{ "hf_id": "reflection/Beam-1.0-501B-A23B", "meta": { "title": "Beam-1.0-501B-A23B", "slug": "beam-1.0-501b-a23b", "provider": "Reflection", "description": "Reflection's Beam — a sparse MoE language model (501B total / 23B active) with hybrid sliding-window + NoPE full attention, a 1M-token window, Harmony-format reasoning and tool calling, and an optional DFlash2 speculative drafter. Available in NVFP4, FP8 and BF16.", "date_added": "2026-10-16", "date_updated": "2026-10-16", "tasks": [ "text" ], "performance_headline": "501B/23B-active MoE with hybrid sliding-window attention, 1M context, and DFlash2 speculative decoding", "related_recipes": [], "hardware": { "b300": "verified", "h200": "verified", "mi300x": "unsupported", "mi325x": "unsupported", "mi350x": "unsupported", "mi355x": "unsupported" }, "default_hardware": "b300" }, "recommended_command": { "hardware": "b300", "strategy": "single_node_tp", "variant": "default", "node_count": 1, "deploy_type": "single_node", "env": { "VLLM_ENGINE_READY_TIMEOUT_S": "3600" }, "docker_image": "vllm/vllm-openai:reflection-beam", "command": "vllm serve reflection/Beam-1.0-501B-A23B-NVFP4 \\\n --kv-cache-dtype fp8 \\\n --tensor-parallel-size 4 \\\n --reasoning-parser beam \\\n --enable-auto-tool-choice \\\n --tool-call-parser beam", "argv": [ "vllm", "serve", "reflection/Beam-1.0-501B-A23B-NVFP4", "--kv-cache-dtype", "fp8", "--tensor-parallel-size", "4", "--reasoning-parser", "beam", "--enable-auto-tool-choice", "--tool-call-parser", "beam" ], "docker_command": "docker run --gpus all \\\n --privileged --ipc=host -p 8000:8000 \\\n -v ~/.cache/huggingface:/root/.cache/huggingface \\\n -e VLLM_ENGINE_READY_TIMEOUT_S=3600 \\\n vllm/vllm-openai:reflection-beam reflection/Beam-1.0-501B-A23B-NVFP4 \\\n --kv-cache-dtype fp8 \\\n --tensor-parallel-size 4 \\\n --reasoning-parser beam \\\n --enable-auto-tool-choice \\\n --tool-call-parser beam", "docker_argv": [ "docker", "run", "--gpus", "all", "--privileged", "--ipc=host", "-p", "8000:8000", "-v", "~/.cache/huggingface:/root/.cache/huggingface", "-e", "VLLM_ENGINE_READY_TIMEOUT_S=3600", "vllm/vllm-openai:reflection-beam", "reflection/Beam-1.0-501B-A23B-NVFP4", "--kv-cache-dtype", "fp8", "--tensor-parallel-size", "4", "--reasoning-parser", "beam", "--enable-auto-tool-choice", "--tool-call-parser", "beam" ], "strategy_spec": { "name": "single_node_tp", "deploy_type": "single_node", "display_name": "Tensor Parallel", "short_label": "TP", "orientation": "latency", "description": "Single-node tensor parallel. Splits the model across all local GPUs. TP size is set to the GPU count at deploy time. The simplest multi-GPU strategy — works for all model architectures.\n", "hardware_match": { "min_gpus": 1, "max_gpus": 8, "multi_node": false }, "vllm_args": [], "parallel_flag": "--tensor-parallel-size" }, "hardware_profile": { "brand": "NVIDIA", "generation": "blackwell", "compute_arch": "sm103", "display_name": "B300", "description": "NVIDIA B300 SXM 268 GB HBM3e · 8-GPU HGX B300 node (Blackwell Ultra)", "gpu_count": 8, "vram_gb": 2144, "multi_node": false }, "alternatives": { "single_node_tep": "/reflection/Beam-1.0-501B-A23B/strategies/single_node_tep.json", "single_node_dep": "/reflection/Beam-1.0-501B-A23B/strategies/single_node_dep.json", "kv_store_centralized_mooncake": "/reflection/Beam-1.0-501B-A23B/strategies/kv_store_centralized_mooncake.json", "kv_store_distributed_mooncake": "/reflection/Beam-1.0-501B-A23B/strategies/kv_store_distributed_mooncake.json" }, "by_hardware": { "b300": "/reflection/Beam-1.0-501B-A23B/hw/b300.json", "h100": "/reflection/Beam-1.0-501B-A23B/hw/h100.json", "h200": "/reflection/Beam-1.0-501B-A23B/hw/h200.json", "b200": "/reflection/Beam-1.0-501B-A23B/hw/b200.json", "gb200": "/reflection/Beam-1.0-501B-A23B/hw/gb200.json", "gb300": "/reflection/Beam-1.0-501B-A23B/hw/gb300.json" } }, "model": { "model_id": "reflection/Beam-1.0-501B-A23B", "min_vllm_version": "0.33.0", "docker_image": { "cu130": "vllm/vllm-openai:reflection-beam" }, "architecture": "moe", "parameter_count": "501B", "active_parameters": "23B", "context_length": 