489 lines
18 KiB
Plaintext
489 lines
18 KiB
Plaintext
---
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title: DeepSeek-V4
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metatags:
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description: "Deploy DeepSeek-V4 with SGLang — a next-generation MoE model from DeepSeek. Blackwell deployments use the FP4 checkpoint; Hopper deployments use the FP8 checkpoint."
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tag: NEW
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---
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## 1. Model Introduction
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**DeepSeek-V4** is the next-generation Mixture-of-Experts model from DeepSeek, released 2026-04-24 under an **MIT License**. It ships as two Instruct repos (one per variant) plus matching Base repos:
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<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
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<colgroup>
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<col style={{width: "30%"}} />
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<col style={{width: "15%"}} />
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<col style={{width: "15%"}} />
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<col style={{width: "40%"}} />
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</colgroup>
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<thead>
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<tr style={{borderBottom: "2px solid #d55816"}}>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Variant</th>
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<th style={{textAlign: "right", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Total params</th>
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<th style={{textAlign: "right", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Active (MoE)</th>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Use</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong><a href="https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash">DeepSeek-V4-Flash</a></strong></td>
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<td style={{padding: "9px 12px", textAlign: "right", backgroundColor: "rgba(255,255,255,0.05)"}}><strong>284B</strong></td>
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<td style={{padding: "9px 12px", textAlign: "right", backgroundColor: "rgba(255,255,255,0.02)"}}>13B</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>single-node serving: B200 / GB300 / H200 on 4 GPUs</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong><a href="https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro">DeepSeek-V4-Pro</a></strong></td>
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<td style={{padding: "9px 12px", textAlign: "right", backgroundColor: "rgba(255,255,255,0.05)"}}><strong>1.6T</strong></td>
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<td style={{padding: "9px 12px", textAlign: "right", backgroundColor: "rgba(255,255,255,0.02)"}}>49B</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>high-capacity: B200 8 GPU / GB300 4 GPU / H200 16 GPU (2 nodes)</td>
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</tr>
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</tbody>
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</table>
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The Instruct repos ship **FP4 MoE experts + FP8 attention / dense** (one mixed-precision checkpoint covers all GPUs that support FP4). The Base (pre-trained only) variants — `DeepSeek-V4-Flash-Base`, `DeepSeek-V4-Pro-Base` — ship pure FP8 mixed and are **not** for chat / tool calling.
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**Key Features** (per the official model card):
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- **Hybrid Attention Architecture** — combines Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) for long-context efficiency. At 1M-token context, DeepSeek-V4-Pro uses only ~27% of per-token inference FLOPs and ~10% of KV cache compared with DeepSeek-V3.2.
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- **Manifold-Constrained Hyper-Connections (mHC)** — strengthens residual connections, improving signal-propagation stability across layers while preserving expressivity.
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- **Muon optimizer** — faster convergence and greater training stability.
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- **Context length: 1M tokens**; pre-trained on 32T+ diverse, high-quality tokens.
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- **Three reasoning modes**: *Non-think* (fast, intuitive responses), *Think High* (conscious logical analysis, slower but more accurate), *Think Max* (push reasoning to its fullest extent). Recommend a ≥ 384K context window when running Think Max.
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- Ships with a dedicated `encoding_dsv4.encode_messages` Python encoder + DSML tool-call grammar (`<｜DSML｜tool_calls>` / `<｜DSML｜invoke>` / `<｜DSML｜parameter>`).
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**Recommended Generation Parameters:** `temperature=1.0`, `top_p=1.0` (per the official model card).
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**License:** MIT.
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**Resources:**
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- HuggingFace: [DeepSeek-V4-Flash](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash), [DeepSeek-V4-Pro](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro)
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- ModelScope: [DeepSeek-V4-Flash](https://modelscope.cn/models/deepseek-ai/DeepSeek-V4-Flash), [DeepSeek-V4-Pro](https://modelscope.cn/models/deepseek-ai/DeepSeek-V4-Pro)
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## 2. SGLang Installation
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SGLang offers multiple installation methods. Choose based on your hardware platform.
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Please refer to the [official SGLang installation guide](../../../docs/get-started/install) for installation instructions.
