Beam 1.0 NIM Serving Recipes

NIM tuning configs (NIM_CONFIG_FILE) for Beam 1.0 (501B total / 23B active\nMoE), low-latency strategy: tensor parallelism with the DFlash2 speculative\ndrafter (block size 8).

Image: nvcr.io/nim/nvidia/sglang-model-free-nim-experimental:2.1.7-variant-beam-1.0-501b (amd64 and arm64)

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tr:nth-child(odd)]:bg-surface-sunken\">

File	GPU	Weights
`beam-1.0_h200_latency_fp8_tp8.yaml`	8x H200	FP8
`beam-1.0_h100_latency_fp8_tp8.yaml`	8x H100 (80 GB)	FP8 (256K context)
`beam-1.0_h20-3e_latency_fp8_tp8.yaml`	8x H20-3e (141 GB)	FP8
`beam-1.0_b200_latency_nvfp4_tp4.yaml`	4x B200	NVFP4
`beam-1.0_gb200_latency_nvfp4_tp4.yaml`	4x GB200 (1 tray)	NVFP4

Format

Each key is an sglang serve flag with - written as _ (true renders the\nbare flag); the optional ENV: mapping is exported into the backend\nenvironment. NIM owns --model-path, --served-model-name, --host and\n--port, so those come from NIM_MODEL_PATH / NIM_SERVED_MODEL_NAME\ninstead. The parallel layout (tp_size, pp_size, data_parallel_size,\nep_size) and context_length use the setting names of NIM's profiles, so the\nrecipe alone fixes the topology. The DFlash2 drafter is referenced by its\nHugging Face id and is downloaded into the NIM cache at startup (about 3.6 GB).

Launch

Download the recipes with the NGC CLI (ngc config set with an NGC API key\nthat can read this resource) and pick the file for your GPUs from the table\nbelow. NGC CLI saves files as 0600, so make the recipe readable by the\ncontainer user (nvs, UID 1000):

ngc registry resource download-version \"nim/nvidia/beam-1.0-recipe:2.1.7-beam-1.0-501b\"\nRECIPE=$PWD/beam-1.0-recipe_v2.1.7-beam-1.0-501b/beam-1.0_h200_latency_fp8_tp8.yaml\nchmod 0644 \"$RECIPE\"\n

The recipe fixes the whole topology; the launch only names the model and the\nrecipe (NIM_SERVED_MODEL_NAME is optional and keeps the served name beam).\nNIM_MODEL_PATH=hf://... downloads the weights from Hugging Face into the NIM\ncache on first start (pass HF_TOKEN to avoid anonymous rate limits):

IMAGE=nvcr.io/nim/nvidia/sglang-model-free-nim-experimental:2.1.7-variant-beam-1.0-501b\ndocker run -d --name beam-nim --gpus all --ipc=host \\\n  --ulimit memlock=-1 --ulimit stack=67108864 -p 8000:8000 \\\n  -v beam-nim-cache:/opt/nim/.cache \\\n  -v \"$RECIPE\":/opt/nim/recipe.yaml:ro \\\n  -e HF_TOKEN \\\n  -e NIM_MODEL_PATH=hf://reflection/Beam-1.0-501B-A23B-FP8 \\\n  -e NIM_CONFIG_FILE=/opt/nim/recipe.yaml \\\n  -e NIM_SERVED_MODEL_NAME=beam \\\n  \"$IMAGE\" nim-serve\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

GPU	Recipe	`NIM_MODEL_PATH`
8x H200	`beam-1.0_h200_latency_fp8_tp8.yaml`	`hf://reflection/Beam-1.0-501B-A23B-FP8`
8x H100	`beam-1.0_h100_latency_fp8_tp8.yaml`	`hf://reflection/Beam-1.0-501B-A23B-FP8`
8x H20-3e	`beam-1.0_h20-3e_latency_fp8_tp8.yaml`	`hf://reflection/Beam-1.0-501B-A23B-FP8`
4x B200	`beam-1.0_b200_latency_nvfp4_tp4.yaml`	`hf://reflection/Beam-1.0-501B-A23B-NVFP4`
4x GB200	`beam-1.0_gb200_latency_nvfp4_tp4.yaml`	`hf://reflection/Beam-1.0-501B-A23B-NVFP4`

On an 8-GPU B200 host, pick four GPUs with --gpus '\"device=4,5,6,7\"'.

No NIM_* override or NIM_MODEL_PROFILE is needed: NIM selects the profile\nthe recipe describes. list-model-profiles with the same mounts shows the\nprofile and its GPU memory figure.

To use weights already on disk instead of downloading from Hugging Face, mount the directory (for example\n-v /path/to/Beam-1.0-501B-A23B-FP8:/models/model:ro), set\nNIM_MODEL_PATH=/models/model, and make it readable with chmod -R a+rX.