1048576, "base_args": [ "--kv-cache-dtype", "fp8" ], "base_env": { "VLLM_ENGINE_READY_TIMEOUT_S": "3600" }, "install": { "docker": { "command": "docker pull vllm/vllm-openai:reflection-beam", "note": "Beam support hasn't landed in a vLLM release yet, so it ships only in this image. Use Docker until it does." } } }, "features": { "reasoning": { "description": "Beam reasoning parser — splits the Harmony `analysis` channel into `reasoning_content`. Reads the checkpoint's `response_template`.", "args": [ "--reasoning-parser", "beam" ] }, "tool_calling": { "description": "Beam tool-call parser with automatic tool choice — tool-call grammar derived from the checkpoint's `response_template`.", "args": [ "--enable-auto-tool-choice", "--tool-call-parser", "beam" ] }, "spec_decoding": { "label": "DFlash2", "description": "DFlash2 speculative decoding with the Beam-trained drafter (DFlash2BeamDraftModel, 5 layers, block size 16), drafting 7 tokens per step. method is dflash; DFlash2 is selected by the draft architecture. The drafter runs on FlashAttention, and adaptive verification checks only the draft tokens likely to be accepted.", "args": [ "--speculative-config", "{\"method\":\"dflash\",\"model\":\"reflection/Beam-1.0-501B-A23B-DFlash2\",\"num_speculative_tokens\":7,\"attention_backend\":\"FLASH_ATTN\",\"enable_adaptive_verification\":true}" ] } }, "opt_in_features": [ "spec_decoding" ], "variants": { "default": { "model_id": "reflection/Beam-1.0-501B-A23B-NVFP4", "precision": "nvfp4", "vram_minimum_gb": 353, "description": "ModelOpt NVFP4 on the routed experts; all other weights BF16. W4A4 on Blackwell; W4A16 through Marlin on Hopper. Recommended on Blackwell and H100.", "precision_hardware": { "brand": "NVIDIA" }, "supported_hardware": [ "arch:sm90", "arch:sm100", "arch:sm103" ], "tp": { "blackwell": 4 }, "hardware_overrides": { "hopper": { "extra_args": [ "--moe-backend", "marlin" ] } }, "json": "/reflection/Beam-1.0-501B-A23B-NVFP4.json" }, "fp8": { "model_id": "reflection/Beam-1.0-501B-A23B-FP8", "precision": "fp8", "vram_minimum_gb": 604, "description": "FP8 per-channel weights (compressed-tensors); router, embeddings and lm_head unquantized. Recommended on H200.", "extra_args": [ "--moe-backend", "triton" ], "json": "/reflection/Beam-1.0-501B-A23B-FP8.json" }, "bf16": { "model_id": "reflection/Beam-1.0-501B-A23B", "precision": "bf16", "vram_minimum_gb": 1204, "description": "Native BF16 checkpoint (FP32 router weights and expert biases). Requires one 8x B200 or B300 node.", "supported_hardware": [ "b200", "b300" ], "extra_args": [ "--kv-cache-dtype", "auto" ] } }, "guide": "## Overview\n\n[Beam](https://huggingface.co/reflection/Beam-1.0-501B-A23B) is Reflection's\nsparse mixture-of-experts language model, with 501B total parameters and 23B\nactive parameters per token. Its 52 layers combine 512-token sliding-window\nattention with NoPE full-attention layers, route each token through 8 of 256\nexperts (layers 0-2 use dense MLPs), and support a context length of\n1,048,576 tokens.\n\nBeam uses the Harmony message format, with an `analysis` channel for\nreasoning and a `final` channel for the answer. Generation stops on\n`<|return|>` at the end of an answer and on `<|call|>` at the end of a tool\ncall.\n\n## Variants\n\n| Variant | Checkpoint | Weights on disk | Minimum GPU memory | Recommended hardware |\n|---|---|---|---|---|\n| NVFP4 (default) | [`reflection/Beam-1.0-501B-A23B-NVFP4`](https://huggingface.co/reflection/Beam-1.0-501B-A23B-NVFP4) | 294 GB | 353 GB | 4x B200/B300 or one GB200/GB300 tray (TP4); 8x H100 (TP8, Marlin) |\n| FP8 | [`reflection/Beam-1.0-501B-A23B-FP8`](https://huggingface.co/reflection/Beam-1.0-501B-A23B-FP8) | 503 GB | 604 GB | 8x H200 (TP8) |\n| BF16 | [`reflection/Beam-1.0-501B-A23B`](https://huggingface.co/reflection/Beam-1.0-501B-A23B) | 1,003 GB | 1,204 GB | 8x B200/B300 (TP8) |\n\n*Minimum GPU memory* is the weights plus 20% headroom for activations,\nCUDA graphs and a working KV cache. It is the total across all GPUs, and\nit is the figure the variant selector above shows.