---
type: "post"
id: "nvidia-adds-official-sglang-instructions-for-the-nvfp4-00fbd0"
slug: "nvidia-adds-official-sglang-instructions-for-the-nvfp4-00fbd0"
title: "NVIDIA adds official SGLang instructions for the NVFP4 DeepSeek checkpoint"
description: "NVIDIA’s model-card update documents native SGLang support for its NVFP4 DeepSeek-V4.1-Flash-1017 checkpoint, alongside the previously documented vLLM path."
retrieval_nugget: "NVIDIA’s model-card update documents native SGLang support for its NVFP4 DeepSeek-V4.1-Flash-1017 checkpoint, alongside the previously documented vLLM path. The launch example was validated on four GB300 GPUs and includes a host-memory setting for Engram tables, giving Blackwell operators a second documented route to serve this checkpoint."
published_at: "2026-11-14"
updated_at: "2026-11-14"
record_date: "2026-11-14"
date_kind: "published_at"
topics: ["inference","open-models"]
entities: ["NVIDIA","SGLang","vLLM","DeepSeek-V4.1-Flash-1017-NVFP4","DeepSeek"]
related_patterns: []
novelty_label: "new_fact"
what_changed: "NVIDIA added official SGLang deployment instructions for its previously published NVFP4 DeepSeek checkpoint."
prior_refs: ["https://newruntime.com/posts/nvidia-has-published-an-nvfp4-quantized-deepseek-v4-a06093/"]
source_url: "https://huggingface.co/nvidia/DeepSeek-V4.1-Flash-1017-NVFP4/commit/3ae9c0d3fbbea5370bd06e679d22044db15883f1"
source_urls: ["https://huggingface.co/nvidia/DeepSeek-V4.1-Flash-1017-NVFP4/commit/3ae9c0d3fbbea5370bd06e679d22044db15883f1"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:nvidia-adds-official-sglang-instructions-for-the-nvfp4-00fbd0"
status: "published"
visuals: []
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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markdown_url: "https://newruntime.com/posts/nvidia-adds-official-sglang-instructions-for-the-nvfp4-00fbd0.md"
---

# NVIDIA adds official SGLang instructions for the NVFP4 DeepSeek checkpoint

## Retrieval answer

NVIDIA’s model-card update documents native SGLang support for its NVFP4 DeepSeek-V4.1-Flash-1017 checkpoint, alongside the previously documented vLLM path. The launch example was validated on four GB300 GPUs and includes a host-memory setting for Engram tables, giving Blackwell operators a second documented route to serve this checkpoint.

## Summary
NVIDIA’s model-card update documents native SGLang support for its NVFP4 DeepSeek-V4.1-Flash-1017 checkpoint, alongside the previously documented vLLM path. The launch example was validated on four GB300 GPUs and includes a host-memory setting for Engram tables, giving Blackwell operators a second documented route to serve this checkpoint.

## What happened
NVIDIA updated the Hugging Face model card for `nvidia/DeepSeek-V4.1-Flash-1017-NVFP4` in a commit titled “Add SGLang deployment instructions.” The change adds SGLang to the supported runtime engines alongside vLLM. The card says SGLang loads the checkpoint natively from November 4, 2026, and points to the merged SGLang pull request #47935. This is a runtime-support and deployment-documentation change for an existing checkpoint, not an announcement of a new model or a new quantization format.

The card’s example launch command is validated on four NVIDIA GB300 GPUs, which use the Blackwell microarchitecture. It calls `sglang.launch_server` with the checkpoint path, tensor parallelism set to four, remote code trusted, an FP8 E4M3 KV cache, the FlashMLA attention backend, and a static memory fraction of 0.9. The command also sets the reasoning and tool-call parsers to `auto`. NVIDIA presents the configuration as an example for serving the checkpoint; it does not describe a universal hardware recipe.

The example enables speculative decoding with the DSpark algorithm and a block size of five. NVIDIA says operators can serve without speculative decoding by dropping the two `--speculative-*` flags. The command sets Engram placement to CPU, replication to `none`, and lookup to `auto`; the model card says this keeps the Engram tables in host memory, at about 97 GiB per node. That requirement is an important operational detail: the documented setup uses substantial host memory in addition to the four GPUs.

