--- title: Hy4 preview description: "Deploy Tencent Hy4 preview with SGLang — launch recipes for the 770B-parameter Mixture-of-Experts model with MLA, DeepSeek Sparse Attention (DSA), and MTP speculative decoding, in BF16 on H200/B200/B300/GB300 and MXFP8 on Blackwell GPUs." tag: NEW --- ## Deployment For all methods and hardware platforms, see the [official SGLang installation guide](/docs/get-started/install). The Docker path below matches the **Docker** framing in the command panel. ```bash Command docker pull lmsysorg/sglang:hy4-preview ``` The `hy4-preview` image bundles the HYV4 model code, the suffix-aware `hunyuan` reasoning/tool-call parsers, and the NEXTN MTP runtime. Switch to `:latest` once a tagged release picks them up. For how to launch the image, see [Install → Method 3: Using Docker](/docs/get-started/install#method-3-using-docker), substituting the inner `sglang serve ...` with what the command generator below produces. Pick your hardware + recipe to generate the launch command. The two serving strategies map to whether the MTP (NextN) draft layer is on: - **Low-Latency** — MTP speculative decoding on (steps=3, draft-tokens=4). Fastest reply for a single user; pick for chat. - **High-Throughput** — MTP off. At saturation the draft+verify overhead outweighs the speedup; best for batch jobs. import { Deployment } from "/src/snippets/_deployment.jsx"; import { config } from "/src/snippets/configs/tencent/hy4-preview.jsx"; import { benchmarks } from "/src/snippets/configs/tencent/hy4-preview-benchmarks.jsx";

Panel controls (top of the command box):
## Playground The Playground lets you turn on additional knobs on top of whichever Deploy cell is currently selected. The base is read live from your Deploy selection — only your overrides change. The knobs come in two flavors: - **Built-in SGLang features** — parallelism overrides (TP / DP-Attention), the MoE a2a backend (DeepEP, which sets EP = TP), reasoning / tool-call parsers, speculative-decoding presets, prefill/decode disaggregation, and HiCache tiers. - **Hy4 specific** — `--reasoning-parser auto` / `--tool-call-parser auto` resolve to the suffix-aware Hunyuan parsers and read the real structural-token strings from the tokenizer vocab at runtime. Lines highlighted **green** are added by your overrides; lines with **red strikethrough** were in the base recipe but stripped by an override. Parallelism combinations beyond the listed recipes (DP-Attention, DeepEP, other TP degrees) are experimentation territory — any override flips the badge to **Not Verified** until the configuration is run end-to-end and submitted back. import { Playground } from "/src/snippets/_playground.jsx";

Panel controls reuse Python / Docker · ⧉ Copy · $ cURL · ⚙ Env from the Deploy panel, plus one extra:

## 1. Model Introduction **Hy4 preview** is Tencent's next-generation flagship Mixture-of-Experts language model: 770B total parameters with 49B active per token, pairing a DeepSeek-style MLA + sparse-attention stack with Hunyuan's own residual control, MoE routing, and attention gating. It is a **text-only** model (vocab 120,832; the endpoint rejects image input by design), released under **Apache-2.0**. **Key architecture:** - **MoE**: 78 layers — layer 0 is a dense MLP, the remaining 77 are sparse MoE with 256 routed experts + 1 shared expert, top-8 sigmoid-scored routing (routed scaling 2.827), expert intermediate size 2048, and bounded SwiGLU (clamp 10.0). - **MLA + DSA on every layer**: Multi-head Latent Attention (`q_lora_rank` 2048, `kv_lora_rank` 512, 192 nope + 64 rope head dims, `v_head_dim` 256) under DeepSeek Sparse Attention — indexer top-k 2048 with 32 index heads, indexers alternating full/shared (one full indexer every 4 layers), and an FP8 index cache. - **iHC residual control**: Hunyuan's own inter-layer residual gating (`enable_ihc`, `hc_mult` 4) with pre- and post-residual gate groups. Semantically distinct from DeepSeek-V4's mHC (no combination step, no Sinkhorn) — the implementations are not interchangeable. - **Gated MLA + attention sink**: element-wise