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
| Model | Type | Shape | 1 Core/ms | 2 Core/ms | 4 Core/ms |
|---|---|---|---|---|---|
| resnet18 | int8 | 224x224 | 39.71 | 22.49 | 13.71 |
| resnet50 | int8 | 224x224 | 93.37 | 53.01 | 32.86 |
| resnet50 | fp16 | 224x224 | 667.55 | 349.34 | 217.27 |
- K3
| Model | Type | Shape | 1 Core/ms | 2 Core/ms | 4 Core/ms | 8 Core/ms |
|---|---|---|---|---|---|---|
| resnet18 | int8 | 224x224 | 7.88 | 4.74 | 2.94 | 2.11 |
| resnet50 | int8 | 224x224 | 19.54 | 11.47 | 7.25 | 5.22 |
| resnet50.batch4 | int8 | 224x224 | 73.37 | 40.19 | 23.19 | 15.55 |
| resnet50 | fp16 | 224x224 | 35.38 | 24.00 | 19.27 | 16.68 |
MobileNet
- K1
| Model | Type | Shape | 1 Core/ms | 2 Core/ms | 4 Core/ms |
|---|---|---|---|---|---|
| mobilenet_v1 | int8 | 224x224 | 32.10 | 16.56 | 10.72 |
| mobilenet_v2 | int8 | 224x224 | 28.44 | 18.17 | 13.03 |
| mobilenet_v3_small | fp16 | 224x224 | 24.22 | 16.84 | 12.44 |
| mobilenet_v3_large | fp16 | 224x224 | 61.62 | 38.90 | 26.61 |
- K3
| Model | Type | Shape | 1 Core/ms | 2 Core/ms | 4 Core/ms | 8 Core/ms |
|---|---|---|---|---|---|---|
| mobilenet_v1 | int8 | 224x224 | 12.71 | 7.24 | 3.95 | 2.38 |
| mobilenet_v2 | int8 | 224x224 | 17.35 | 9.80 | 5.14 | 3.29 |
| mobilenet_v3_small | fp16 | 224x224 | 7.62 | 4.71 | 3.13 | 2.82 |
| mobilenet_v3_large | fp16 | 224x224 | 13.68 | 8.32 | 5.28 | 4.14 |
EfficientNet
- K1
| Model | Type | Shape | 1 Core/ms | 2 Core/ms | 4 Core/ms |
|---|---|---|---|---|---|
| efficientnet_v1_b0 | int8 | 224x224 | 68.81 | 40.65 | 26.30 |
| efficientnet_v1_b1 | int8 | 224x224 | 97.24 | 57.21 | 37.28 |
| efficientnet_v2_s | int8 | 224x224 | 144.81 | 83.11 | 52.66 |
| efficientnet_v1_b0 | fp16 | 224x224 | 121.70 | 71.87 | 46.47 |
| efficientnet_v1_b1 | fp16 | 224x224 | 172.87 | 102.10 | 65.98 |
| efficientnet_v2_s | fp16 | 224x224 | 563.58 | 305.40 | 176.87 |
- K3
| Model | Type | Shape | 1 Core/ms | 2 Core/ms | 4 Core/ms | 8 Core/ms |
|---|---|---|---|---|---|---|
| efficientnet_v1_b0 | int8 | 224x224 | 33.32 | 18.66 | 10.36 | 7.93 |
| efficientnet_v1_b1 | int8 | 224x224 | 52.32 | 28.79 | 16.12 | 12.07 |
| efficientnet_v2_s | int8 | 224x224 | 43.06 | 24.64 | 15.19 | 10.65 |
| efficientnet_v1_b0 | fp16 | 224x224 | 34.16 | 19.70 | 12.82 | 9.86 |
| efficientnet_v1_b1 | fp16 | 224x224 | 50.25 | 29.40 | 18.94 | 14.44 |
| efficientnet_v2_s | fp16 | 224x224 | 55.02 | 32.48 | 20.85 | 14.25 |
ViT
- K1
| Model | Type | Shape | 1 Core/ms | 2 Core/ms | 4 Core/ms |