The card reports GSM8K accuracy of 96.82% with DSpark and 96.59% without it for this SGLang command. It also says text input, reasoning, and tool calling were validated with the command. The card does not present this result as a broad comparison across runtimes or workloads. Its vLLM notes remain distinct: the checkpoint’s Engram lookup tables are stored as MXFP4, which stock vLLM images cannot load, so the card directs users to a vLLM build with the referenced MXFP4 Engram support.

## What the archive adds
New Runtime covered NVIDIA’s publication of this NVFP4-quantized checkpoint on [November 1, 2026](https://newruntime.com/posts/nvidia-has-published-an-nvfp4-quantized-deepseek-v4-a06093/), noting its Blackwell focus and the need for patched vLLM. The SGLang instructions are a new fact on that known checkpoint story: they add a second documented runtime path, while the earlier note’s vLLM constraint remains relevant to users choosing that engine.

## Trend signal
This does not yet fit a pattern New Runtime tracks. The update improves runtime choice for one checkpoint, but does not by itself establish a broader shift in agent harnesses, interoperability protocols, or production economics. Operators can read it as a concrete compatibility milestone, not evidence that the same support or performance profile applies across other models, hardware, or serving workloads.

## Why now
The model card dates native SGLang loading from November 4, 2026, and points to SGLang pull request #47935, which the card identifies as merged. That timing turns the earlier model-specific deployment caveat into a current documented option. The source does not explain why NVIDIA prioritized this runtime support at this moment or describe a wider rollout plan.

## Connections to other events
New Runtime’s [October 10, 2026 note on vLLM support for NVIDIA Vera Rubin NVL72](https://newruntime.com/posts/vllm-adds-day-0-support-for-nvidia-vera-aaa9ff/) described runtime readiness arriving in layers: compatibility and tuned execution paths can precede broad production evidence. This checkpoint update has the same limited shape: a concrete runtime path is documented and tested on named hardware, but the model card’s validation is not equivalent to independent production evidence.

## Why it matters
For a team already serving this NVFP4 checkpoint on Blackwell, a native SGLang path can reduce dependence on a specially patched vLLM build and make SGLang an option to evaluate within an existing serving stack. The model card gives operators a starting command rather than only naming the runtime, and it makes several deployment choices visible: four-way tensor parallelism, FP8 E4M3 KV cache, FlashMLA, CPU placement for Engram, and optional DSpark decoding.

The operational trade-off is not simply “another supported runtime.” The example uses about 97 GiB of host memory per node for Engram tables, and its reported GSM8K difference between enabled and disabled DSpark is modest. Teams should weigh the runtime fit, host-memory capacity, latency and throughput on their own traffic, and the complexity of speculative decoding. The update makes SGLang testable; it does not establish that it is faster, cheaper, or more accurate than vLLM for a particular production workload.

## Limits and uncertainty
NVIDIA reports that the SGLang command was validated on four GB300 GPUs and gives GSM8K scores with and without DSpark. The model card does not provide a cross-runtime benchmark, serving throughput, latency, memory comparison against vLLM, or results across a broad set of production workloads. It does not establish compatibility with other GPU generations or configurations beyond the named test. The reported validation covers text input, reasoning, and tool calling; the source does not claim that every feature or workload has been tested. Teams should validate the exact model, runtime version, hardware, and request mix they intend to deploy.

## Skill to build now
If your team runs SGLang on GB300 and is evaluating this checkpoint, stage the model-card command in a disposable environment before changing production. Record whether the host-memory requirement of roughly 97 GiB per node is practical, compare the same representative prompts with and without the two DSpark flags, and verify text, reasoning, and tool calls against your own acceptance checks. Treat the published GSM8K scores as a reference point, not a substitute for workload-specific measurement.

## Reader takeaway
The useful change is a documented SGLang route for one NVIDIA NVFP4 DeepSeek checkpoint, with a four-GB300 launch example and explicit Engram and DSpark settings. It broadens the runtime choice, but teams should measure their own deployment: the source gives a validation point, not proof of a general performance win.

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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