H100

TP8 FP8 puts about 60 GB of target weights and 1.4 GB of DFlash2 drafter on\neach 80 GB GPU, so the H100 file sets mem_fraction_static to 0.88 and\ncontext_length to 262144. A single prompt close to 256K tokens can exceed\nthe KV cache capacity; the full 1M context needs GPUs with more memory (H200).

GB200

The GB200 file sets the NCCL multi-node NVLink options and limits CUDA graphs\nto decode batch sizes up to 64 (no prefill graphs). Launch it with the IMEX\nchannel and the two ulimits:

docker run ... --ulimit memlock=-1 --ulimit stack=67108864 \\\n  -e NVIDIA_IMEX_CHANNELS=0 ... \"$IMAGE\" nim-serve\n

On some GB200 hosts the driver refuses to instantiate CUDA graphs inside a\ncontainer (the log shows Failed to create CPU mapping, or the first request\nfails with CUDA-capable device(s) is/are busy or unavailable). Add\n--privileged to the docker run command on such hosts.

SGLang's symmetric memory and NVLink multicast need IMEX on GB200, even inside\none tray: run nvidia-imex on the host (a single-node domain is enough for\nTP4) and pass NVIDIA_IMEX_CHANNELS=0 so the container gets\n/dev/nvidia-caps-imex-channels/channel0.

Query: curl localhost:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{\"model\":\"beam\",\"messages\":[{\"role\":\"user\",\"content\":\"Hello\"}]}'

License/Terms of Use

GOVERNING TERMS: The NIM container is governed by the NVIDIA Software License Agreement and the Product-Specific Terms for NVIDIA AI Products.

The use of any models with this container is governed by the NVIDIA Open Model Agreement in addition to any terms which govern the specific models used.

These recipes are validated for Beam 1.0 (reflection/Beam-1.0-501B-A23B-FP8, reflection/Beam-1.0-501B-A23B-NVFP4 and the reflection/Beam-1.0-501B-A23B-DFlash2 drafter). NVIDIA does not provide these models; their use is governed by the Apache License 2.0.

You are responsible for ensuring that your use of any provided models complies with all applicable laws.

Get Help

Getting started with the NIM

Deploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the NIM Container page for release documentation, deployment guides and more NIM Container LLM.

Enterprise Support

Get access to knowledge base articles and support cases or submit a ticket.

