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Last Updated: 2026-07-22 11:25:16

ModelZoo

Model data in ModelZoo is updated regularly. For model application examples, refer to ai-sdk.

Base Models

  • K1
  • Inference engine version: spacemit-ort-2.0.2+beta1
  • OS: bianbu-3.0
  • Date: 2026-2-9
  • K3
  • Inference engine version: v2.0.3
  • OS: bianbu-4.0rc3
  • Date: 2026-5-26

Benchmark Method

# Enter the spacemit-ort library path
# cd {spacemit_ort_lib}/
export LD_LIBRARY_PATH=./lib/

# Replace ${model_path} with your own model file path and ${num of cores} with the number of CPU cores to use
./bin/onnxruntime_perf_test ${model_path} -e spacemit -r 10 -x 1 -S 1 -s -c 1 -i "SPACEMIT_EP_INTRA_THREAD_NUM|${num of cores}" -I

# Example output:
using SpaceMITExecutionProvider
setting SPACEMIT_EP_INTRA_THREAD_NUM : 4
Setting intra_op_num_threads to 1
Session creation time cost: 0.169475 s
First inference time cost: 109 ms
Total inference time cost: 0.0727021 s
Total inference requests: 10
Average inference time cost total: 7.270205 ms
Total inference run time: 0.0727619 s
Number of inferences per second: 137.435
Avg CPU usage: 62 %
Peak working set size: 91336704 bytes
Avg CPU usage:62
Peak working set size:91336704
Runs:10
Min Latency: 0.00720383 s
Max Latency: 0.00730163 s
P50 Latency: 0.00727787 s
P90 Latency: 0.00730163 s
P95 Latency: 0.00730163 s
P99 Latency: 0.00730163 s
P999 Latency: 0.00730163 s

# "Average inference time cost total" indicates the single-frame inference latency.

ResNet

  • K1
ModelTypeShape1 Core/ms2 Core/ms4 Core/ms
resnet18int8224x22439.7122.4913.71
resnet50int8224x22493.3753.0132.86
resnet50fp16224x224667.55349.34217.27
  • K3
ModelTypeShape1 Core/ms2 Core/ms4 Core/ms8 Core/ms
resnet18int8224x2247.884.742.942.11
resnet50int8224x22419.5411.477.255.22
resnet50.batch4int8224x22473.3740.1923.1915.55
resnet50fp16224x22435.3824.0019.2716.68

MobileNet

  • K1
ModelTypeShape1 Core/ms2 Core/ms4 Core/ms
mobilenet_v1int8224x22432.1016.5610.72
mobilenet_v2int8224x22428.4418.1713.03
mobilenet_v3_smallfp16224x22424.2216.8412.44
mobilenet_v3_largefp16224x22461.6238.9026.61
  • K3
ModelTypeShape1 Core/ms2 Core/ms4 Core/ms8 Core/ms
mobilenet_v1int8224x22412.717.243.952.38
mobilenet_v2int8224x22417.359.805.143.29
mobilenet_v3_smallfp16224x2247.624.713.132.82
mobilenet_v3_largefp16224x22413.688.325.284.14

EfficientNet

  • K1
ModelTypeShape1 Core/ms2 Core/ms4 Core/ms
efficientnet_v1_b0int8224x22468.8140.6526.30
efficientnet_v1_b1int8224x22497.2457.2137.28
efficientnet_v2_sint8224x224144.8183.1152.66
efficientnet_v1_b0fp16224x224121.7071.8746.47
efficientnet_v1_b1fp16224x224172.87102.1065.98
efficientnet_v2_sfp16224x224563.58305.40176.87
  • K3
ModelTypeShape1 Core/ms2 Core/ms4 Core/ms8 Core/ms
efficientnet_v1_b0int8224x22433.3218.6610.367.93
efficientnet_v1_b1int8224x22452.3228.7916.1212.07
efficientnet_v2_sint8224x22443.0624.6415.1910.65
efficientnet_v1_b0fp16224x22434.1619.7012.829.86
efficientnet_v1_b1fp16224x22450.2529.4018.9414.44
efficientnet_v2_sfp16224x22455.0232.4820.8514.25

ViT

  • K1
ModelTypeShape1 Core/ms2 Core/ms4 Core/ms
vit_b_16int8224x224527.78356.00200.91
vit_b_16fp16224x2242557.031425.90774.00
  • K3
ModelTypeShape1 Core/ms2 Core/ms4 Core/ms8 Core/ms
vit_b_16int8224x224104.2558.9337.3925.01
vit_b_16fp16224x224206.15122.1782.5662.04

YOLOv5

  • K1
ModelTypeShape1 Core/ms2 Core/ms4 Core/ms
yolov5nint8640x640233.24149.24111.18
yolov5sint8640x640450.00238.84140.92
yolov5mint8640x640996.12483.86269.41
  • K3
ModelTypeShape1 Core/ms2 Core/ms4 Core/ms8 Core/ms
yolov5nint8640x64044.7224.5614.519.80
yolov5sint8640x64074.3840.7724.2715.96
yolov5mint8640x640153.5882.7346.5329.65

YOLOv6

  • K1
ModelTypeShape1 Core/ms2 Core/ms4 Core/ms
yolov6nint8640x640177.65100.0462.43
yolov6sint8640x640462.12237.01132.61
  • K3
ModelTypeShape1 Core/ms2 Core/ms4 Core/ms8 Core/ms
yolov6nint8640x64032.9318.5911.117.72
yolov6sint8640x64066.3636.6121.5613.60

