Gino George John

Efficient Home Server Infrastructure for Mixed-Criticality Workloads

Convergence at the Edge: Architectural Analysis of Energy-Efficient Home Server Infrastructure for Mixed-Criticality Workloads

1. Executive Summary

The contemporary home laboratory has evolved from a hobbyist pursuit into a critical infrastructure layer, hosting services that rival small business enterprise deployments in complexity and utility. This report addresses the architectural requirements for a “Converged Home Server”—a single node capable of sustaining a heterogeneous workload comprised of Network Video Recording (Frigate NVR), Home Automation (Home Assistant), Enterprise Resource Planning (ERPNext), Network Security (AdGuard), and emerging Artificial Intelligence applications (Small Language Models and AI Penetration Testing tools).

The primary constraint of this analysis is the optimization of energy efficiency and operational cost without compromising the performance required for real-time object detection on 12 camera streams and responsive AI inference. This “performance-per-watt” paradigm necessitates a departure from traditional used enterprise rack-mount gear (e.g., Dell PowerEdge R720) toward modern, high-density micro-architectures found in Mini PCs and Mini-ITX embedded platforms.

Our exhaustive analysis of the current hardware landscape, specifically evaluating Intel Raptor Lake (13th Gen), Intel Meteor Lake (Core Ultra), and AMD Zen 4 architectures, yields distinct stratified recommendations. For pure operational efficiency and reliability, the HP Elite Mini 800 G9 (Intel Core i5-13500T) emerges as the superior platform, leveraging mature Intel QuickSync technology to handle video workloads at near-zero incremental power cost. For users requiring high-speed networking (10GbE) and hybrid storage flexibility, the Minisforum MS-01 provides a workstation-class bridge solution, albeit with a higher thermal and energy penalty. The Minisforum BD795i represents the high-performance tier, offering desktop-class compute density for users prioritizing AI throughput over absolute idle efficiency.

This report details the architectural trade-offs, thermal dynamics, and total cost of ownership (TCO) for these systems, providing a comprehensive blueprint for deploying a 2025-ready home server infrastructure.

2. Workload Characterization and Resource Dimensioning

To architect a system that is both performant and efficient, one must first deconstruct the target workloads into their fundamental computational primitives. The proposed software stack represents a “Mixed-Criticality” workload, where real-time, continuous processes (NVR) compete for resources with bursty, latency-sensitive applications (SLM, ERP).

2.1 The Continuous Compute Baseline: Frigate NVR

Frigate NVR transforms a passive video storage server into an active AI-driven security appliance. For a deployment of 12 cameras, the computational demand is bifurcated into two distinct pipelines: Video Decoding and Object Detection.

2.1.1 The Mechanics of Video Decoding

Before any analysis can occur, the compressed H.264 or H.265 video streams must be decoded into raw frames (YUV).

  • Throughput Dynamics: A single 4K (8MP) stream at 5 frames per second (FPS) typically requires decoding ~40 megapixels per second. With 12 cameras—assuming a mix of 4K and 1080p—the system must handle a sustained decode throughput of roughly 300-500 megapixels per second.
  • The CPU Bottleneck: Attempting to perform this decode via software (CPU-based libavcodec) is catastrophically inefficient. On a modern CPU, this could consume 60-80% of available cycles, driving package power to 60W+ and generating significant heat.
  • The Hardware Imperative: The selection of the hardware platform is therefore dictated by its Fixed-Function Video Decode capabilities. Intel’s QuickSync Video (QSV) engine is the industry standard for this task. The Intel UHD Graphics 770 (found in the i5-13500T) features two Multi-Format Codec (MFX) engines, capable of decoding dozens of streams simultaneously with negligible power draw (typically <3W for the media engine).1 AMD’s VCN (Video Core Next) engine has improved significantly but historically lags in driver maturity and FFmpeg integration within Docker containers, often requiring more manual configuration to achieve stability parity with Intel.

2.1.2 The Inference Engine: Tensor Math

Once decoded, Frigate inspects specific regions of frames for objects. This relies on quantization-heavy tensor operations (INT8 precision).

