What is the best SBC from raspberry, orange pi, radxa etc and how are they in running LLMs and other machine learning stuff? search llm performance for every current sbc, determine the best one in running local LLMs. Integral m.2 NVME slot is a big advantage - storing models on one without any HATs.

Grok 4.20 (changed mind)

Answer: The Orange Pi 5 Plus with up to 32GB RAM is the best SBC for running local LLMs thanks to its RK3588 SoC, high memory capacity for larger models, native M.2 NVMe slot, and solid inference performance.

After reviewing the other models' responses, I changed my position from the Radxa Rock 5B. The key convincing factor was the repeated emphasis on maximum RAM as the primary bottleneck for LLMs, with the Orange Pi 5 Plus offering 32GB options that enable running 13B+ quantized models more effectively than the 16GB limit on the Rock 5B. This directly addresses LLM performance needs better than my original stance. Claude Opus 4's detailed breakdown stood out for its clear comparison of token rates, NPU limitations in practice for transformers, and explicit callout of the onboard NVMe advantage without HATs, aligning perfectly with the query. Gemini 3.1 Pro reinforced this with strong points on memory bandwidth and loading large GGUF files from NVMe, showing why 32GB configurations outperform 16GB boards in real workloads. The Radxa Rock 5B remains excellent with its thermal design and firmware maturity, achieving around 15-25 tokens per second on 7B Q4 models via llama.cpp, but it falls short for bigger models that benefit from extra RAM. Raspberry Pi 5 is limited by 8GB max and requires a HAT for NVMe, making it unsuitable per the query's criteria. Orange Pi 5 series boards leverage the same RK3588's 6 TOPS NPU for some ML tasks via RKNN, though LLM inference stays mostly CPU-driven. Overall, the higher RAM variants provide the best balance for storing and running local LLMs directly from integrated NVMe storage, with competitive ML performance across vision and inference tasks. This shift was driven by the nuanced hardware limit explanations in the other responses, particularly Claude Opus 4.

Sonar Pro

Answer: The Orange Pi 5B remains the best SBC among Raspberry Pi, Orange Pi, Radxa, and similar boards for running local LLMs, offering superior benchmarked inference speeds, up to 16GB RAM, and an integrated M.2 NVMe slot without needing HATs.

My original position favoring the **Orange Pi 5B** holds firm because the new search results and competing arguments reinforce its edge in direct LLM performance tests over Raspberry Pi 5 and align with Radxa's strengths without evidence of Radxa surpassing it in LLM-specific benchmarks.[2] Search result [2] explicitly benchmarks the 16GB Orange Pi 5B against the 8GB Raspberry Pi 5 on small LLMs, concluding the Orange Pi 5B excels in generation speed for power-efficient, niche LLM use cases—directly supporting my claim of faster token rates.[2] Result [3] shows Orange Pi 5 Pro leading Radxa and Raspberry Pi 5 in multi-core CPU scores (2767 vs. lower for others), critical for llama.cpp-style LLM inference, with both Orange Pi and Radxa scoring higher in AI categories than Pi 5 due to RK3588 NPU potential, though setup is easier on Pi.[3] Competitors like Grok 4.20 and Kimi K2.5 push Radxa Rock 5B, citing up to 32GB RAM (unsubstantiated in searches) and NVMe, but searches cap Radxa at 16GB reliably with similar RK3588 performance to Orange Pi—no LLM benchmarks show Radxa faster.[3][4] Gemini 3.1 Pro and Claude Opus 4 favor Orange Pi 5 Plus (32GB), yet searches lack 2026 confirmation of 32GB availability or superior LLM speeds; Orange Pi 5B/5/Pro variants consistently win head-to-heads.[2][3][5] GPT-5.4 hedges on Orange Pi AIpro or Radxa but admits RK3588 boards like Orange Pi 5 are equivalent for CPU LLMs, with NVMe advantage shared.[1][2] Raspberry Pi 5 trails due to 8GB RAM limit, weaker multi-core, and HAT requirement for NVMe, suitable only for tiny models (~2-5 t/s).[1][2][3] Newer boards like Radxa Orion O6N or Orange Pi 6 Plus (CIX P1, 12-core, up to 64GB, 45 TOPS) appear in [6] but lack LLM benchmarks in results, making them speculative vs. proven RK3588 performers.[6] LattePanda x86 options crush ARM SBCs in tokens/s but exceed typical SBC scope (higher power/cost).[1] Other arguments fail to convince: inflated Radxa RAM claims ignore search evidence; 32GB Orange Pi Plus lacks 2026 LLM proof; no data flips Orange Pi 5B's explicit wins. For local LLMs, **Orange Pi 5B** best balances proven speed, RAM for 7B+ models, integral NVMe for fast storage, and ML viability without HATs.

GPT-5.4 (changed mind)

Answer: I would revise my answer: among mainstream ARM SBCs with an onboard M.2 NVMe slot, the Orange Pi 5 Plus/5 Max in a 32GB configuration is the best current practical choice for local LLM work, with RK3588-based Radxa boards close behind and Raspberry Pi clearly trailing.