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**Docker Images by Hardware Platform:**
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<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
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<colgroup>
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<col style={{width: "55%"}} />
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<col style={{width: "45%"}} />
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</colgroup>
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<thead>
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<tr style={{borderBottom: "2px solid #d55816"}}>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Hardware Platform</th>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Docker Image</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>NVIDIA B200</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>lmsysorg/sglang:deepseek-v4-blackwell</code></td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>NVIDIA GB300</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>lmsysorg/sglang:deepseek-v4-grace-blackwell</code></td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>NVIDIA H200</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>lmsysorg/sglang:deepseek-v4-hopper</code></td>
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</tr>
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</tbody>
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</table>
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For how to actually launch one of these images, see [Install → Method 3: Using Docker](../../../docs/get-started/install#method-3-using-docker). A minimal example (substitute the image tag for your platform and the inner `sglang serve ...` with whatever the [command generator](#3-model-deployment) below produces):
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```bash Command
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docker run --gpus all \
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--shm-size 32g \
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-p 30000:30000 \
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-v ~/.cache/huggingface:/root/.cache/huggingface \
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--env "HF_TOKEN=<your-hf-token>" \
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--ipc=host \
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lmsysorg/sglang:deepseek-v4-blackwell \
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sglang serve <use args below>
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```
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## 3. Model Deployment
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SGLang supports three main serving recipes for DeepSeek-V4 with different latency/throughput trade-offs (`low-latency`, `balanced`, `max-throughput`), plus specialized recipes for long-context (`cp`, prefill context-parallel) and prefill/decode disaggregation (`pd-disagg`). The interactive generator below emits the exact launch command for any `(hardware, variant, recipe)` combination.
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<Note>
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For H200 GPU deployments, use the SGLang checkpoint under `sgl-project`, not the default DeepSeek checkpoint.
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</Note>
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### 3.1 Basic Configuration
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**Interactive Command Generator**: Use the selector below to generate the deployment command for your hardware + recipe combination.
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import { DeepSeekV4Deployment } from "/src/snippets/autoregressive/deepseek-v4-deployment.jsx";
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<DeepSeekV4Deployment />
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### 3.2 Configuration Tips
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{/* TODO: expand this section as more recipes are validated end-to-end. */}
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**Concurrency & DeepEP dispatch buffer**
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Must hold: `max-running-requests × MTP_draft_tokens ≤ SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK`. Violating it blows DeepEP's dispatch buffer at steady-state load (`deep_ep.cpp:1105`). When tuning, move `--cuda-graph-max-bs`, `--max-running-requests`, and the env together.
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The generator currently picks values on the **conservative** side (mirroring an internal stress-test matrix). They run safely out of the box but likely leave throughput on the table — please tune them up toward your actual workload's peak concurrency and report findings back so the defaults can be revised.
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**MTP (Multi-Token Prediction, EAGLE)**
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- `low-latency`: steps=3, draft-tokens=4 → largest win at bs=1.
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- `balanced`: steps=1, draft-tokens=2 → gentler MTP, reduces throughput hit at higher batch.
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- `max-throughput`: MTP disabled — at saturation the verify step costs more than it saves.
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- MTP currently requires `SGLANG_ENABLE_SPEC_V2=1`.
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<a id="hopper-note" />
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**Hopper (H200) note**
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H200 image (`lmsysorg/sglang:deepseek-v4-hopper`) and FP8 checkpoints
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(`sgl-project/DeepSeek-V4-Flash-FP8`, `sgl-project/DeepSeek-V4-Pro-FP8`) are
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publicly available.
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PD-Disagg recipes on H200 may require `docker run --privileged --ulimit memlock=-1`
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(or `--device /dev/infiniband:/dev/infiniband --cap-add IPC_LOCK`) so mooncake
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can discover the IB HCAs; without IB exposure mooncake silently falls back to
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TCP, which can lead to garbled KV transfer on large checkpoints.
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**Base model usage**
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In order to use base models, please enable `SGLANG_FIX_DSV4_BASE_MODEL_LOAD=1` and use latest code, before the next round of testing matrix is finished.
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**GB300 PD-Disagg cross-pod MNNVL**
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On some GB300 clusters with cross-pod KV transfer over NVLink, mooncake may
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fail with `nvlink_transport.cpp:497 Requested address ... not found!`. If
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this happens, prepend `MC_FORCE_MNNVL=1 NCCL_MNNVL_ENABLE=1 NCCL_CUMEM_ENABLE=1`
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to both prefill and decode `sglang serve` commands.