  • Inference Velocity: For 12 cameras running detection at 5 FPS, the system requires a total throughput of 60 Inferences Per Second (IPS).
  • The Google Coral Paradigm Shift: Historically, the Google Coral Edge TPU was a mandatory addition, offering ~100 IPS at 2 watts. However, availability constraints and the stagnation of the Edge TPU hardware (unchanged since 2019) have forced a re-evaluation.
  • OpenVINO Viability: Modern Intel iGPUs (12th Gen+) utilizing the OpenVINO toolkit have effectively closed the gap. Benchmarks indicate that an Intel UHD 770 can sustain inference speeds of 10-15ms for MobileNetV2 models.1 While slightly slower than the Coral (~8ms), this performance is well within the 100ms budget required for real-time tracking. Consequently, for an energy-efficient build, relying on the iGPU eliminates the need for a separate accelerator, simplifying the bill of materials and reducing idle power associated with PCIe/USB root hubs.2

2.2 Enterprise Resource Planning: ERPNext

ERPNext represents a monolithic business application architecture. Unlike the streaming nature of Frigate, ERPNext is transaction-oriented.

  • Database Dependency: The core of ERPNext is MariaDB. Database performance in a home lab is rarely CPU-bound; it is I/O bound. The responsiveness of the ERP interface depends on the storage subsystem’s Random 4K Read/Write performance (IOPS).
  • Memory Pressure: ERPNext, combined with its Redis caching layer and background workers (Celery), is memory hungry. A stable production-like instance can easily reserve 4-8GB of RAM.
  • Storage Contention: Running a transactional database alongside an NVR (which writes sequential large blocks constantly) invites “I/O Wait” latency. The mechanical hard drives (HDDs) of yore would collapse under this mixed load. The requirement for NVMe storage is not merely for speed, but for Queue Depth. NVMe protocols allow for massive parallelism (64,000 queues), enabling the database to slip small transaction writes in between the massive video stream writes of the NVR without blocking.4

2.3 The Emerging Frontier: Small Language Models (SLM) & AI Tools

The request to host SLMs (like Microsoft’s Phi-3, Mistral 7B, or Llama-3-8B) and “AI Ethical hacking tools” (e.g., Hashcat for password recovery, automated pentesting agents) introduces a fundamentally different resource constraint: Memory Bandwidth and Parallel Compute.

2.3.1 The “VRAM” Bottleneck

Large Language Models (and SLMs) are bandwidth-starved. In a system without a discrete GPU (dGPU), the system RAM functions as VRAM.

  • Bandwidth Scaling: DDR5 memory (4800 MT/s or 5600 MT/s) is essential. A dual-channel DDR5-5600 setup offers ~89.6 GB/s of bandwidth. While dwarfed by a dedicated GPU (e.g., RTX 4090 ~1000 GB/s), it is sufficient for running quantized models (Q4_K_M) at readable speeds (10-20 tokens per second).
  • Capacity Planning: A 7B parameter model at 4-bit quantization occupies ~5GB of RAM. However, the “Context Window” (the conversation history) consumes exponentially more as it grows. To run an SLM alongside ERPNext, Frigate, and Home Assistant, 32GB of RAM is the absolute minimum viable configuration. For a robust experience that allows for future model growth (e.g., 13B models) and concurrent AI tools, 64GB is the recommended baseline.5

2.3.2 The Compute Divergence: Hashcat & Pentesting

“AI Ethical hacking Tools” is a broad category.

  • Automated Recon: Tools that scan networks or analyze code (e.g., Nuclei, Static Analysis) are CPU-bound and benefit from high core counts.
  • Password Cracking: Tools like Hashcat are GPU-bound. They rely on massive parallel integer operations. Here, the Integrated GPU (iGPU) becomes a limiting factor. While AMD’s Radeon 780M (RDNA3) is significantly faster than Intel’s UHD 770 in Hashcat benchmarks, neither holds a candle to even a low-end discrete GPU.6 If the user’s “Ethical hacking” requirement heavily prioritizes password cracking, a system with a PCIe slot for a discrete GPU (like the Minisforum MS-01 or BD795i) becomes mandatory.

2.4 Virtualization Layer: Proxmox VE

The choice of Proxmox VE as the hypervisor provides a robust, enterprise-grade foundation based on KVM (Kernel-based Virtual Machine) and LXC (Linux Containers).

