Orange Pi 5 Plus

Hardware · Rockchip

What AI models can the Rockchip RK3588 / RK3588S (8 GB) run?

RAM
8.0 GB
Usable for models
6.4 GB
Memory bandwidth
34 GB/s
Runs models on
CPU cores
Released
2022

Models run on the CPU cores here, so the memory’s speed and how fast those cores can read it set the pace: small models (about 1-4B) are usable, bigger ones are slow. We plan around 6.4 GB of the 8.0 GB (what Linux leaves free for a model). 6 TOPS NPU (three cores). llama.cpp and Ollama use the CPU. Rockchip’s RKLLM toolkit (and front ends such as RKLLama) runs converted 8-bit models on the NPU; generation is still limited by memory speed, so it isn’t much faster than the CPU, but it leaves the CPU free.

More about this hardware

MakerRockchip
TypeSingle-board computer or edge AI board
Share of memory for models80% (what Linux typically leaves free on a board this size)
ChipRockchip RK3588
AI accelerator6 TOPS NPU (three cores). llama.cpp and Ollama use the CPU. Rockchip’s RKLLM toolkit (and front ends such as RKLLama) runs converted 8-bit models on the NPU; generation is still limited by memory speed, so it isn’t much faster than the CPU, but it leaves the CPU free.
Boards with this chipOrange Pi 5 / 5 Plus / 5 Max / 5 Ultra · Radxa ROCK 5B / 5B+ / 5 ITX · FriendlyElec NanoPC-T6 · Khadas Edge2 · Turing RK1 · Vicharak Axon · KICKPI K8C · Seeed reComputer RK3588
Memory typeLPDDR4X-4224, 64-bit (boards with LPDDR5 reach about 44 GB/s)
Bandwidth figure33.8 GB/s, the chip’s rated memory speed
Note4× Cortex-A76 at 2.4 GHz plus 4× Cortex-A55, Mali-G610 GPU. Speeds assume llama.cpp on the CPU: Turing Pi measured 11.25 tokens/s for Llama 3.2 3B at Q4_K_M on an RK3588, about 60% of the memory’s rated speed. The RK3588S is the same chip with fewer I/O lanes. Use a fan or a large heatsink.
Specificationswww.orangepi.org

Apps that run models well on the Rockchip RK3588 / RK3588S (8 GB)

For a typical model that fits in memory. Each model page picks the best app for that model on this hardware. All apps

3 products with the Rockchip RK3588 / RK3588S (8 GB)

Best LLMs for the Rockchip RK3588 / RK3588S (8 GB)

Quality Model Quant Memory Speed Runs
57Good quality (B) · 57 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. Ling 3.0 Tiny (8B-A1.3B)Ant Group (inclusionAI) Q4_K_M 6.2 GB 24.4 tok/s Runs welltight fit
58Good quality (B) · 58 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. Gemma 4 E2BGoogle Q4_K_M 4.3 GB 15.0 tok/s Runs well
65Good quality (B) · 65 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. MiMo V2.6 Distill Qwen 9BXiaomi Q3_K_M 6.0 GB 4.8 tok/s Slowtight fit
65Good quality (B) · 65 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. SenseNova Loop 9B PreviewSenseTime Q3_K_M 6.3 GB 4.6 tok/s Slowtight fit
42Fair quality (C) · 42 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. Unlimited OCR 2 (3B-A0.6B)Baidu Q5_K_M 4.0 GB 40.8 tok/s Runs great
57Good quality (B) · 57 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. Nanbeige4.2 3BBOSS Zhipin (Nanbeige) Q3_K_M 4.6 GB 10.7 tok/s Runs well
64Good quality (B) · 64 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. Ornith 1.5 9BDeepReinforce Q3_K_M 6.1 GB 4.6 tok/s Slowtight fit
63Good quality (B) · 63 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. Qwen3.5 9BAlibaba Q3_K_M 6.1 GB 4.6 tok/s Slowtight fit
63Good quality (B) · 63 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. Ornith 1.0 9BDeepReinforce Q3_K_M 6.1 GB 4.6 tok/s Slowtight fit
62Good quality (B) · 62 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. TMax 9BAi2 Q3_K_M 5.8 GB 5.0 tok/s Slowtight fit
49Fair quality (C) · 49 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. LFM2.5 8B-A1BLiquid AI Q4_K_M 6.4 GB 21.4 tok/s Runs welltight fit
41Fair quality (C) · 41 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. Granite 4.0 H Tiny (7B-A1B)IBM Q4_K_M 5.4 GB 31.0 tok/s Runs great