","resourceVersions",[249,250],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_222":193,"_86":223,"_43":224,"_225":-5,"_226":227,"_89":-5,"_228":229,"_230":231,"_232":136,"_233":-5,"_234":-5,"_235":26,"_236":-5,"_237":26,"_238":239,"_240":26,"_241":239,"_242":26,"_243":239,"_244":26,"_245":239,"_246":26,"_247":248},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nim/nvidia/resources/beam-1.0-recipe/-","version","-","resolvedVersion","2.1.7-variant-beam-1.0-501b","imageReference","registry","targetPath","cliDownloadCommand","ngc registry resource download-version nim/nvidia/beam-1.0-recipe:2.1.7-variant-beam-1.0-501b","helmFetchCommand","viewAllVersionsPath","/orgs/nim/nvidia/resources/beam-1.0-recipe/-/version-history","accessCheckResult",{"_221":136},"hasRelatedCollections","showNimMetadataTab","context",{"_80":81,"_82":83},1793676846288,[],[],[212,213,214,215,216,217,218],{"_39":53,"_41":62,"_43":55,"_45":56,"_47":154,"_49":50},{"_39":53,"_41":60,"_43":55,"_45":56,"_47":61,"_49":50},{"_39":53,"_41":153,"_43":55,"_45":56,"_47":220,"_49":50},{"_39":53,"_41":58,"_43":55,"_45":56,"_47":219,"_49":50},{"_39":53,"_41":54,"_43":55,"_45":56,"_47":57,"_49":50},{"_39":51,"_41":52,"_43":44,"_45":46,"_47":48,"_49":50},{"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50},75,47,"allowed","versionId","Beam 1.0 verified tuning configs (H200, H100, H20-3e, B200, GB200)","UPLOAD_COMPLETE","releaseType","createdDate",["D",1793663247651],"totalSizeInBytes",5852,"totalFileCount",5,"isSigned","digest","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_73":191,"_251":253,"_225":-5,"_232":136,"_235":26,"_247":252,"_237":26},{"_73":193,"_251":193,"_225":-5,"_232":136,"_235":26,"_247":252,"_237":26},"name",[],"Latest (2.1.7-variant-beam-1.0-501b)","nim/nvidia/beam-1.0-recipe","nim/nvidia/resources/beam-1.0-recipe","beam-1.0-recipe","NIM tuning configs (NIM_CONFIG_FILE) for Beam 1.0 501B-A23B, low-latency TP + DFlash2: H200/H100/H20-3e FP8 TP8, B200/GB200 NVFP4 TP4.","# Beam 1.0 NIM Serving Recipes\n\nNIM tuning configs (`NIM_CONFIG_FILE`) for Beam 1.0 (501B total / 23B active\nMoE), low-latency strategy: tensor parallelism with the DFlash2 speculative\ndrafter (block size 8).\n\nImage: `nvcr.io/nim/nvidia/sglang-model-free-nim-experimental:2.1.7-variant-beam-1.0-501b` (amd64 and arm64)\n\n| File | GPU | Weights |\n|---|---|---|\n| `beam-1.0_h200_latency_fp8_tp8.yaml` | 8x H200 | FP8 |\n| `beam-1.0_h100_latency_fp8_tp8.yaml` | 8x H100 (80 GB) | FP8 (256K context) |\n| `beam-1.0_h20-3e_latency_fp8_tp8.yaml` | 8x H20-3e (141 GB) | FP8 |\n| `beam-1.0_b200_latency_nvfp4_tp4.yaml` | 4x B200 | NVFP4 |\n| `beam-1.0_gb200_latency_nvfp4_tp4.yaml` | 4x GB200 (1 tray) | NVFP4 |\n\n## Format\n\nEach key is an `sglang serve` flag with `-` written as `_` (`true` renders the\nbare flag); the optional `ENV:` mapping is exported into the backend\nenvironment. NIM owns `--model-path`, `--served-model-name`, `--host` and\n`--port`, so those come from `NIM_MODEL_PATH` / `NIM_SERVED_MODEL_NAME`\ninstead. The parallel layout (`tp_size`, `pp_size`, `data_parallel_size`,\n`ep_size`) and `context_length` use the setting names of NIM's profiles, so the\nrecipe alone fixes the topology. The DFlash2 drafter is referenced by its\nHugging Face id and is downloaded into the NIM cache at startup (about 3.6 GB).\n\n## Launch\n\nDownload the recipes with the NGC CLI (`ngc config set` with an NGC API key\nthat can read this resource) and pick the file for your GPUs from the table\nbelow. NGC CLI saves files as 0600, so make the recipe readable by the\ncontainer user (`nvs`, UID 1000):\n\n```bash\nngc registry resource download-version \"nim/nvidia/beam-1.0-recipe:2.1.7-beam-1.0-501b\"\nRECIPE=$PWD/beam-1.0-recipe_v2.1.7-beam-1.0-501b/beam-1.0_h200_latency_fp8_tp8.yaml\nchmod 0644 \"$RECIPE\"\n```\n\nThe recipe fixes the whole topology; the launch only names the model and the\nrecipe (`NIM_SERVED_MODEL_NAME` is optional and keeps the served name `beam`).\n`NIM_MODEL_PATH=hf://...` downloads the weights from Hugging Face into the NIM\ncache on first start (pass `HF_TOKEN` to avoid anonymous rate limits):\n\n```bash\nIMAGE=nvcr.io/nim/nvidia/sglang-model-free-nim-experimental:2.1.7-variant-beam-1.0-501b\ndocker run -d --name beam-nim --gpus all --ipc=host \\\n --ulimit memlock=-1 --ulimit stack=67108864 -p 8000:8000 \\\n -v beam-nim-cache:/opt/nim/.cache \\\n -v \"$RECIPE\":/opt/nim/recipe.yaml:ro \\\n -e HF_TOKEN \\\n -e NIM_MODEL_PATH=hf://reflection/Beam-1.0-501B-A23B-FP8 \\\n -e NIM_CONFIG_FILE=/opt/nim/recipe.yaml \\\n -e NIM_SERVED_MODEL_NAME=beam \\\n \"$IMAGE\" nim-serve\n```\n\n| GPU | Recipe | `NIM_MODEL_PATH` |\n|---|---|---|\n| 8x H200 | `beam-1.0_h200_latency_fp8_tp8.yaml` | `hf://reflection/Beam-1.0-501B-A23B-FP8` |\n| 8x H100 | `beam-1.0_h100_latency_fp8_tp8.yaml` | `hf://reflection/Beam-1.0-501B-A23B-FP8` |\n| 8x H20-3e | `beam-1.0_h20-3e_latency_fp8_tp8.yaml` | `hf://reflection/Beam-1.0-501B-A23B-FP8` |\n| 4x B200 | `beam-1.0_b200_latency_nvfp4_tp4.yaml` | `hf://reflection/Beam-1.0-501B-A23B-NVFP4` |\n| 4x GB200 | `beam-1.0_gb200_latency_nvfp4_tp4.yaml` | `hf://reflection/Beam-1.0-501B-A23B-NVFP4` |\n\nOn an 8-GPU B200 host, pick four GPUs with `--gpus '\"device=4,5,6,7\"'`.