Raspberry Pi Model 5: Edge AI Platform

• Raspberry Pi Model 5 is a single-board computer featuring a quad-core ARM Cortex-A76 CPU, 8GB LPDDR4X memory, and a robust design for edge AI applications.
• It supports real-time deep neural network inference with INT8 quantization for vision models and optimized LLM inference up to 1.5B parameters under strict thermal and power constraints.
• System-level tunability including aggressive quantization, dynamic thermal management, and CPU affinity tuning makes it a cost-effective platform for resource-constrained edge AI research and deployments.

The Raspberry Pi Model 5 is a single-board computer (SBC) distinguished by its quad-core ARM Cortex-A76 CPU and 8 GB LPDDR4X memory, engineered for edge computing and embedded AI workloads. Owing to its microarchitecture, high memory bandwidth, and energy efficiency, the device supports real-time deep neural network inference for both vision and LLMs under thermal and power constraints that are characteristic of battery-powered or headless edge deployments. Its performance profile, thermal management, and system-level tunability have made it a subject of academic scrutiny for low-latency, resource-constrained AI inference, as documented in recent benchmarks on INT8 object detection and quantized LLM workloads (Boddu et al., 10 Jun 2025, Tung et al., 20 Oct 2025).

1. System Architecture and Hardware Characteristics

The Raspberry Pi 5 is built around the Broadcom BCM2712 SoC, which integrates four ARM Cortex-A76 performance cores operating at 2.4 GHz. The platform employs 8 GB LPDDR4X memory (either single- or dual-channel at 4266–4267 MT/s, with up to 34 GB/s peak bandwidth) and provides modern I/O including USB 3.0, Gigabit Ethernet, and PCIe Gen2 expansion lanes. The device’s storage is typically provisioned via a high-speed UHS-I microSD card. Power is supplied through a 5 V USB-C interface, with inline wattage monitoring feasible for research on energy efficiency.

A metal heat spreader is integrated for passive cooling, with provisions for an active 30 mm fan for sustained high-load operation. Over extended workloads, a heatsink and fan are recommended to prevent SoC junction temperatures from exceeding 70–80 °C, thereby avoiding thermal throttling and ensuring computational determinism (Boddu et al., 10 Jun 2025, Tung et al., 20 Oct 2025).

2. Deep Neural Network (DNN) Vision Model Inference

Benchmarking with YOLOv4-Tiny quantized to INT8 via TensorFlow Lite demonstrates the Pi 5’s viability for real-time vision inference in aerial emergency object-detection. The model runs entirely on the CPU (VideoCore VII GPU is not leveraged), utilizing NEON-optimized C++ kernels. Experimental evaluation (100 images, 416×416 px) reports:

Metric	Value	Unit
Inference time	28.2 ±1.3	ms/image
Frame rate	35.5	fps
CPU utilization	~95%	–
Power consumption	13.85	W
On-board temp (idle)	45	°C
On-board temp (load)	65	°C

The INT8 quantization is pivotal, yielding ~35 fps at 13.85 W, in contrast to 7–8 fps and >20 W for FP32 models, thus enabling battery-powered operation in remote nodes. CPU occupancy remains at ~95%, with nearly zero GPU utilization, underscoring the importance of CPU-bound optimization on this platform. Quantized inference displays tight runtime variance (26.9–29.5 ms/image), attesting to stable kernel performance (Boddu et al., 10 Jun 2025).

3. LLM Inference and Edge NLP

The Raspberry Pi 5 supports quantized LLM inference for models up to 1.5B parameters, leveraging q4_k_m quantization (0.5 bytes/parameter plus ~0.5 GB runtime overhead). Empirical benchmarks with TinyLlama (1B) and Qwen2.5 (1.5B) yield generation rates of 13.2 tokens/s and 9.8 tokens/s, respectively, with Ollama; or up to 28.5 tokens/s via Llamafile. Memory requirements dictate that 1.5B models approach the practical 8 GB ceiling, with observable swapping and thrashing above ~7.5 GB usage.

Power demand remains within 6.8–10.0 W at inference peaks, with Llamafile delivering 30–40% energy savings relative to Ollama owing to more efficient runtime implementation. Runtime selection, threading, and affinity tuning are critical deployment variables, with explicit recommendations:

• “Performance” CPU governor locked to prevent DVFS-induced frequency jitter.
• All four Cortex-A76 cores are leveraged, with core affinity set to isolate model inference and minimize context switches.
• Swap is disabled and overcommit set to “never” for OOM determinism (Tung et al., 20 Oct 2025).

4. System and Architectural Bottlenecks

Key bottlenecks for edge inference workloads are explicit:

• Memory bandwidth: LPDDR4X at 34 GB/s is generally adequate for small-to-medium workloads; contention surfaces as model weights and attention matrices outstrip L2 cache, impeding scaling across threads.
• Cache hierarchy: Each core receives a 1MB L2 cache with no L3; large model blocks regularly evict upper cache lines, especially for LLM inference.
• SIMD/vector execution: Though the Cortex-A76 supports NEON and partial SVE2, inference runtimes for both vision and language workloads predominantly exercise scalar and legacy vector operations; end-to-end throughput is thus often limited by quantized scalar loops rather than vectorized pipelines.
• GPU inaccessibility: The VideoCore VII GPU is not integrated into TFLite or LLM inference, and all matrix operations execute on the ARM CPU cores. This constrains further acceleration unless future software releases expose suitable GPU kernels (Boddu et al., 10 Jun 2025, Tung et al., 20 Oct 2025).

5. Deployment Recommendations and Practical Implications

Researchers and practitioners are advised to adhere to aggressive quantization and system-level optimization recipes:

• Target INT8 quantization for vision models and q4_k_m for LLMs; this reduces RAM requirements and power overhead (down to ~14 W for vision, <10 W for LLMs).
• Constrain model sizes to ≤1.5B parameters for LLMs to avoid reliability and paging events.
• Set the CPU governor to “performance,” pin processes to discrete cores, and minimize OS-peripheral load by disabling unused services.
• Employ modest active cooling solutions for workloads exceeding 30 s continuous execution; passive cooling suffices for burst workloads.
• For real-time video inference, batch inputs in small groups to amortize data movement overheads and improve power efficiency.
• Lower input resolution or response time requirements if sub-maximum throughput is tolerable, further reducing energy demands.
• The Pi 5 platform is well-suited for privacy-sensitive and offline applications, including healthcare, fieldwork, and in situ automation, as well as safety-critical deployments such as emergency response and aerial object detection (Boddu et al., 10 Jun 2025, Tung et al., 20 Oct 2025).

6. Research Significance and Emerging Directions

The Raspberry Pi 5 exemplifies a new baseline for affordable, general-purpose single-board compute in the context of edge AI. Its ability to host both vision DNNs (YOLOv4-Tiny INT8) and LLMs (up to 1.5B parameters with q4_k_m quantization) within stringent power and thermal envelopes establishes a reference point for future edge AI investigations. The current research foregrounds the importance of quantization, hierarchical memory optimization, and deliberate runtime tuning, and highlights the architectural gap in accessible on-device acceleration for neural inference. A plausible implication is that future research should address the expose of GPU capabilities to inference engines and explore architectural co-design for further energy reduction without compromising throughput or accuracy.

References:

[Efficient Edge Deployment of Quantized YOLOv4-Tiny for Aerial Emergency Object Detection on Raspberry Pi 5, (Boddu et al., 10 Jun 2025)] [An Evaluation of LLMs Inference on Popular Single-board Computers, (Tung et al., 20 Oct 2025)]