Best Image models for the Rockchip RK3588 / RK3588S (8 GB)

Quality Model Quant Memory Speed Runs
84Great quality (A) · 84 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. FLUX 3 Image KleinBlack Forest Labs Q4 GGUF 5.7 GB 17.5 min Very slowtight fit · encoder unloaded after use
76Great quality (A) · 76 of 100: how capable the model is at FP8, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. FLUX.2 [klein] 4BBlack Forest Labs FP8 6.2 GB 2.3 min Very slowtight fit · encoder unloaded after use
78Great quality (A) · 78 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. Z-Image TurboAlibaba (Tongyi) Q4 GGUF 6.1 GB 7.7 min Very slowtight fit · encoder unloaded after use
76Great quality (A) · 76 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. MOSS-Image SFTOpenMOSS Q4 GGUF 5.4 GB 43.1 min Very slowencoder unloaded after use
39Basic quality (D) · 39 of 100: how capable the model is at FP8, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. SDXL TurboStability AI FP8 4.2 GB 20 s Runs well
76Great quality (A) · 76 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. Z-ImageAlibaba (Tongyi) Q4 GGUF 6.1 GB 52.3 min Very slowtight fit · encoder unloaded after use

Best Video models for the Rockchip RK3588 / RK3588S (8 GB)

Quality Model Quant Memory Speed Runs
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. AnimateDiff v3 (SD 1.5)Shanghai AI Lab FP16 4.7 GB 48.9 min Very slow
49Fair quality (C) · 49 of 100: how capable the model is at FP8, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. LTX-Video 2BLightricks FP8 6.1 GB 57.7 min Very slowtight fit · encoder unloaded after use
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. CogVideoX 5BZhipu AI FP8 5.9 GB 8.6 h Very slowtight fit · CPU offload
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. CogVideoX 5B I2VZhipu AI FP8 6.1 GB 8.6 h Very slowtight fit · CPU offload
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. Wan 2.1 T2V 1.3BAlibaba FP8 6.0 GB 11.4 h Very slowtight fit · CPU offload

Best Decision models for the Rockchip RK3588 / RK3588S (8 GB)

Quality Model Quant Memory Speed Runs
78Great quality (A) · 78 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. OpenAI Privacy FilterOpenAI Q8_0 2.4 GB 120 ms Runs great
86Excellent quality (A+) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. Jina Reranker v3.5Jina AI Q8_0 2.0 GB 2.6 s Very slow
89Excellent quality (A+) · 89 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. Qwen3 Reranker 4BAlibaba Q6_K 4.7 GB 17.3 s Very slow
84Great quality (A) · 84 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. Jina Reranker v3Jina AI Q8_0 2.0 GB 2.6 s Very slow
80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. GLiNER2.5 DecideFastino Q8_0 1.2 GB 1.1 s Slow
87Excellent quality (A+) · 87 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. zerank-2ZeroEntropy Q6_K 4.7 GB 17.3 s Very slow
86Excellent quality (A+) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. Shieldstral 1.0 3BMistral AI Q8_0 5.4 GB 16.6 s Very slow
80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. FRIDA-DecisionsSber AI Q8_0 1.7 GB 1.9 s Very slow

Best LLMs by task on this hardware Open in Model Finder

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

Was this page helpful?