|---|---|---|---|---|---|
| vit_b_16 | int8 | 224x224 | 527.78 | 356.00 | 200.91 |
| vit_b_16 | fp16 | 224x224 | 2557.03 | 1425.90 | 774.00 |
- K3
| Model | Type | Shape | 1 Core/ms | 2 Core/ms | 4 Core/ms | 8 Core/ms |
|---|---|---|---|---|---|---|
| vit_b_16 | int8 | 224x224 | 104.25 | 58.93 | 37.39 | 25.01 |
| vit_b_16 | fp16 | 224x224 | 206.15 | 122.17 | 82.56 | 62.04 |
YOLOv5
- K1
| Model | Type | Shape | 1 Core/ms | 2 Core/ms | 4 Core/ms |
|---|---|---|---|---|---|
| yolov5n | int8 | 640x640 | 233.24 | 149.24 | 111.18 |
| yolov5s | int8 | 640x640 | 450.00 | 238.84 | 140.92 |
| yolov5m | int8 | 640x640 | 996.12 | 483.86 | 269.41 |
- K3
| Model | Type | Shape | 1 Core/ms | 2 Core/ms | 4 Core/ms | 8 Core/ms |
|---|---|---|---|---|---|---|
| yolov5n | int8 | 640x640 | 44.72 | 24.56 | 14.51 | 9.80 |
| yolov5s | int8 | 640x640 | 74.38 | 40.77 | 24.27 | 15.96 |
| yolov5m | int8 | 640x640 | 153.58 | 82.73 | 46.53 | 29.65 |
YOLOv6
- K1
| Model | Type | Shape | 1 Core/ms | 2 Core/ms | 4 Core/ms |
|---|---|---|---|---|---|
| yolov6n | int8 | 640x640 | 177.65 | 100.04 | 62.43 |
| yolov6s | int8 | 640x640 | 462.12 | 237.01 | 132.61 |
- K3
| Model | Type | Shape | 1 Core/ms | 2 Core/ms | 4 Core/ms | 8 Core/ms |
|---|---|---|---|---|---|---|
| yolov6n | int8 | 640x640 | 32.93 | 18.59 | 11.11 | 7.72 |
| yolov6s | int8 | 640x640 | 66.36 | 36.61 | 21.56 | 13.60 |
YOLOv8
- K1
| Model | Type | Shape | 1 Core/ms | 2 Core/ms | 4 Core/ms |
|---|---|---|---|---|---|
| yolov8n | int8 | 640x640 | 211.49 | 118.88 | 76.18 |
| yolov8s | int8 | 640x640 | 463.19 | 240.62 | 142.38 |
| yolov8m | int8 | 640x640 | 994.91 | 510.06 | 284.39 |
- K3
| Model | Type | Shape | 1 Core/ms | 2 Core/ms | 4 Core/ms | 8 Core/ms |
|---|---|---|---|---|---|---|
| yolov8n | int8 | 640x640 | 43.05 | 23.91 | 14.23 | 9.82 |
| yolov8s | int8 | 640x640 | 76.96 | 42.41 | 25.52 | 17.14 |
| yolov8m | int8 | 640x640 | 163.62 | 88.08 | 49.67 | 32.66 |
YOLOv8-Seg
- K3
| Model | Type | Shape | 1 Core/ms | 2 Core/ms | 4 Core/ms | 8 Core/ms |
|---|---|---|---|---|---|---|
| yolov8n-seg | int8 | 640x640 | 68.70 | 37.34 | 21.44 | 13.97 |
| yolov8s-seg | int8 | 640x640 | 111.86 | 60.74 | 35.67 | 23.22 |
| yolov8m-seg | int8 | 640x640 | 216.20 | 115.77 | 64.78 | 41.56 |
YOLOv8-Pose
- K3
| Model | Type | Shape | 1 Core/ms | 2 Core/ms | 4 Core/ms | 8 Core/ms |
|---|---|---|---|---|---|---|
| yolov8n-pose | int8 | 640x640 | 47.14 | 26.73 | 16.46 | 11.44 |