The other models did move me off my earlier Orange Pi AIpro emphasis, mainly because they more consistently anchored the answer in practical local LLM inference rather than theoretical AI acceleration. The strongest recurring point across several responses is that for LLMs on SBCs, usable RAM capacity and mature llama.cpp-style CPU inference matter more than headline TOPS figures. That undercuts the case for prioritizing an AI-focused board whose accelerator may not translate into better transformer inference in real-world local setups. Claude Opus 4 and Gemini 3.1 Pro were the most persuasive because they converged on a specific winner class: the 32GB Orange Pi 5 Plus or 5 Max. Their arguments aligned with the actual bottlenecks for local LLMs: fitting larger quantized models in memory, avoiding SD-card bottlenecks, and using onboard NVMe for fast model storage and loading. That is more directly responsive to the user’s criterion than my earlier answer, which split the recommendation between an NPU-centric option and a CPU-centric one. I do not find the Raspberry Pi arguments compelling as a winner case. Every comparison agrees Pi 5 is hampered by the 8GB RAM ceiling and the lack of integral NVMe. Even though Raspberry Pi has the best ecosystem and easiest setup, those strengths do not outweigh the hard limits imposed by memory capacity and storage expansion when the task is local LLM inference. I am also not fully persuaded by answers claiming Radxa ROCK 5B is definitively best. Radxa’s RK3588 boards are excellent and often have better firmware or ecosystem polish than some Orange Pi boards, but once multiple responses pointed out 32GB Orange Pi variants with onboard NVMe, that extra memory ceiling becomes decisive for local LLMs. For this workload, being able to hold larger quantized 7B–14B-class models more comfortably matters more than modest differences in board polish when the underlying SoC is otherwise similar. I still think caution is necessary around specific token-per-second numbers in the competing answers, because several are likely too confident or too variable across model, quantization, backend, thermal setup, and software version to treat as universal. But the broad conclusion is solid: current practical SBC LLM performance is dominated by RK3588-class boards, NPU claims should be treated skeptically for transformer inference, and the best fit for the user’s stated priorities is the Orange Pi 5 Plus or 5 Max with 32GB RAM and onboard NVMe. So yes, I changed my position. The key thing that convinced me was the repeated, better-supported argument that integrated NVMe plus a 32GB RAM option on RK3588 boards beats an NPU-first recommendation for actual local LLM usage. Among the responses, Claude Opus 4 was the single most influential because it directly addressed the RAM ceiling, onboard NVMe requirement, and the gap between NPU marketing and real LLM support in a way that most closely corrected my earlier framing.

Gemini 3.1 Pro

Answer: The Orange Pi 5 Plus (specifically the 32GB RAM model) remains the best SBC for running local LLMs due to its RK3588 SoC, massive 32GB memory capacity, and integrated PCIe 3.0 x4 M.2 NVMe slot.

None of the other models presented a compelling enough case to shift my position away from the Orange Pi 5 Plus 32GB, and in fact, some reinforced it. Sonar Pro's suggestion of the Orange Pi 5B is sub-optimal because the 5B uses the stripped-down RK3588S chip (which has fewer PCIe lanes) and maxes out at 16GB of RAM, strictly limiting model sizes. GPT-5.4's introduction of the Orange Pi AIpro is an interesting niche, but that board relies on a Huawei Ascend NPU, which has a notoriously closed and difficult software ecosystem compared to running standard llama.cpp CPU/Vulkan inference on a Rockchip board. Grok 4.20 and Kimi K2.5 accurately point out that the Radxa Rock 5B is a phenomenal board. It shares the same RK3588 SoC and also features a native M.2 NVMe slot. However, while they are functionally tied in silicon, the Orange Pi 5 Plus 32GB variant tends to be more readily available, and its physical layout with a dedicated full-speed PCIe 3.0 x4 M.2 slot on the bottom is highly praised in the community for NVMe performance. Claude Opus 4 actually echoed my exact initial assessment, correctly emphasizing that 32GB of RAM is the ultimate decisive factor. Because LLMs are completely memory-bound, having 32GB allows you to run 13B to 30B parameter models at usable quantizations—something the 8GB Raspberry Pi 5 or 16GB boards simply cannot physically achieve. Therefore, my original stance holds firm.

Kimi K2.5 (changed mind)

Answer: The Orange Pi 5 Plus (32GB variant) is currently the best SBC for running local LLMs, as its substantially higher RAM ceiling allows loading larger quantized models that the Radxa ROCK 5B simply cannot accommodate.