  • Resource Overhead: Proxmox itself is lightweight (~1-2GB RAM).
  • Architectural Efficiency: The primary decision point for energy efficiency is LXC vs. VM.
  • LXC (Containers): Containers share the host kernel. This allows multiple containers (e.g., Frigate, Plex, Jellyfin) to share the iGPU resource dynamically without complex “partitioning” (SR-IOV). This is the most energy-efficient approach as it avoids the overhead of full hardware emulation.
  • VM (Virtual Machines): VMs provide security isolation—critical for “AI Ethical hacking tools” that might be handling malware or sensitive targets. However, passing a hardware device (like the iGPU or Coral TPU) to a VM typically dedicates it to that VM exclusively (PCIe Passthrough), preventing its use by other services.
  • Recommendation: A hybrid approach. Run core services (Frigate, Home Assistant, AdGuard) in LXC containers for efficiency and resource sharing. Run sensitive or distinct workloads (ERPNext, Kali Linux/Ethical hacking Tools) in isolated VMs.2

3. Hardware Architecture Deep Dive

The market currently offers three distinct micro-architectural paths relevant to this deployment, each with unique thermal and efficiency profiles.

3.1 Intel Hybrid Architecture (Raptor Lake / Alder Lake)

  • Representative CPUs: Core i5-13500T, i9-12900H.
  • Mechanism: Combines “Performance Cores” (P-Cores) for burst loads and “Efficiency Cores” (E-Cores) for background tasks.
  • Implication for Home Lab: This architecture is ideal for the user’s converged workload. Frigate and AdGuard can be pinned to E-Cores, sipping power, while ERPNext transactions or AI inference bursts can trigger the P-Cores.
  • QuickSync Dominance: The unassailable advantage of Intel in the home server space remains QuickSync. The media engines are separated from the compute execution units, meaning heavy video decoding does not slow down the CPU for other tasks.2

3.2 AMD Zen 4 & RDNA 3 (Phoenix / Hawk Point)

  • Representative CPUs: Ryzen 7 8845HS, Ryzen 9 7945HX.
  • Mechanism: High-performance cores with strong integrated graphics (Radeon 780M).
  • The Chiplet Penalty: High-end AMD CPUs (like the 7945HX) use a “chiplet” design, where cores are on separate silicon dies from the I/O die. This interconnect consumes significant power even at idle, often resulting in a 30-40W idle floor compared to 10W on monolithic Intel chips.9 Monolithic AMD chips (like the 8845HS) avoid this but still struggle with lower C-state residency compared to Intel’s mature desktop platforms.
  • NPU Potential: The Ryzen 8000 series includes the XDNA NPU (16 TOPS). While promising for offloading SLMs, software support in the Linux kernel is currently in its infancy. For a “set and forget” server in 2025, relying on the NPU is a risk compared to the established OpenVINO/CUDA ecosystems.10

3.3 Intel Core Ultra (Meteor Lake)

  • Representative CPU: Core Ultra 5 125H (Asus NUC 14 Pro).
  • Mechanism: A tile-based architecture introducing a dedicated NPU and a significantly more powerful “Arc” iGPU.
  • The Future-Proofing Bet: The Arc iGPU offers roughly 2x the shading performance of the older UHD graphics, making it better for SLM inference and transcoding. The NPU (“Intel AI Boost”) is rapidly gaining support in OpenVINO, potentially allowing Frigate to offload detection to the NPU entirely in future updates (0.17+), freeing the GPU for other tasks.1 However, early BIOS and driver maturity issues have led to mixed reports on idle power efficiency.12

4. Candidate System Analysis

Based on the requirements for energy efficiency, NVMe support, and range (Mini PC to Mini Server), we have selected four primary candidates.

4.1 The Enterprise Efficiency Champion: HP Elite Mini 800 G9

This unit represents the pinnacle of the “1-Liter” corporate desktop form factor. Engineered for massive fleet deployments, it prioritizes reliability and idle efficiency above all else.

