Image-Text-to-Text
Transformers
Safetensors
kimi_k3
feature-extraction
vLLM
cubic-quantization
W2A8
W3A8
W4A8
W2A16
W3A16
W4A16
multimodal
custom_code
8-bit precision
Instructions to use QuantTrio/Kimi-K3-Cubic-2.5Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantTrio/Kimi-K3-Cubic-2.5Bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="QuantTrio/Kimi-K3-Cubic-2.5Bit", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantTrio/Kimi-K3-Cubic-2.5Bit", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use QuantTrio/Kimi-K3-Cubic-2.5Bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantTrio/Kimi-K3-Cubic-2.5Bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantTrio/Kimi-K3-Cubic-2.5Bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/QuantTrio/Kimi-K3-Cubic-2.5Bit
- SGLang
How to use QuantTrio/Kimi-K3-Cubic-2.5Bit with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "QuantTrio/Kimi-K3-Cubic-2.5Bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantTrio/Kimi-K3-Cubic-2.5Bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "QuantTrio/Kimi-K3-Cubic-2.5Bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantTrio/Kimi-K3-Cubic-2.5Bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use QuantTrio/Kimi-K3-Cubic-2.5Bit with Docker Model Runner:
docker model run hf.co/QuantTrio/Kimi-K3-Cubic-2.5Bit
Update README.md
Browse files
README.md
CHANGED
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@@ -151,9 +151,108 @@ vllm serve \
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--disable-uvicorn-access-log
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```
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Observed performance for this command on 8 × NVIDIA H200 141GB
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(2026-08-05) is:
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| Item | Observed value |
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|------|---------------:|
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| Dynamic Cubic mode | A8 |
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## 8. Contact Us
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If you have any questions, please reach out at [support@moonshot.ai](mailto:support@moonshot.ai).
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--disable-uvicorn-access-log
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```
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Observed performance for this command on 8 × NVIDIA H200 141GB and 8 × NVIDIA H20 141GB
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### 【docker with vLLM Startup Command】
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```bash
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docker pull swr.cn-north-4.myhuaweicloud.com/desaysv/vllm-cubic:0.26.1-cubic.20260805
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export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
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export VLLM_CUBIC_DYNAMIC_A8=1
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export FLASHINFER_DISABLE_VERSION_CHECK=1
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vllm serve \
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__YOUR_PATH__/Kimi-K3-Cubic-2.5Bit \
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--served-model-name Kimi-K3-Cubic-2.5Bit \
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--trust-remote-code \
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--quantization cubic \
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--kv-cache-dtype fp8_q16 \
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--gpu-memory-utilization 0.985 \
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--tensor-parallel-size 8 \
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--enable-expert-parallel \
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--mm-encoder-tp-mode data \
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--max-model-len auto \
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--max-num-seqs 128 \
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--max-num-batched-tokens 2048 \
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--enable-prefix-caching \
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--enable-auto-tool-choice \
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--tool-call-parser kimi_k3 \
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--reasoning-parser kimi_k3 \
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--host 0.0.0.0 \
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--port 8000 \
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--disable-uvicorn-access-log
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```
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*skip deepgemm error ,its not effect start
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### 【benchmark】10*8K/1K
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| Item | Observed value |
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|------|---------------:|
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| Total requests | 10 |
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| Successful requests | 10 |
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| Failed requests | 0 |
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| Unfinished requests | 0 |
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| Average concurrency | 10 |
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| Total duration | 209.75 s |
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| Total throughput | 464.11 tokens/s |
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| Input throughput | 684.34 tokens/s |
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| Output throughput | 51.07 tokens/s |
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| Average request latency | 204.23 s |
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| Average TTFT | 69,607.26 ms |
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| Average TPOT | 164.76 ms |
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| Average ITL | 95.25 ms |
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| TTFT (P50) | 63,297.56 ms |
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| TTFT (P90) | 128,423.96 ms |
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| TTFT (P99) | 128,423.96 ms |
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| ITL (P50) | 81.85 ms |
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| ITL (P90) | 178.56 ms |
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| ITL (P99) | 178.56 ms |
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| TPOT (P50) | 154.40 ms |
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| TPOT (P90) | 206.35 ms |
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| TPOT (P99) | 206.35 ms |
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| Latency (P50) | 204.10 s |
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| Latency (P90) | 208.45 s |
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| Latency (P99) | 208.45 s |
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| Input tokens per request | 8,088 |
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| Output tokens per request | 1,000 |
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### 【benchmark】10*64K/3000
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| Item | Observed value |
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|------|---------------:|
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| Total requests | 10 |
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| Successful requests | 10 |
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| Failed requests | 0 |
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| Unfinished requests | 0 |
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| Average concurrency | 9 |
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| Total duration | 1,461.29 s |
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| Total throughput | 519.15 tokens/s |
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| Input throughput | 513.63 tokens/s |
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| Output throughput | 23.21 tokens/s |
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| Average request latency | 1,387.96 s |
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| Average TTFT | 882,028.37 ms |
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| Average TPOT | 334.97 ms |
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| Average ITL | 107.57 ms |
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| TTFT (P50) | 604,938.80 ms |
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| TTFT (P90) | 1,352,678.48 ms |
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| TTFT (P99) | 1,352,678.48 ms |
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| ITL (P50) | 61.83 ms |
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| ITL (P90) | 313.74 ms |
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| ITL (P99) | 313.74 ms |
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| TPOT (P50) | 378.19 ms |
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| TPOT (P90) | 465.73 ms |
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| TPOT (P99) | 465.73 ms |
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| Latency (P50) | 1,394.02 s |
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| Latency (P90) | 1,460.10 s |
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| Latency (P99) | 1,460.10 s |
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| Input tokens per request | 64,088 |
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| Output tokens per request | 3,000 |
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| Item | Observed value |
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|------|---------------:|
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| Dynamic Cubic mode | A8 |
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## 8. Contact Us
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+
If you have any questions, please reach out at [support@moonshot.ai](mailto:support@moonshot.ai).
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