update readme

README.md

@@ -23,7 +23,7 @@ Our vision for KTransformers is to serve as a flexible platform for experimentin
 
 <h2 id="Updates">🔥 Updates</h2>
 
-* **Mar 27, 2025**: Support Multi-concurrency.
+* **Apr 2, 2025**: Support Multi-concurrency. ([Tutorial](./doc/en/balance-serve.md)).
 * **Mar 15, 2025**: Support ROCm on AMD GPU ([Tutorial](./doc/en/ROCm.md)).
 * **Mar 5, 2025**: Support unsloth 1.58/2.51 bits weights and [IQ1_S/FP8 hybrid](./doc/en/fp8_kernel.md) weights. Support 139K [Longer Context](./doc/en/DeepseekR1_V3_tutorial.md#v022--v023-longer-context--fp8-kernel) for DeepSeek-V3 and R1 in 24GB VRAM.
 * **Feb 25, 2025**: Support [FP8 GPU kernel](./doc/en/fp8_kernel.md) for DeepSeek-V3 and R1; [Longer Context](./doc/en/DeepseekR1_V3_tutorial.md#v022-longer-context).

doc/en/balance-serve.md

+# balance_serve backend (multi-concurrency) for ktransformers
+
+## Installation Guide
+
+### 1. Set Up Conda Environment
+We recommend using Miniconda3/Anaconda3 for environment management:
+
+```bash
+# Download Miniconda
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
+
+# Create environment
+conda create --name ktransformers python=3.11
+conda activate ktransformers
+
+# Install required libraries
+conda install -c conda-forge libstdcxx-ng
+
+# Verify GLIBCXX version (should include 3.4.32)
+strings ~/anaconda3/envs/ktransformers/lib/libstdc++.so.6 | grep GLIBCXX
+```
+
+> **Note:** Adjust the Anaconda path if your installation directory differs from `~/anaconda3`
+
+### 2. Install dependencies
+
+
+```bash
+sudo apt install libtbb-dev libssl-dev libcurl4-openssl-dev libaio1 libaio-dev libfmt-dev libgflags-dev zlib1g-dev patchelf
+```
+
+### 3. Build ktransformers
+
+```bash
+# Clone repository
+git clone https://github.com/kvcache-ai/ktransformers.git
+cd ktransformers
+git submodule update --init --recursive
+
+# Optional: Compile web interface
+# See: api/server/website.md
+
+# Install single NUMA dependencies
+sudo env USE_BALANCE_SERVE=1 PYTHONPATH="$(which python)" PATH="$(dirname $(which python)):$PATH" bash ./install.sh
+# Install Dual NUMA dependencies
+sudo env USE_BALANCE_SERVE=1 USE_NUMA=1 PYTHONPATH="$(which python)" PATH="$(dirname $(which python)):$PATH" bash ./install.sh
+```
+
+## Running DeepSeek-R1-Q4KM Models
+
+### Configuration for 24GB VRAM GPUs
+Use our optimized configuration for constrained VRAM:
+
+```bash
+python ktransformers/server/main.py \
+  --model_path <path_to_safetensor_config> \
+  --gguf_path <path_to_gguf_files> \
+  --optimize_config_path ktransformers/optimize/optimize_rules/DeepSeek-V3-Chat-serve.yaml \
+  --max_new_tokens 1024 \
+  --cache_lens 32768 \
+  --chunk_size 256 \
+  --max_batch_size 4 \
+  --backend_type balance_serve
+```
+
+It features the following arguments:
+
+- `--max_new_tokens`: Maximum number of tokens generated per request.
+- `--cache_lens`: Total length of kvcache allocated by the scheduler. All requests share a kvcache space. 
+- `--chunk_size`: Maximum number of tokens processed in a single run by the engine.
+corresponding to 32768 tokens, and the space occupied will be released after the requests are completed.
+- `--max_batch_size`: Maximum number of requests (prefill + decode) processed in a single run by the engine. (Supported only by `balance_serve`)
+- `--backend_type`: `balance_serve` is a multi-concurrency backend engine introduced in version v0.2.4. The original single-concurrency engine is `ktransformers`.