-[^1]: DeepSeek-R1 cannot give the correct output if quantization is used or has precision issues (fixed in [b110084](https://github.com/sgl-project/sglang/commit/b110084654a1986f0148901190e2f280c605476f))
- [^2]: TPS@1 (Tokens Per Second for single request) is read directly from SGLang's logging.
- [^3]: CUDA error at graph capture.
- [^4]: CUDA out of memory.
- [^5]: Requires setting `mem-fraction-static=0.7` to avoid OOM errors.
- [^6]: TypeError: object of type 'NoneType' has no len().
- [^7]: All statistics are collected from the test bench. Token count is calculated using the same tokenizer used in inference.
- [^8]: Average Throughput(prefill+decode, token/s) = (total tokens)/(total time).
- [^9]: Average Decoding Throughput = (sum of (output tokens/duration) for each successful request)/(number of successful requests).
- [^10]: The maximum number of requests to run concurrently at a SGLang backend, controlled by `--max-running-requests`.
-[^11]: Tested by [Lzhang-Hub](https://github.com/sgl-project/sglang/issues/3956#issuecomment-2700514223).
1. DeepSeek-R1 cannot give the correct output if quantization is used or has precision issues (fixed in [b110084](https://github.com/sgl-project/sglang/commit/b110084654a1986f0148901190e2f280c605476f))
2. TPS@1 (Tokens Per Second for single request) is read directly from SGLang's logging.
3. CUDA error at graph capture.
4. CUDA out of memory.
5. Requires setting `mem-fraction-static=0.7` to avoid OOM errors.
6. TypeError: object of type 'NoneType' has no len().
7. All statistics are collected from the test bench. Token count is calculated using the same tokenizer used in inference.
8. Average Throughput(prefill+decode, token/s) = (total tokens)/(total time).
9. Average Decoding Throughput = (sum of (output tokens/duration) for each successful request)/(number of successful requests).
10. The maximum number of requests to run concurrently at a SGLang backend, controlled by `--max-running-requests`.
11. Tested by [Lzhang-Hub](https://github.com/sgl-project/sglang/issues/3956#issuecomment-2700514223).
**Description**: [MLA](https://arxiv.org/pdf/2405.04434) is an innovative attention mechanism introduced by the DeepSeek team, aimed at improving inference efficiency. SGLang has implemented specific optimizations for this, including:
-**Weight Absorption**: By applying the associative law of matrix multiplication to reorder computation steps, this method balances computation and memory access and improves efficiency in the decoding phase.
-**Flashinfer MLA Wrapper**: By providing `--enable-flashinfer-mla` argument, the server will use MLA kernels customized by Flashinfer. More details can be referred to [this document](https://docs.flashinfer.ai/api/mla.html). Under long input scenarios, flashinfer mla can improve performance significantly. Optimized triton kernels will be used when flashinfer mla is turned off.
-**FP8 Quantization**: W8A8 FP8 and KV Cache FP8 quantization enables efficient FP8 inference. Additionally, we have implemented Batched Matrix Multiplication (BMM) operator to facilitate FP8 inference in MLA with weight absorption.
-**CUDA Graph & Torch.compile**: Both MLA and Mixture of Experts (MoE) are compatible with CUDA Graph and Torch.compile, which reduces latency and accelerates decoding speed for small batch sizes.
Overall, with these optimizations, we have achieved up to a 7x acceleration in output throughput compared to the previous version.
<palign="center">
<imgsrc="https://lmsys.org/images/blog/sglang_v0_3/deepseek_mla.svg"alt="Multi-head Latent Attention for DeepSeek Series Models">
</p>
**Usage**: MLA optimization is enabled by default, to disable, use `--disable-mla`.
**Reference**: Check [Blog](https://lmsys.org/blog/2024-09-04-sglang-v0-3/#deepseek-multi-head-latent-attention-mla-throughput-optimizations) and [Slides](https://github.com/sgl-project/sgl-learning-materials/blob/main/slides/lmsys_1st_meetup_deepseek_mla.pdf) for more details.
## Data Parallelism Attention
**Description**: This optimization involves data parallelism (DP) for the MLA attention mechanism of DeepSeek Series Models, which allows for a significant reduction in the KV cache size, enabling larger batch sizes. Each DP worker independently handles different types of batches (prefill, decode, idle), which are then synchronized before and after processing through the Mixture-of-Experts (MoE) layer.
<palign="center">
<imgsrc="https://lmsys.org/images/blog/sglang_v0_4/dp_attention.svg"alt="Data Parallelism Attention for DeepSeek Series Models">
</p>
**Usage**: This optimization is aimed at improving throughput and should be used for scenarios with high QPS (Queries Per Second). Data Parallelism Attention optimization can be enabled by `--enable-dp-attention` for DeepSeek Series Models.