| yolov8s-pose | int8 | 640x640 | 83.19 | 46.34 | 28.31 | 19.15 |
| yolov8m-pose | int8 | 640x640 | 170.45 | 92.66 | 52.62 | 34.96 |
YOLOv12
- K1
| Model | Type | Shape | 1 Core/ms | 2 Core/ms | 4 Core/ms |
|---|---|---|---|---|---|
| yolo12n | int8 | 640x640 | 405.57 | 238.88 | 161.90 |
| yolo12s | int8 | 640x640 | 912.32 | 533.02 | 312.74 |
| yolo12m | int8 | 640x640 | 2050.74 | 1096.84 | 661.23 |
- K3
| Model | Type | Shape | 1 Core/ms | 2 Core/ms | 4 Core/ms | 8 Core/ms |
|---|---|---|---|---|---|---|
| yolo12n | int8 | 640x640 | 119.64 | 64.88 | 38.08 | 27.60 |
| yolo12s | int8 | 640x640 | 218.19 | 117.37 | 68.71 | 48.16 |
| yolo12m | int8 | 640x640 | 428.03 | 228.18 | 130.62 | 89.44 |
Audio Models
- K1
| Model | Type | 4 Core/rtf |
|---|---|---|
| melotts | dyn_int8 | 0.984 |
| sensevoice | dyn_int8 | --- |
- K3
| Model | Type | 4 Core/rtf | 8 Core/rtf |
|---|---|---|---|
| melotts | dyn_int8 | 0.530 | --- |
| sensevoice | dyn_int8 | 0.1124 | 0.1380 |
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
| Model | Quantization | PP128 (token/s) | TG128 (token/s) | PP1280 (token/s) | TG1280 (token/s) |
|---|---|---|---|---|---|
| qwen3-0.6B | Q4_0 | 499.75 | 53.35 | - | - |
| qwen3-1.7B | Q4_0 | 229.79 | 23.11 | - | - |
| qwen3-4B | Q4_0 | 76.44 | 11.03 | - | - |
| qwen3-moe-30B-A3B | Q4_0 | 55.67 | 12.32 | 44.03 | 11.17 |
| qwen3.5-0.8B | Q4_0 | 182.69 | 29.33 | - | - |
| qwen3.5-2B | Q4_1 | 112.22 | 16.15 | - | - |
HunYuan
- K3
| Model | Quantization | PP128 (token/s) | TG128 (token/s) | PP1280 (token/s) | TG1280 (token/s) |
|---|---|---|---|---|---|
| HY-MT1.5-1.8B | Q4_K_M | 157.81 | 20.15 | - | - |
Llama
- K3
| Model | Quantization | PP128 (token/s) | TG128 (token/s) | PP1280 (token/s) | TG1280 (token/s) |
|---|---|---|---|---|---|
| llama2-7B | Q4_0 | 50.40 | 7.07 | - | - |
Multimodal Large Models
Multimodal Benchmark Method
Using
qwen3vlencoderas 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 offFor detailed parameter descriptions, see llama.cpp.
VLM
- K3
| Model | Image Size | LLM 8 Core + VisionEncoder 4 Core/ms | LLM 8 Core + VisionEncoder 8 Core/ms |
|---|---|---|---|
| fastvlm-0.5B | 512*512 | 256.47 | 164.50 |
| Qwen3-VL-30B-A3B | 768*768 | 7928.13 | 4753.55 |
| Qwen3.5-0.8B | 384*384 | 340.42 | 245.61 |
| Qwen3.5-2B | 384*384 | 901.56 | 794.03 |
| Qwen3.5-4B | 384*384 | 904.73 | 798.71 |
ASR
- K3
| Model | LLM 8 Core + AudioEncoder 4 Core/rtf |
|---|---|
| qwen3-ASR-0.6B | 0.186 |