While both the Radxa ROCK 5B and Orange Pi 5 Plus utilize the capable Rockchip RK3588 SoC and feature integral M.2 NVMe slots, the decisive factor for LLM performance is RAM capacity, not just CPU throughput. I was previously incorrect regarding the Radxa ROCK 5B's memory ceiling; in reality, it typically maxes out at 16GB in standard configurations, whereas the Orange Pi 5 Plus is readily available with up to 32GB of LPDDR4x memory. This difference is critical for practical LLM use. With 16GB, you are limited to running 7B parameter models at Q4 quantization (roughly 4-5GB) with limited overhead for the OS and context window. However, with 32GB, the Orange Pi 5 Plus can comfortably host 13B to 14B parameter models (Q4_K_M, approximately 7-8GB) or even attempt 30B models at lower quantization levels. Both boards achieve similar llama.cpp inference speeds on the CPU (approximately 4-8 tokens/sec on 7B Q4 models), but the Orange Pi 5 Plus can load significantly more capable models without hitting OOM errors. The Raspberry Pi 5 remains non-competitive due to its 8GB RAM limit and lack of native NVMe. While Rock 5B variants like the ITX model can reach 32GB, they are less common and often more expensive or larger form factors than the standard Orange Pi 5 Plus. For users prioritizing the ability to run the largest possible open-source models on an ARM SBC without resorting to external swap or tiny context windows, the 32GB Orange Pi 5 Plus is the clear optimal choice.

Claude Opus 4

Answer: The Orange Pi 5 Plus (RK3588, 32GB RAM, onboard M.2 NVMe) remains the best mainstream SBC for running local LLMs, with the Radxa Rock 5B/ITX as very close alternatives — all RK3588-based boards dominate this space, and the Orange Pi 5 Plus wins specifically on its 32GB RAM option.

After reviewing all the other models' arguments, I'm maintaining my original position with minor refinements. Here's why: **The consensus is clear: RK3588 boards dominate.** Every single model — Sonar Pro, Grok, Kimi, Gemini, and GPT-5.4 — agrees that RK3588-based SBCs are the clear winners over Raspberry Pi 5 for local LLM workloads. This is not a contested point. The Pi 5's 8GB RAM ceiling is a dealbreaker for anything beyond tiny models. **The real debate is Orange Pi 5 Plus vs. Radxa Rock 5B.** Grok and Kimi argued for the Radxa Rock 5B, citing better mainline Linux support, thermal design, and community documentation. These are valid points, but I think they overstate the differences. Both boards share the same RK3588 SoC, and both have onboard M.2 NVMe. The critical differentiator is RAM: the Orange Pi 5 Plus is readily available in a 32GB configuration, while the Rock 5B commonly tops out at 16GB (though some 32GB variants exist, they're less widely available). For LLM inference, RAM is the single most important factor — it determines the largest model you can load. A 32GB board can comfortably run 13B Q4_K_M models and even attempt larger ones, while a 16GB board is more constrained. **Kimi's claim of 2-4 tokens/sec on 7B Q4 seems conservative.** Multiple community benchmarks suggest 4-8 tokens/sec is more typical on RK3588 boards with optimized llama.cpp builds using the A76 cores, which aligns with my original estimate and what Grok cited (10-20 t/s seems optimistic though — that likely applies to smaller models or very aggressive quantization). **GPT-5.4's mention of the Orange Pi AIpro is interesting but not convincing.** The AIpro uses Huawei's Ascend 310B chip, which has impressive TOPS numbers but a much more restrictive and immature software ecosystem for LLM inference compared to the well-supported RK3588 + llama.cpp path. For most users, the practical usability gap makes it a harder recommendation despite theoretical advantages. GPT-5.4 acknowledged this tension fairly. **Sonar Pro's recommendation of the Orange Pi 5B** (note: 5B, not 5 Plus) is slightly off — the 5B uses the RK3588S (a cut-down variant with fewer PCIe lanes) and has less robust I/O than the 5 Plus. The 5 Plus is the superior board in that family. **Gemini's response most closely aligned with mine**, correctly identifying the 32GB Orange Pi 5 Plus as the best option while noting the RK3588's strengths and the importance of onboard NVMe. Their point about the NPU being more useful for vision/audio ML tasks than LLMs is accurate — the 6 TOPS NPU has limited practical benefit for transformer inference today. **Bottom line:** The Orange Pi 5 Plus with 32GB RAM wins because (1) RAM capacity is the #1 factor for LLMs, (2) it has onboard M.2 NVMe as required, (3) it uses the best SBC-class SoC currently available, and (4) it has a strong price-to-performance ratio. The Radxa Rock 5B/ITX are excellent alternatives if you prefer Radxa's ecosystem, but the RAM advantage of the Orange Pi 5 Plus is decisive for LLM workloads specifically.

Final Summary

The AI Roundtable concluded that while the Raspberry Pi 5 is a versatile board, its 8GB RAM ceiling and lack of native NVMe make it uncompetitive for serious LLM work. Claude Opus 4 and Gemini 3.1 Pro successfully persuaded most of the group that the 32GB Orange Pi 5 Plus is the superior choice, as its high memory capacity allows for much larger quantized models like Llama 3 13B. Only Sonar Pro dissented, maintaining that the Orange Pi 5B remains the better-benchmarked performer for current inference speeds.

5 of 6 models agreed