attention output gating evaluated in fp32, plus a learnable per-head attention sink propagated through all attention backends. - **MTP (NextN)**: one built-in multi-token-prediction draft layer (`model.mtp_layers.0`, 10B parameters with ~0.7B active, present in both checkpoints) enabling speculative decoding out of the box. - **Long context**: 1M max positions (RoPE theta 1e7). Size `--context-length` to your KV budget — the sizing table in §2 suggests 262,144 (131,072 on H200). **Available models:** - [tencent/Hy4-preview](https://huggingface.co/tencent/Hy4-preview) — BF16 (~1.5TB weights) - [tencent/Hy4-preview-FP8](https://huggingface.co/tencent/Hy4-preview-FP8) — MXFP8 (ModelOpt recipe, UE8M0 group-32 weight scales, dynamic activations; ~760GB weights) **Recommended generation:** `temperature=0.9`, `top_p=1.0` (informational — SGLang applies the checkpoint's `generation_config.json` defaults; don't hardcode sampling parameters in client code). Thinking depth is controlled per request via `reasoning_effort` (defaults to `high`; `no_think` for direct responses — see §3.1). **Resources:** [HuggingFace](https://huggingface.co/tencent/Hy4-preview) · [GitHub](https://github.com/Tencent-Hunyuan/Hy4-preview). **Special tokens.** The Hy4 preview tokenizer's structural tokens are suffix-bearing (``, ``, ``, ``, ``). SGLang's Hunyuan reasoning/tool-call parsers resolve the real token strings from the tokenizer vocab at runtime, so `--reasoning-parser auto --tool-call-parser auto` work out of the box. ## 2. Configuration Tips **Hardware sizing.** BF16 weights are ~1.5TB and MXFP8 ~760GB; the MLA KV cache (compressed `kv_lora` 512 + rope 64, plus the DSA FP8 indexer cache, ≈95KB/token) is replicated per TP rank, so the per-rank pool left after weights sets the context ceiling:
GPU VRAM MXFP8 (~760GB) BF16 (~1.5TB)
H200 141GB Not supported — the MXFP8 kernel path requires SM100 (Blackwell) TP16, 2×8 nodes · 131K context
B200 192GB TP8, single node · 262K context TP16, 2×8 nodes · 262K context (8×192GB ≈ the weights alone)
B300 288GB TP4, single node · 262K context (~190GB/rank) TP8, single 8-GPU node · 262K context
GB300 288GB TP4, single node · 262K context TP8, 2×4 nodes · 262K context (GB300 hosts carry 4 GPUs)
On H200 (BF16, TP16 → ~95GB of weights per rank) the per-rank pool holds roughly 260K tokens of KV — set `--context-length 131072` there (the model default is 1M positions, far beyond the pool); the 192GB+ parts comfortably support 262144. **DSA attention backend.** Every layer runs DeepSeek Sparse Attention; SGLang auto-selects the DSA backend for HYV4 (`--attention-backend dsa` with `flashmla_sparse` prefill/decode and an FP8 indexer cache over a bf16 KV pool), so the recipes don't pass attention flags. Override only with a kernel-specific reason. **MXFP8 kernel stack.** The MXFP8 checkpoint self-describes via its ModelOpt `hf_quant_config` (dynamic activations, UE8M0 group-32 weight scales) — no `--quantization` flag needed; the recipes pin `--moe-runner-backend deep_gemm --fp8-gemm-backend deep_gemm`, the validated HYV4 MXFP8 path (the runtime also defaults both to `deep_gemm` for HYV4 when the flags are left unset). The MXFP8 kernel path requires SM100+ (Blackwell); H200 (SM90) cannot serve the MXFP8 checkpoint — use BF16 there. **CUDA graph decode vs eager.** Decode CUDA-graph capture is on by default; long-duration soak validation of the graph path on Hy4 is still in progress. If you hit instability under long mixed agentic workloads, pass `--disable-cuda-graph` to fall back to eager decode (a restart recovers cleanly either way). **MTP (NextN) speculative decoding.** Both checkpoints ship one draft layer; the preset is `--speculative-algorithm NEXTN --speculative-num-steps 3 --speculative-num-draft-tokens 4` (top-k 1). Speculative decoding reserves 4 draft-token slots per request, so the effective request budget is `prompt_tokens + max_tokens + 4 ≤ context length` — requests at the exact context boundary are rejected with the reservation accounted for. **Fail-fast guardrails.