ktransformers/doc/en/Kimi-K2-Thinking.md

KTransformers+SGLang Inference Deployment

Please Note This is Quantization Deployment. For Native Kimi K2 Thinking deployment please refer to here.

Installation

Step 1: Install SGLang

Install the kvcache-ai fork of SGLang (one of):

# Option A: One-click install (from ktransformers root)
./install.sh

# Option B: pip install
pip install sglang-kt

Important: Use sglang-kt (kvcache-ai fork), not the official sglang package. Run pip uninstall sglang first if you have the official version installed.

Step 2: Install KTransformers CPU Kernels

The KTransformers CPU kernels (kt-kernel) provide AMX-optimized computation for hybrid inference, for detailed installation instructions and troubleshooting, refer to the official kt-kernel installation guide.

Download Model

Download the official KIMI weights as GPU weights.

• huggingface: https://huggingface.co/moonshotai/Kimi-K2-Thinking
• modelscope: https://modelscope.cn/models/moonshotai/Kimi-K2-Thinking

Download the AMX INT4 quantized weights from https://huggingface.co/KVCache-ai/Kimi-K2-Thinking-CPU-weight as CPU weights.

How to start

python -m sglang.launch_server   --host 0.0.0.0   --port 60000   --model path/to/Kimi-K2-Thinking/   --kt-weight-path path/to/Kimi-K2-Instruct-CPU-weight/   --kt-cpuinfer 56   --kt-threadpool-count 2   --kt-num-gpu-experts 200   --kt-method AMXINT4   --attention-backend flashinfer   --trust-remote-code   --mem-fraction-static 0.98   --chunked-prefill-size 4096   --max-running-requests 37   --max-total-tokens 37000   --enable-mixed-chunk   --tensor-parallel-size 8   --enable-p2p-check   --disable-shared-experts-fusion

tips:

--kt-cpuinfer: is recommended to be set to (number of physical CPU cores - 8 (number of GPUs)).

--kt-num-gpu-experts: refers to the number of experts retained on GPUs, which should be adjusted according to your available GPU memory and expected KV cache space.

Test

When testing, you need to add --disable-radix-cache and --disable-chunked-prefix-cache when starting the server.

bench prefill

python -m sglang.bench_serving   --backend sglang   --host 127.0.0.1   --port 60000   --num-prompts 37 --random-input-len 1024 --random-output-len 1 --random-range-ratio 1.0 --dataset-name random

bench decode

python -m sglang.bench_serving   --backend sglang   --host 127.0.0.1   --port 60000   --num-prompts 37 --random-input-len 10 --random-output-len 512 --random-range-ratio 1.0 --dataset-name random

Performance

System Configuration:

• GPUs: 8× NVIDIA L20
• CPU: Intel(R) Xeon(R) Gold 6454S

Bench prefill

============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 not set
Successful requests:                     37
Benchmark duration (s):                  65.58
Total input tokens:                      37888
Total input text tokens:                 37888
Total input vision tokens:               0
Total generated tokens:                  37
Total generated tokens (retokenized):    37
Request throughput (req/s):              0.56
Input token throughput (tok/s):          577.74
Output token throughput (tok/s):         0.56
Total token throughput (tok/s):          578.30
Concurrency:                             23.31
----------------End-to-End Latency----------------
Mean E2E Latency (ms):                   41316.50
Median E2E Latency (ms):                 41500.35
---------------Time to First Token----------------
Mean TTFT (ms):                          41316.48
Median TTFT (ms):                        41500.35
P99 TTFT (ms):                           65336.31
---------------Inter-Token Latency----------------
Mean ITL (ms):                           0.00
Median ITL (ms):                         0.00
P95 ITL (ms):                            0.00
P99 ITL (ms):                            0.00
Max ITL (ms):                            0.00
==================================================

Bench decode

============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 not set
Successful requests:                     37
Benchmark duration (s):                  412.66
Total input tokens:                      370
Total input text tokens:                 370
Total input vision tokens:               0
Total generated tokens:                  18944
Total generated tokens (retokenized):    18618
Request throughput (req/s):              0.09
Input token throughput (tok/s):          0.90
Output token throughput (tok/s):         45.91
Total token throughput (tok/s):          46.80
Concurrency:                             37.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms):                   412620.35
Median E2E Latency (ms):                 412640.56
---------------Time to First Token----------------
Mean TTFT (ms):                          3551.87
Median TTFT (ms):                        3633.59
P99 TTFT (ms):                           3637.37
---------------Inter-Token Latency----------------
Mean ITL (ms):                           800.53
Median ITL (ms):                         797.89
P95 ITL (ms):                            840.06
P99 ITL (ms):                            864.96
Max ITL (ms):                            3044.56
==================================================