  • Processor: Intel Core i5-13500T (35W TDP).
  • Config: 6 P-Cores + 8 E-Cores (20 Threads). This provides ample threading for Proxmox.
  • Efficiency: The “T” series designation implies a strictly enforced power limit. However, for short bursts (like web page loads in ERPNext), it can boost significantly. Its defining characteristic is its ability to drop into deep C-states (C8/C10) instantly, resulting in idle power draw as low as 10-12 Watts at the wall.13
  • Graphics & AI: Features the UHD 770 iGPU.
  • Frigate: Capable of decoding 20+ 1080p streams via QuickSync. Capable of running OpenVINO detection for 12 cameras with <20% total GPU load.
  • Ethical hacking Tools: Weak for password cracking, but sufficient for basic tasks.
  • Storage & Expansion:
  • NVMe: Two M.2 2280 PCIe Gen4 slots. This is critical. It allows for a ZFS Mirror (RAID 1) configuration for the OS/VMs, or separate drives for OS and NVR storage.
  • FlexIO: The hidden superpower of the HP Mini. It features a modular port system (“FlexIO”) that allows the addition of a native 10GbE NIC or 2.5GbE NIC without consuming an M.2 slot or using unreliable USB dongles.15
  • RAM: Officially supports 64GB DDR5-4800. Community validation confirms 96GB (2x48GB) modules work perfectly, providing huge headroom for SLMs.17
  • Why it wins: Lowest TCO, highest reliability, best software support (Intel QSV).

4.2 The Converged Workstation: Minisforum MS-01

The MS-01 is a category-defying device, bridging the gap between a NUC and a rack server. It is designed for the “homelabber” who needs 10GbE networking and hybrid storage.

  • Processor: Intel Core i9-12900H or 13900H.
  • Power Penalty: These are 45W+ mobile workstation chips. They are tuned for performance, not idle efficiency. Expect idle power to sit around 25-30 Watts, more than double the HP G9.19
  • Networking: The standout feature is the inclusion of Dual 10GbE SFP+ ports (Intel X710 controller). For users with a 10GbE fiber backbone, this eliminates the need for hot, power-hungry RJ45 transceivers.
  • Expansion: It includes a PCIe 4.0 x8 Slot.
  • Strategic Advantage: This slot allows the installation of a low-profile discrete GPU (like the NVIDIA RTX A2000 or RTX 3050). For the user’s “AI Ethical hacking Tools” requirement, this is game-changing. Hashcat performance on an RTX A2000 is exponentially higher than any iGPU.
  • Storage Complexity: It features three M.2 slots, but with caveats. Slot 1 is Gen4x4. Slot 2 is Gen3x4. Slot 3 is Gen3x2.21 Careful drive placement is required (e.g., OS/ERP on Slot 1, Frigate on Slot 2).
  • Thermal Dynamics: The i9 runs hot. The MS-01 includes active cooling for the SSDs, which is excellent for NVMe longevity but adds fan noise.

4.3 The DIY Mini-Server: Minisforum BD795i

For the user who finds “Mini PCs” too limiting and rack servers too loud, the BD795i offers a Mini-ITX motherboard solution.

  • Processor: AMD Ryzen 9 7945HX (16 Cores / 32 Threads).
  • Performance: This is effectively a desktop 7950X in a mobile package. It obliterates the Intel chips in raw multi-core compilation or virtualization tasks.
  • Efficiency: Due to the chiplet design, idle power is high (~40W+).
  • PCIe Gen 5: It features a full PCIe 5.0 x16 slot.
  • Implication: This allows the user to build a system with a full-size GPU (e.g., RTX 3090, 4090) in a standard ITX case. If the “SLM” requirement extends to running large models (70B parameters) locally, this is the only viable path.23
  • Cooling: It comes with a CPU heatsink but requires a user-supplied 120mm fan. This allows for near-silent operation compared to the 40mm blower fans of the MS-01 or HP G9.

4.4 The AI APU: Beelink SER8

A representative of the modern AMD “Mini PC.”

  • Processor: Ryzen 7 8845HS.
  • Graphics: Radeon 780M.
  • Pros: Great for gaming/simulation.
  • Cons: Video encode/decode support in Linux/Docker is improving but still less “plug-and-play” than Intel QSV.
  • Power Issues: The SER8 has documented BIOS issues causing high idle power (~18W) which can be mitigated to ~8W with specific driver tweaks in Windows, but Linux support for these power states is inconsistent.24
  • Recommendation: A strong contender if the user prioritizes CPU compute per dollar, but less optimal for an NVR-centric build due to the QSV advantage of Intel.

5. Implementation Strategy: Addressing the Converged Stack

5.1 Optimization of Frigate on Proxmox

To achieve the “energy efficiency” goal, the configuration of Frigate is critical.