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## 4. Model Invocation
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### 4.1 Basic Usage
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For basic API usage and request examples, see:
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- [Basic API Usage](../../../docs/basic_usage/send_request)
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Once the server is running (for example via the command generator above), send a request:
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```shell Command
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curl http://localhost:30000/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "deepseek-ai/DeepSeek-V4-Flash",
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"messages": [{"role": "user", "content": "What is 15% of 240?"}]
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}'
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```
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> **PD-Disagg note**: if you deployed with the `pd-disagg` recipe from the generator above, the prefill server is on port `30000`, the decode server on `30001`, and the **router** on port `8000` — client traffic should target `http://localhost:8000`, not `:30000`.
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### 4.2 Advanced Usage
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#### 4.2.1 Reasoning Parser
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Enable the `deepseek-v4` reasoning parser (check the box in the [command panel above](#3-model-deployment)) to separate thinking from the final answer into `reasoning_content` vs `content`.
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**Streaming with Thinking Process:**
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```python Example
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:30000/v1",
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api_key="EMPTY"
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)
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response = client.chat.completions.create(
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model="deepseek-ai/DeepSeek-V4-Flash",
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messages=[
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{"role": "user", "content": "Solve this problem step by step: What is 15% of 240?"}
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],
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max_tokens=2048,
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extra_body={"chat_template_kwargs": {"thinking": True}},
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stream=True,
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)
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thinking_started = False
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has_thinking = False
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has_answer = False
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for chunk in response:
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if not chunk.choices:
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continue
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delta = chunk.choices[0].delta
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if getattr(delta, "reasoning_content", None):
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if not thinking_started:
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print("=============== Thinking =================", flush=True)
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thinking_started = True
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has_thinking = True
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print(delta.reasoning_content, end="", flush=True)
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if delta.content:
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if has_thinking and not has_answer:
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print("\n=============== Content =================", flush=True)
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has_answer = True
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print(delta.content, end="", flush=True)
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print()
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```
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**Output Example:**
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```text Output
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Pending update — replace with real server output after deployment.
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```
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#### 4.2.2 Tool Calling
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Enable the `deepseekv4` tool-call parser (check the box in the [command panel above](#3-model-deployment)) to surface structured tool calls via `message.tool_calls`.
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**Python Example (with Thinking Process):**
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```python Example
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from openai import OpenAI
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client = OpenAI(
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base_url="http://localhost:30000/v1",
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api_key="EMPTY"
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)
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_weather",
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"description": "Get the current weather for a location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {"type": "string", "description": "The city name"},
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"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
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},
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"required": ["location"],
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},
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},
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}
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]
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response = client.chat.completions.create(
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model="deepseek-ai/DeepSeek-V4-Flash",
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messages=[{"role": "user", "content": "What's the weather in Beijing?"}],
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tools=tools,
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extra_body={"chat_template_kwargs": {"thinking": True}},
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stream=True,
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)
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thinking_started = False
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has_thinking = False
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tool_calls_accumulator = {}
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for chunk in response:
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if not chunk.choices:
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continue
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delta = chunk.choices[0].delta
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if getattr(delta, "reasoning_content", None):
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if not thinking_started:
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print("=============== Thinking =================", flush=True)
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thinking_started = True
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has_thinking = True
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print(delta.reasoning_content, end="", flush=True)
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if getattr(delta, "tool_calls", None):
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if has_thinking and thinking_started:
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print("\n=============== Content =================\n", flush=True)
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thinking_started = False
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for tool_call in delta.tool_calls:
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index = tool_call.index
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if index not in tool_calls_accumulator:
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tool_calls_accumulator[index] = {"name": None, "arguments": ""}
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if tool_call.function:
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if tool_call.function.name:
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tool_calls_accumulator[index]["name"] = tool_call.function.name
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if tool_call.function.arguments:
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tool_calls_accumulator[index]["arguments"] += tool_call.function.arguments
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if delta.content:
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print(delta.content, end="", flush=True)
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for index, tool_call in sorted(tool_calls_accumulator.items()):
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print(f"Tool Call: {tool_call['name']}")
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print(f" Arguments: {tool_call['arguments']}")
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print()
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```
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**Output Example:**
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```text Output
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Pending update — replace with real server output after deployment.