** The model rejects pipeline parallelism and `--enable-prefill-cp` before allocation. The recipes run pure TP; DP-Attention, DeepEP (which sets EP = TP at runtime), and other TP degrees are Playground experimentation territory. **Multi-node BF16.** The BF16 weights don't fit a single H200/B200/GB300 host, so those cells are 2-node TP recipes — run the generated command on every node (the panel injects `--nnodes 2 --node-rank --dist-init-addr`) and keep the weights on storage shared across ranks. **Text-only.** Image input is rejected with HTTP 400 by design — don't route vision traffic to this endpoint. **Large prefills under concurrency.** First-prefill latency on very large prompts can exceed 30 s under high concurrency; use a client timeout of 300 s (and moderate concurrency) for long-context agentic workloads instead of the common 30 s default. ## 3. Advanced Usage ### 3.1 Reasoning (`reasoning_effort`) Hy4 preview is a hybrid-thinking model: reasoning defaults to `high` (deep chain-of-thought — suits math, coding, and agentic tasks). The top-level OpenAI `reasoning_effort` field accepts the standard tiers (e.g. `high`); for direct responses, pass the model-specific `no_think` through `extra_body={"chat_template_kwargs": {"reasoning_effort": "no_think"}}`. The Deploy recipes enable the reasoning parser (`--reasoning-parser auto`) so thinking is separated into `reasoning_content` and the final answer into `content`: ```python Example from openai import OpenAI client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY") response = client.chat.completions.create( model="tencent/Hy4-preview-FP8", messages=[{"role": "user", "content": "Solve step by step: What is 15% of 240?"}], reasoning_effort="high", max_tokens=2048, ) msg = response.choices[0].message print("=============== Thinking =================") print(msg.reasoning_content) print("=============== Content =================") print(msg.content) ``` ```text Output Pending update — will be captured verbatim from a live Hy4 preview server. ``` ```python Example from openai import OpenAI client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY") response = client.chat.completions.create( model="tencent/Hy4-preview-FP8", messages=[{"role": "user", "content": "Give me a one-line summary of relativity."}], extra_body={"chat_template_kwargs": {"reasoning_effort": "no_think"}}, max_tokens=256, ) print("Content:", response.choices[0].message.content) ``` ```text Output Pending update — will be captured verbatim from a live Hy4 preview server. ``` ### 3.2 Tool Calling Hy4 preview emits tool calls through suffixed structural tokens with an `arg_key` / `arg_value` argument format; SGLang's Hunyuan tool-call parser reassembles them into OpenAI-compatible `message.tool_calls` with schema-aware type coercion, for both streaming and non-streaming requests. The Deploy recipes enable both parsers together (`--reasoning-parser auto --tool-call-parser auto`) — the reasoning parser strips thinking tokens before the tool-call parser runs. Tool-call output is parsed, not grammar-constrained: `tool_choice: "required"` / named-function forcing is not enforced with structural-tag guided decoding on the current implementation. ```python Example from openai import OpenAI client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY") tools = [ { "type": "function", "function": { "name": "get_weather", "description": "Get the current weather for a city.", "parameters": { "type": "object", "properties": { "city": {"type": "string"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, }, "required": ["city"], }, }, } ] response = client.chat.completions.create( model="tencent/Hy4-preview-FP8", messages=[{"role": "user", "content": "What's the weather in Beijing? Use fahrenheit."}], tools=tools, ) msg = response.choices[0].message print("Reasoning:", msg.reasoning_content) print("Content: ", msg.content) for tc in msg.tool_calls or []: print(f"Tool Call: {tc.function.name}") print(f" Arguments: {tc.function.arguments}") ``` ```text Output Pending update — will be captured verbatim from a live Hy4 preview server. ```