  • Passthrough: In a Proxmox LXC container, the iGPU device (/dev/dri/renderD128) must be passed through. This allows Frigate to access the hardware without VM overhead.
  • OpenVINO Configuration:
    YAML
    detectors:
      ov:
        type: openvino
        device: AUTO
        model:
          path: /openvino-model/ssdlite_mobilenet_v2.xml

    Using device: AUTO allows the Intel drivers to intelligently balance load between the CPU and iGPU execution units. For 12 cameras, explicitly setting device: GPU is often preferred to ensure the CPU remains idle.1

5.2 Optimizing NVMe for Energy and Endurance

  • Thermal Throttling: NVMe drives consume 5-8W under load and can reach 70°C+ quickly in restricted airflow environments (like the HP G9). At 70°C, they throttle, killing ERPNext performance.
  • Mitigation: Apply high-quality thermal pads (e.g., Gelid GP-Extreme) bridging the SSD controller to the chassis metal or the included heatsink caddy.
  • Endurance (TBW): Continuous video recording is brutal on SSDs.
  • Drive Selection: Avoid QLC drives (e.g., Crucial P3, Intel 660p). Their low endurance means a 12-camera setup could exhaust the drive’s writable life in <18 months. Use TLC drives (e.g., WD Red SN700, Samsung 970 EVO Plus, SK Hynix Gold P31). The SK Hynix P31 is particularly noted for its exceptional power efficiency (laptop-class), making it ideal for this build.4

5.3 Network Architecture for Security

  • AdGuard & Isolation: Running AdGuard Home acts as the DNS sinkhole.
  • VLANs: With the converged server, it is best practice to trunk VLANs to the Proxmox bridge.
  • Camera VLAN: Isolated, no internet access. Frigate interface connects here.
  • IoT VLAN: Home Assistant connects here.
  • Management VLAN: Proxmox interface.
    The HP Elite Mini G9’s ability to add a second physical NIC via FlexIO is advantageous here, allowing physical separation of the Camera network from the LAN if VLAN switching is not available.15

6. Comparative Analysis: Price, Power, and Capability

The following table synthesizes the data to aid decision-making. Prices reflect the current market for late 2024/early 2025.

Table 1: Converged Home Server Platform Comparison

FeatureHP Elite Mini 800 G9Minisforum MS-01Beelink SER8Minisforum BD795i (ITX)
System TypeEnterprise “Tiny” PCWorkstation Mini PCConsumer Mini PCDIY Mini-Server (ITX)
CPUIntel Core i5-13500T (14C/20T)Intel Core i9-12900H (14C/20T)AMD Ryzen 7 8845HS (8C/16T)AMD Ryzen 9 7945HX (16C/32T)
Video DecodeIntel QuickSync (UHD 770)Intel QuickSync (Iris Xe)AMD VCN (RDNA3)AMD VCN (RDNA 610M)
AI InferenceOpenVINO (Good)OpenVINO (Good)Ryzen AI (Immature)CPU / Discrete GPU
Discrete GPUNoYes (PCIe x8 Low Profile)NoYes (PCIe x16 Full)
Max RAM64GB (96GB Tested)64GB (96GB Tested)64GB64GB (96GB Tested)
Networking1GbE (10GbE via FlexIO)2x 10GbE SFP+2.5GbE2.5GbE
Storage2x M.2 NVMe Gen43x M.2 NVMe (Mixed)2x M.2 NVMe Gen42x M.2 Gen5 + PCIe
Idle Power~11 – 13 W~28 – 32 W~16 – 18 W~40 – 50 W
Load Power (Est)~45 W~90 W~65 W~140 W
Noise LevelSilent / QuietAudible under loadQuietSilent (Depends on Fan)
Est. Price$450 – $550 (Refurb/Used)$680 (New)$600 (New)$500 (Board Only) + Case/PSU
RecommendationEfficiency & ReliabilityNetworking & Ethical Hacking ToolsGeneral ComputeMax AI / SLM Power

7. Operational Cost Analysis (TCO)

The “running cost” requirement is significant for a 24/7 device.

  • Assumptions: $0.25 per kWh. 24 hours/day. 365 days/year.
  • Load Profile: 80% Idle (NVR recording only), 20% Active (AI inference/ERP use).