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```
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## 5. Benchmark
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### 5.1 Speed Benchmark on Blackwell
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**Test Environment:**
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- Hardware: NVIDIA B200 GPU (4x)
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- Model: DeepSeek-V4-Flash (FP4)
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- Tensor Parallelism: 4
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- sglang version: Pending update
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We use SGLang's built-in benchmarking tool to conduct performance evaluation on the [ShareGPT_Vicuna_unfiltered](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) dataset. This dataset contains real conversation data and can better reflect performance in actual use scenarios. To simulate real-world usage patterns, we configure each request with 1024 input tokens and 1024 output tokens, representing typical medium-length conversations with detailed responses.
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#### 5.1.1 Latency-Sensitive Benchmark
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- **Model Deployment Command:** see the [command panel above](#3-model-deployment).
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- Benchmark Command:
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```shell Command
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python3 -m sglang.bench_serving \
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--backend sglang \
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--host 127.0.0.1 \
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--port 30000 \
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--model deepseek-ai/DeepSeek-V4-Flash \
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--random-input-len 1024 \
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--random-output-len 1024 \
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--num-prompts 10 \
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--max-concurrency 1
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```
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- **Test Results:**
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```text Output
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Pending update — replace with real bench_serving output after the latency run.
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```
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#### 5.1.2 Throughput-Sensitive Benchmark
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- **Model Deployment Command:** see the [command panel above](#3-model-deployment).
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- Benchmark Command:
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```shell Command
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python3 -m sglang.bench_serving \
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--backend sglang \
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--host 127.0.0.1 \
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--port 30000 \
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--model deepseek-ai/DeepSeek-V4-Flash \
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--random-input-len 1024 \
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--random-output-len 1024 \
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--num-prompts 1000 \
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--max-concurrency 100
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```
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- **Test Results:**
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```text Output
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Pending update — replace with real bench_serving output after the throughput run.
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```
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### 5.2 Accuracy Benchmark
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#### 5.2.1 GSM8K Benchmark
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- **Benchmark Command:**
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```shell Command
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python3 -m sglang.test.few_shot_gsm8k --num-questions 200 --port 30000
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```
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- **Test Results:**
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- DeepSeek-V4-Flash (FP4, Blackwell)
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```
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Pending update
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```
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- DeepSeek-V4-Flash (FP8, Hopper)
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```
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Pending update
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```
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#### 5.2.2 MMLU Benchmark
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- **Benchmark Command:**
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```shell Command
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cd sglang
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bash benchmark/mmlu/download_data.sh
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python3 benchmark/mmlu/bench_sglang.py --nsub 10 --port 30000
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```
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- **Test Results:**
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- DeepSeek-V4-Flash (FP4, Blackwell)
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```
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Pending update
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```
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- DeepSeek-V4-Flash (FP8, Hopper)
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```
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Pending update
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```
|
||
|
||
### 5.3 Speed Benchmark on Hopper
|
||
|
||
**Test Environment:**
|
||
|
||
- Hardware: NVIDIA H200 GPU (4x)
|
||
- Model: DeepSeek-V4-Flash (FP8)
|
||
- Tensor Parallelism: 4
|
||
- sglang version: Pending update
|
||
|
||
#### 5.3.1 Latency-Sensitive Benchmark
|
||
|
||
- **Model Deployment Command:** see the [command panel above](#3-model-deployment).
|
||
|
||
- Benchmark Command:
|
||
|
||
```shell Command
|
||
python3 -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--host 127.0.0.1 \
|
||
--port 30000 \
|
||
--model deepseek-ai/DeepSeek-V4-Flash \
|
||
--random-input-len 1024 \
|
||
--random-output-len 1024 \
|
||
--num-prompts 10 \
|
||
--max-concurrency 1
|
||
```
|
||
|
||
- **Test Results:**
|
||
|
||
```text Output
|
||
Pending update — replace with real bench_serving output after the latency run.
|
||
```
|
||
|
||
#### 5.3.2 Throughput-Sensitive Benchmark
|
||
|
||
- **Model Deployment Command:** see the [command panel above](#3-model-deployment).
|
||
|
||
- Benchmark Command:
|
||
|
||
```shell Command
|
||
python3 -m sglang.bench_serving \
|
||
--backend sglang \
|
||
--host 127.0.0.1 \
|
||
--port 30000 \
|
||
--model deepseek-ai/DeepSeek-V4-Flash \
|
||
--random-input-len 1024 \
|
||
--random-output-len 1024 \
|
||
--num-prompts 1000 \
|
||
--max-concurrency 100
|
||
```
|
||
|
||
- **Test Results:**
|
||
|
||
```text Output
|
||
Pending update — replace with real bench_serving output after the throughput run.
|
||
```
|