**Description**: For users with limited memory on a single node, SGLang supports serving DeepSeek Series Models, including DeepSeek V3, across multiple nodes using tensor parallelism. This approach partitions the model parameters across multiple GPUs or nodes to handle models that are too large for one node's memory.
**Usage**: Check [here](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#example-serving-with-2-h208) for usage examples.
## Block-wise FP8
**Description**: SGLang implements block-wise FP8 quantization with two key optimizations:
-**Activation**: E4M3 format using per-token-per-128-channel sub-vector scales with online casting.
-**Weight**: Per-128x128-block quantization for better numerical stability.
**Usage**: turn on by default for DeepSeek V3 models.
## Reasoning Content for DeepSeek R1
See [Separate Reasoning](https://docs.sglang.ai/backend/separate_reasoning.html).
SGLang provides several optimizations specifically designed for the DeepSeek model to boost its inference speed. This document outlines current optimizations for DeepSeek. Additionally, the SGLang team is actively developing enhancements for [DeepSeek V3](https://github.com/sgl-project/sglang/issues/2591).
...
...
@@ -75,65 +75,6 @@ Please refer to [the example](https://github.com/sgl-project/sglang/tree/main/be
-[Serving with four A100*8 nodes](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#example-serving-with-four-a1008-nodes).
**Description**: [MLA](https://arxiv.org/pdf/2405.04434) is an innovative attention mechanism introduced by the DeepSeek team, aimed at improving inference efficiency. SGLang has implemented specific optimizations for this, including:
-**Weight Absorption**: By applying the associative law of matrix multiplication to reorder computation steps, this method balances computation and memory access and improves efficiency in the decoding phase.
-**Flashinfer MLA Wrapper**: By providing `--enable-flashinfer-mla` argument, the server will use MLA kernels customized by Flashinfer. More details can be referred to [this document](https://docs.flashinfer.ai/api/mla.html). Under long input scenarios, flashinfer mla can improve performance significantly. Optimized triton kernels will be used when flashinfer mla is turned off.
-**FP8 Quantization**: W8A8 FP8 and KV Cache FP8 quantization enables efficient FP8 inference. Additionally, we have implemented Batched Matrix Multiplication (BMM) operator to facilitate FP8 inference in MLA with weight absorption.
-**CUDA Graph & Torch.compile**: Both MLA and Mixture of Experts (MoE) are compatible with CUDA Graph and Torch.compile, which reduces latency and accelerates decoding speed for small batch sizes.
Overall, with these optimizations, we have achieved up to a 7x acceleration in output throughput compared to the previous version.
<palign="center">
<imgsrc="https://lmsys.org/images/blog/sglang_v0_3/deepseek_mla.svg"alt="Multi-head Latent Attention for DeepSeek Series Models">
</p>
**Usage**: MLA optimization is enabled by default, to disable, use `--disable-mla`.
**Reference**: Check [Blog](https://lmsys.org/blog/2024-09-04-sglang-v0-3/#deepseek-multi-head-latent-attention-mla-throughput-optimizations) and [Slides](https://github.com/sgl-project/sgl-learning-materials/blob/main/slides/lmsys_1st_meetup_deepseek_mla.pdf) for more details.
### Data Parallelism Attention
**Description**: This optimization involves data parallelism (DP) for the MLA attention mechanism of DeepSeek Series Models, which allows for a significant reduction in the KV cache size, enabling larger batch sizes. Each DP worker independently handles different types of batches (prefill, decode, idle), which are then synchronized before and after processing through the Mixture-of-Experts (MoE) layer.
<palign="center">
<imgsrc="https://lmsys.org/images/blog/sglang_v0_4/dp_attention.svg"alt="Data Parallelism Attention for DeepSeek Series Models">
</p>
**Usage**: This optimization is aimed at improving throughput and should be used for scenarios with high QPS (Queries Per Second). Data Parallelism Attention optimization can be enabled by `--enable-dp-attention` for DeepSeek Series Models.
**Description**: For users with limited memory on a single node, SGLang supports serving DeepSeek Series Models, including DeepSeek V3, across multiple nodes using tensor parallelism. This approach partitions the model parameters across multiple GPUs or nodes to handle models that are too large for one node's memory.
**Usage**: Check [here](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#example-serving-with-2-h208) for usage examples.
### Block-wise FP8
**Description**: SGLang implements block-wise FP8 quantization with two key optimizations:
-**Activation**: E4M3 format using per-token-per-128-channel sub-vector scales with online casting.
-**Weight**: Per-128x128-block quantization for better numerical stability.
**Usage**: turn on by default for DeepSeek V3 models.
### Reasoning Content for DeepSeek R1
See [Separate Reasoning](https://docs.sglang.ai/backend/separate_reasoning.html).