HP Elite Mini 800 G9:

  • Average Wattage: ~18W (11W idle + 7W NVR storage/decode).
  • Annual Consumption: 157 kWh.
  • Annual Cost: $39.25

Minisforum MS-01:

  • Average Wattage: ~40W (30W idle + 10W NVR/Networking).
  • Annual Consumption: 350 kWh.
  • Annual Cost: $87.50

Minisforum BD795i (with GPU):

  • Average Wattage: ~70W (40W idle + 15W GPU idle + 15W active).
  • Annual Consumption: 613 kWh.
  • Annual Cost: $153.25

Insight: Over a 5-year operational lifespan, the HP Elite Mini G9 saves roughly $240 in electricity compared to the MS-01, and $570 compared to the BD795i. This cost difference effectively pays for a 4TB NVMe drive or a memory upgrade.

8. Conclusion and Final Recommendation

The architectural decision ultimately rests on the user’s prioritization of “Efficiency” versus “Capability” regarding the AI Ethical hacking Tools.

The “Rational” Choice (Recommended): HP Elite Mini 800 G9

For a user whose primary focus is “energy efficiency and running cost,” the HP Elite Mini 800 G9 is the unequivocal winner. It handles the 12-camera Frigate load effortlessly via QuickSync, supports the necessary RAM for SLMs, and offers enterprise reliability at the lowest power envelope.

  • Configuration: i5-13500T, 64GB DDR5, 2x 2TB NVMe (ZFS Mirror), HP 10GbE FlexIO Module.

The “Power User” Choice: Minisforum MS-01

If the “AI Ethical hacking Tools” requirement specifically involves heavy password cracking or CUDA-exclusive workloads, the MS-01 is the only form factor that bridges the gap. The ability to slot in an NVIDIA RTX A2000 makes it a compact powerhouse, justifying the higher operational cost with vastly superior capabilities in specific AI domains.

  • Configuration: i9-12900H, 96GB DDR5, 1x 2TB Gen4 NVMe (OS), 1x 4TB Gen3 NVMe (NVR), NVIDIA RTX A2000.

The “Visionary” Choice: Minisforum BD795i

For users intending to run large-scale local LLMs (70B+) or act as a high-performance build server, the ITX form factor allows for a full-size GPU and superior cooling. This is a “Mini Server” in the truest sense, sacrificing efficiency for raw, unadulterated throughput.

Final Verdict: Start with the HP Elite Mini 800 G9. Its performance-per-watt metric is unbeatable for the defined converged workload. Add a USB Coral TPU only if OpenVINO usage impacts ERPNext latency, though our analysis suggests the iGPU will suffice. This path minimizes initial outlay, minimizes monthly bills, and maximizes reliability.