\n\nNo `NIM_*` override or `NIM_MODEL_PROFILE` is needed: NIM selects the profile\nthe recipe describes. `list-model-profiles` with the same mounts shows the\nprofile and its GPU memory figure.\n\nTo use weights already on disk instead of downloading from Hugging Face, mount the directory (for example\n`-v /path/to/Beam-1.0-501B-A23B-FP8:/models/model:ro`), set\n`NIM_MODEL_PATH=/models/model`, and make it readable with `chmod -R a+rX`.\n\n### H100\n\nTP8 FP8 puts about 60 GB of target weights and 1.4 GB of DFlash2 drafter on\neach 80 GB GPU, so the H100 file sets `mem_fraction_static` to 0.88 and\n`context_length` to 262144. A single prompt close to 256K tokens can exceed\nthe KV cache capacity; the full 1M context needs GPUs with more memory (H200).\n\n### GB200\n\nThe GB200 file sets the NCCL multi-node NVLink options and limits CUDA graphs\nto decode batch sizes up to 64 (no prefill graphs). Launch it with the IMEX\nchannel and the two ulimits:\n\n```bash\ndocker run ... --ulimit memlock=-1 --ulimit stack=67108864 \\\n -e NVIDIA_IMEX_CHANNELS=0 ... \"$IMAGE\" nim-serve\n```\n\nOn some GB200 hosts the driver refuses to instantiate CUDA graphs inside a\ncontainer (the log shows `Failed to create CPU mapping`, or the first request\nfails with `CUDA-capable device(s) is/are busy or unavailable`). Add\n`--privileged` to the `docker run` command on such hosts.\n\nSGLang's symmetric memory and NVLink multicast need IMEX on GB200, even inside\none tray: run `nvidia-imex` on the host (a single-node domain is enough for\nTP4) and pass `NVIDIA_IMEX_CHANNELS=0` so the container gets\n`/dev/nvidia-caps-imex-channels/channel0`.\n\nQuery: `curl localhost:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{\"model\":\"beam\",\"messages\":[{\"role\":\"user\",\"content\":\"Hello\"}]}'`\n\n## License/Terms of Use\n\n**GOVERNING TERMS:** The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/).\n\nThe use of any models with this container is governed by the [NVIDIA Open Model Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/) in addition to any terms which govern the specific models used.\n\nThese recipes are validated for Beam 1.0 (reflection/Beam-1.0-501B-A23B-FP8, reflection/Beam-1.0-501B-A23B-NVFP4 and the reflection/Beam-1.0-501B-A23B-DFlash2 drafter). NVIDIA does not provide these models; their use is governed by the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).\n\nYou are responsible for ensuring that your use of any provided models complies with all applicable laws.\n\n## Get Help\n\n### Getting started with the NIM\n\nDeploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the NIM Container page for release documentation, deployment guides and more [NIM Container LLM](https://docs.nvidia.com/nim/large-language-models/latest/introduction.html).\n\n### Enterprise Support\n\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).","Beam 1.0 501B Recipe",["D",1793663248396],"reflection","/api/image/nim/nvidia/resources/beam-1.0-recipe","bias","explainability","privacy","safetyAndSecurity",[276],[272,273],[],[],[],{"_87":129,"_121":129,"_122":275,"_124":125},{"_87":120,"_121":120,"_122":274,"_124":125},{"_126":125,"_121":120},{"_126":125,"_121":129},{"_87":277,"_121":278,"_122":279,"_124":280},"NVIDIA NIM","nimmcro_nvidia_nim",{"_126":281,"_121":278},"feature","nvidia_nim",{"_159":1411,"_161":1412},"ngcApps",[1364],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1345":1346,"_1347":1348,"_1349":1350,"_1351":1352,"_1353":293,"_190":319,"_1354":1355,"_1356":50,"_1357":136,"_1358":136,"_1359":1360,"_1361":136,"_1362":1363},"amplitude",{"_1342":1343,"_954":-7,"_1344":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1336":1337,"_1338":1339,"_1340":1341},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1793676846166,"1.30.0","defaultSearchSuggestions",{"_1224":239,"_1225":1226,"_1227":1228,"_1229":1230},"announcements",[],"consentState",{"_357":1214,"_1215":26,"_1216":26,"_1217":136,"_1218":1219},"consentConfig",{"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354,"_355":356,"_357":358},"showContactAdmin",1793676846167,[],[],[333,334,335,336,337,338],{"_39":53,"_41":62,"_43":59,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":60,"_43":59,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":58,"_43":59,"_45":-5,"_47":-5,"_49":50},{"_39":53,"_41":54,"_43":59,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":59,"_45":-5,"_47":-5,"_49":50},{"_39":40,"_41":42,"_43":59,"_45":-5,"_47":-5,"_49":50},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. 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