YOLOv8

  • K1
ModelTypeShape1 Core/ms2 Core/ms4 Core/ms
yolov8nint8640x640211.49118.8876.18
yolov8sint8640x640463.19240.62142.38
yolov8mint8640x640994.91510.06284.39
  • K3
ModelTypeShape1 Core/ms2 Core/ms4 Core/ms8 Core/ms
yolov8nint8640x64043.0523.9114.239.82
yolov8sint8640x64076.9642.4125.5217.14
yolov8mint8640x640163.6288.0849.6732.66

YOLOv8-Seg

  • K3
ModelTypeShape1 Core/ms2 Core/ms4 Core/ms8 Core/ms
yolov8n-segint8640x64068.7037.3421.4413.97
yolov8s-segint8640x640111.8660.7435.6723.22
yolov8m-segint8640x640216.20115.7764.7841.56

YOLOv8-Pose

  • K3
ModelTypeShape1 Core/ms2 Core/ms4 Core/ms8 Core/ms
yolov8n-poseint8640x64047.1426.7316.4611.44
yolov8s-poseint8640x64083.1946.3428.3119.15
yolov8m-poseint8640x640170.4592.6652.6234.96

YOLOv12

  • K1
ModelTypeShape1 Core/ms2 Core/ms4 Core/ms
yolo12nint8640x640405.57238.88161.90
yolo12sint8640x640912.32533.02312.74
yolo12mint8640x6402050.741096.84661.23
  • K3
ModelTypeShape1 Core/ms2 Core/ms4 Core/ms8 Core/ms
yolo12nint8640x640119.6464.8838.0827.60
yolo12sint8640x640218.19117.3768.7148.16
yolo12mint8640x640428.03228.18130.6289.44

Audio Models

  • K1
ModelType4 Core/rtf
melottsdyn_int80.984
sensevoicedyn_int8---
  • K3
ModelType4 Core/rtf8 Core/rtf
melottsdyn_int80.530---
sensevoicedyn_int80.11240.1380

Large Language Models

  • K3

    • llama.cpp version: 0.1.1
    • OS: bianbu-4.0rc3
    • Date: 2026-5-26

LLM Benchmark Method

# Enter the spacemit-llama.cpp library path
# cd {spacemit-llama.cpp}/
export LD_LIBRARY_PATH=./lib/

# Replace ${model_path} with your own model file path and ${num of cores} with the number of CPU cores to use
./bin/llama-bench -m ${model_path} -t ${num of cores} -p 128 -n 128 -mmp 0 -fa 1 -ub 128

# Example output:
CPU_RISCV64_SPACEMIT: tcm is available, blk_size: 393216, blk_num: 8, is_fake_tcm: 0
CPU_RISCV64_SPACEMIT: num_cores: 16, num_perfer_cores: 8, perfer_core_arch_id: a064, exclude_main_thread: 0, use_ime1: 0, use_ime2: 1, mem_backend: HPAGE, cpu_mask: ff00, aicpu_id_offset: 8
CPU_RISCV64_SPACEMIT: alloc_chunk: open(/dev/tcm_sync_mem) failed, errno=2
CPU_RISCV64_SPACEMIT: failed to allocate init_barrier from shared mem, falling back to heap
| model                          |       size |     params | backend    | threads | n_ubatch | fa | mmap |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | ---: | --------------: | -------------------: |
| qwen3 0.6B Q4_0                | 358.78 MiB |   596.05 M | CPU        |       8 |      128 |  1 |    0 |           pp128 |        499.75 ± 0.22 |
| qwen3 0.6B Q4_0                | 358.78 MiB |   596.05 M | CPU        |       8 |      128 |  1 |    0 |           tg128 |         53.35 ± 0.03 |

Qwen

  • K3
ModelQuantizationPP128 (token/s)TG128 (token/s)PP1280 (token/s)TG1280 (token/s)
qwen3-0.6BQ4_0499.7553.35--
qwen3-1.7BQ4_0229.7923.11--
qwen3-4BQ4_076.4411.03--
qwen3-moe-30B-A3BQ4_055.6712.3244.0311.17
qwen3.5-0.8BQ4_0182.6929.33--
qwen3.5-2BQ4_1112.2216.15--

HunYuan

  • K3
ModelQuantizationPP128 (token/s)TG128 (token/s)PP1280 (token/s)TG1280 (token/s)
HY-MT1.5-1.8BQ4_K_M157.8120.15--

Llama

  • K3
ModelQuantizationPP128 (token/s)TG128 (token/s)PP1280 (token/s)TG1280 (token/s)
llama2-7BQ4_050.407.07--

Multimodal Large Models

  • K3

    • llama.cpp version: 0.1.1
    • Inference engine version: v2.0.3
    • OS: bianbu-4.0rc3
    • Date: 2026-5-26

Multimodal Benchmark Method

Using qwen3vlencoder as an example:

export LD_LIBRARY_PATH=./spacemit-llama.cpp/lib:./spacemit_ort/lib
export SPACEMIT_EP_DENSE_ACCURACY_LEVEL=1

llama-server -m qwen3vl-30b-text-q4_1.gguf --media-backend smt --smt-config-dir ./ -ctk f16 -ctv f16 -t 8 -c 1024 --host 0.0.0.0 --port 8080 --reasoning-budget 0 --reasoning off

For detailed parameter descriptions, see llama.cpp.

VLM

  • K3
ModelImage SizeLLM 8 Core + VisionEncoder 4 Core/msLLM 8 Core + VisionEncoder 8 Core/ms
fastvlm-0.5B512*512256.47164.50
Qwen3-VL-30B-A3B768*7687928.134753.55
Qwen3.5-0.8B384*384340.42245.61
Qwen3.5-2B384*384901.56794.03
Qwen3.5-4B384*384904.73798.71

ASR

  • K3
ModelLLM 8 Core + AudioEncoder 4 Core/rtf
qwen3-ASR-0.6B0.186

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