Works cited

  1. Recommended hardware | Frigate, accessed December 27, 2025, https://docs.frigate.video/frigate/hardware/
  2. Frigate coral vs. openvino sharing the igpu : r/frigate_nvr – Reddit, accessed December 27, 2025, https://www.reddit.com/r/frigate_nvr/comments/1hpcivl/frigate_coral_vs_openvino_sharing_the_igpu/
  3. A year with Frigate and the switch from Coral to a 4060 running ONNX : r/frigate_nvr – Reddit, accessed December 27, 2025, https://www.reddit.com/r/frigate_nvr/comments/1o9byfn/a_year_with_frigate_and_the_switch_from_coral_to/
  4. Best Proxmox Compute Server Builds (2025): Why Your Homelab Needs a Dedicated Compute Node – DiyMediaServer, accessed December 27, 2025, https://diymediaserver.com/post/media-server-compute-2025/
  5. Recommended Hardware for Running LLMs Locally – GeeksforGeeks, accessed December 27, 2025, https://www.geeksforgeeks.org/deep-learning/recommended-hardware-for-running-llms-locally/
  6. Hashcat on AMD 780M, accessed December 27, 2025, https://hashcat.net/forum/thread-12225.html
  7. Intel Arc iGPU (core ultra 5 125H) vs Amd 780m (r7 8845hs) : r/IntelArc – Reddit, accessed December 27, 2025, https://www.reddit.com/r/IntelArc/comments/1ftpu9y/intel_arc_igpu_core_ultra_5_125h_vs_amd_780m_r7/
  8. Discussion comparing coral and iGPU (OpenVino) as detector, pros & cons #5742 – GitHub, accessed December 27, 2025, https://github.com/blakeblackshear/frigate/discussions/5742
  9. Storage and device expansion on minisforum BD795i SE motherboard : r/homelab – Reddit, accessed December 27, 2025, https://www.reddit.com/r/homelab/comments/1hr3k85/storage_and_device_expansion_on_minisforum_bd795i/
  10. AMD-specific Ollama Alternative? – Framework Community, accessed December 27, 2025, https://community.frame.work/t/amd-specific-ollama-alternative/73584
  11. support for NPU with openvino · blakeblackshear frigate · Discussion #15697 – GitHub, accessed December 27, 2025, https://github.com/blakeblackshear/frigate/discussions/15697
  12. Ultra 5 125H Drawing 16-17W power even at Idle or low usage – Intel Community, accessed December 27, 2025, https://community.intel.com/t5/Mobile-and-Desktop-Processors/Ultra-5-125H-Drawing-16-17W-power-even-at-Idle-or-low-usage/td-p/1603046
  13. HP G9 power consumption : r/homelab – Reddit, accessed December 27, 2025, https://www.reddit.com/r/homelab/comments/1jdmqul/hp_g9_power_consumption/
  14. Low power server – Proxmox Support Forum, accessed December 27, 2025, https://forum.proxmox.com/threads/low-power-server.134040/
  15. hp flex io | Newegg.com, accessed December 27, 2025, https://www.newegg.com/p/pl?d=hp+flex+io
  16. HP Flex IO V2 10Gbase T Module 56Q71AA for 1L PCs – ServeTheHome, accessed December 27, 2025, https://www.servethehome.com/hp-flex-io-v2-10gbase-t-module-56q71aa-for-1l-pcs-marvell/
  17. HP Elite Mini 800 G9 Upgrade – RAM & NVMe SSD for the Ultimate Homelab Setup!, accessed December 27, 2025, https://www.youtube.com/watch?v=eRJuetcQ-aA
  18. HP Elite MINI 800 G9 – RAM Memory Upgrade : r/homelab – Reddit, accessed December 27, 2025, https://www.reddit.com/r/homelab/comments/15h7uyq/hp_elite_mini_800_g9_ram_memory_upgrade/
  19. Minis Forum MS-01 idle power draw information : r/MiniPCs – Reddit, accessed December 27, 2025, https://www.reddit.com/r/MiniPCs/comments/1d10wex/minis_forum_ms01_idle_power_draw_information/
  20. Minisforum MS-01 Power Use – scottstuff.net, accessed December 27, 2025, https://scottstuff.net/posts/2025/01/08/minisforum-ms01-power/
  21. MS-01 M.2 slots speed : r/MiniPCs – Reddit, accessed December 27, 2025, https://www.reddit.com/r/MiniPCs/comments/1ikm6py/ms01_m2_slots_speed/
  22. Minisforum MS-01 compatibility · Issue #59 · magic-blue-smoke/Dual-Edge-TPU-Adapter, accessed December 27, 2025, https://github.com/magic-blue-smoke/Dual-Edge-TPU-Adapter/issues/59
  23. MinisForum’s 16 Cores, Low Power BD795i SE Motherboard Review : r/sffpc – Reddit, accessed December 27, 2025, https://www.reddit.com/r/sffpc/comments/1j2s5i5/minisforums_16_cores_low_power_bd795i_se/
  24. SER8 – Very high power consumption Idle in Windows desktop – Beelink Forum, accessed December 27, 2025, https://bbs.bee-link.com/d/4095-ser8-very-high-power-consumption-idle-in-windows-desktop
  25. SER8 8745HS High Power Consumption and Wrong CPU Clocks : r/BeelinkOfficial – Reddit, accessed December 27, 2025, https://www.reddit.com/r/BeelinkOfficial/comments/1ksga1s/ser8_8745hs_high_power_consumption_and_wrong_cpu/

Raspberry Pi 5 + google coral VS N100 miniPC + openVINO for Frigate NVR? – Reddit, accessed December 27, 2025, https://www.reddit.com/r/frigate_nvr/comments/1al9uwb/raspberry_pi_5_google_coral_vs_n100_minipc/

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