achieve 95% A100 Throughput
vLLM PagedAttention Optimization for Gaudi-2
The vLLM PagedAttention mechanism presents unique challenges on Gaudi-2 due to the SDK's limitations in directly controlling MME units via TPC-C. Our optimization strategy involved refactoring the 2D BlockTable to a 1D BlockList to eliminate redundant KV cache block gathers caused by zero-padding. This, along with restructuring query tensor shapes for batched GEMM, enabled the Gaudi graph compiler to more effectively pipeline TPC-based KV cache block gather operations and MME-based GEMM operations. This PyTorch-level approach significantly improved PagedAttention throughput by 7.4x over the baseline and achieved end-to-end LLM performance competitive with A100, despite the low-level API limitations.
This section presents a holistic evaluation of Gaudi-2's performance and energy efficiency for end-to-end AI applications, specifically Recommendation Systems (RecSys) and Large Language Models (LLMs). It quantifies the speedup and energy-efficiency gains or losses compared to A100.
50%
Higher LLM Energy Efficiency
For LLM serving, Gaudi-2 demonstrates 50% higher energy efficiency than A100 across both single and multi-device deployments.
| Workload | Gaudi-2 (Intel) | A100 (NVIDIA) |
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| RecSys (RM1, RM2) |
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| LLMs (Llama-3.1-8B/70B Instruct) |
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| RecSys Energy Efficiency |
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| LLM Energy Efficiency |
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RecSys Workload Performance Discrepancy
For recommendation systems (RecSys), Gaudi-2 generally lags behind A100, particularly for memory-intensive RM2 models with small embedding vector sizes (<256 bytes), where it experiences up to 70% performance loss. This is primarily attributed to Gaudi-2's 256-byte minimum memory access granularity and reduced memory bandwidth utilization for fine-grained vector gather operations, a pattern observed in our microbenchmark analysis. Despite custom TPC-C kernel optimizations for embedding layers improving throughput to 95% of A100's, the end-to-end RecSys performance for Gaudi-2 still shows an average 20% slowdown and 28% lower energy efficiency compared to A100.
Calculate Your Potential AI ROI
Estimate the efficiency gains and cost savings your enterprise could achieve by optimizing AI workloads with advanced accelerators like Intel Gaudi NPUs.
Estimated Annual Savings
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Productive Hours Reclaimed Annually
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Implementation Timeline & Strategic Roadmap
A phased approach to integrate Intel Gaudi NPUs into your enterprise AI infrastructure, ensuring a smooth transition and maximizing performance gains.
Phase 1: AI Readiness Assessment
Evaluate current infrastructure, identify key workloads, and define success metrics for AI integration. This phase includes a detailed audit of existing data pipelines and computing resources to determine compatibility and necessary upgrades for Gaudi NPU adoption. Initial workshops with key stakeholders will establish a clear vision for AI deployment.
Phase 2: Pilot Program with Gaudi NPUs
Deploy Gaudi NPUs for a selected, high-impact AI workload (e.g., LLM inference or a specific RecSys component). Develop custom TPC-C kernels where needed for performance-critical operations and integrate with existing PyTorch/TensorFlow frameworks. Benchmark performance and energy efficiency against current baselines (e.g., NVIDIA A100) to validate potential ROI.
Phase 3: Production Rollout & Optimization
Scale up Gaudi NPU deployment across identified workloads, incorporating best practices for multi-device serving and collective communication. Continuously monitor performance and energy consumption, applying ongoing software optimizations at both high and low levels. Establish a feedback loop for continuous improvement and adaptation to new AI models and SDK updates.
Phase 4: Ecosystem Integration & Future-Proofing
Integrate Gaudi NPUs into the broader enterprise AI ecosystem, ensuring compatibility with data platforms, MLOps tools, and other enterprise systems. Explore advanced features and future Gaudi generations (e.g., Gaudi-3) to maintain competitive advantage. Develop internal expertise and contribute to the Gaudi community to foster long-term sustainability and innovation.
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