\n\nThe NVFP4 checkpoint quantizes only the routed experts; attention, dense\nMLPs, the router, embeddings and the LM head stay in BF16. On Blackwell it\nruns W4A4; on Hopper, vLLM serves the FP4 experts through Marlin with BF16\nactivations.\n\n## Prerequisites\n\n- **vLLM:** the `vllm/vllm-openai:reflection-beam` Docker image. Beam\n support has not shipped in a vLLM release yet.\n- **Hardware:** NVIDIA GPUs. Beam's kernels, including a custom all-reduce\n used with expert parallelism, are CUDA-only.\n- **Transformers:** the tokenizer uses the Transformers 5 format\n (`TokenizersBackend`).\n\n### Install vLLM\n\n```bash\ndocker pull vllm/vllm-openai:reflection-beam\n```\n\nThe `vllm serve` commands below run inside this image; the command builder\nabove renders the full `docker run` line.\n\n## Launching the server\n\n### FP8 with TP8 on H200 or Blackwell\n\n```bash\nvllm serve reflection/Beam-1.0-501B-A23B-FP8 \\\n --tensor-parallel-size 8 \\\n --moe-backend triton \\\n --kv-cache-dtype fp8 \\\n --enable-auto-tool-choice \\\n --tool-call-parser beam \\\n --reasoning-parser beam\n```\n\nThis is the best-performing FP8 configuration measured on both H200 and\nB300. Set `--moe-backend triton` explicitly; vLLM otherwise selects CUTLASS\nfor the FP8 MoE. vLLM's default attention backends, FlashAttention 3 on\nHopper and FlashInfer with TRT-LLM kernels on Blackwell, are already the\nfastest measured.\n\n### NVFP4 with TP4 on Blackwell\n\n```bash\nvllm serve reflection/Beam-1.0-501B-A23B-NVFP4 \\\n --tensor-parallel-size 4 \\\n --kv-cache-dtype fp8 \\\n --enable-auto-tool-choice \\\n --tool-call-parser beam \\\n --reasoning-parser beam\n```\n\nAt TP4 the weights take about 73 GB per GPU, leaving room for the full\ncontext window many times over; an 8-GPU node can host two replicas. The\nfirst start takes about 10 minutes because FlashInfer autotunes the MoE\nkernel. That is close to vLLM's default 600 s engine-ready timeout, so the\ngenerated command sets `VLLM_ENGINE_READY_TIMEOUT_S=3600`.\n\n### NVFP4 with TP8 on H100\n\n```bash\nvllm serve reflection/Beam-1.0-501B-A23B-NVFP4 \\\n --tensor-parallel-size 8 \\\n --moe-backend marlin \\\n --kv-cache-dtype fp8 \\\n --enable-auto-tool-choice \\\n --tool-call-parser beam \\\n --reasoning-parser beam\n```\n\nSet `--moe-backend marlin` explicitly; it is faster than the backend vLLM\nselects by default on Hopper. The FP8 checkpoint also fits on 8x H100 but\nleaves much less memory for the KV cache.\n\n### BF16 with TP8 on Blackwell\n\n```bash\nvllm serve reflection/Beam-1.0-501B-A23B \\\n --tensor-parallel-size 8 \\\n --enable-auto-tool-choice \\\n --tool-call-parser beam \\\n --reasoning-parser beam\n```\n\n### Tuning\n\n- **KV cache dtype:** the NVFP4 and FP8 commands use\n `--kv-cache-dtype fp8`, which halves KV memory per token (on 8x H200 with\n the FP8 checkpoint, about 14M to 28M tokens). The BF16 command keeps the\n KV cache in BF16.\n- **Parallelism:** tensor parallelism is the recommended single-node layout.\n TEP (`--enable-expert-parallel`) and DEP (`--data-parallel-size 8\n --enable-expert-parallel`) are also supported; DEP is better suited to\n very large batch sizes.\n\n### MegaMoE with attention DP (NVFP4, Blackwell)\n\nFor prefill-heavy, high-concurrency traffic, run attention data-parallel\nwith expert parallelism on FlashInfer's CuTe DSL NVFP4 MegaMoE kernel,\nwhich overlaps the expert all-to-all with the expert GEMMs. Start from the\nNVFP4 command and replace the parallel and MoE settings:\n\n```bash\nvllm serve reflection/Beam-1.0-501B-A23B-NVFP4 \\\n --data-parallel-size 4 \\\n --enable-expert-parallel \\\n --moe-backend flashinfer_moe_ep_cutedsl \\\n --kv-cache-dtype fp8 \\\n --enable-auto-tool-choice \\\n --tool-call-parser beam \\\n --reasoning-parser beam\n```\n\n- Only for DEP (`--data-parallel-size` with `--enable-expert-parallel`).\n It does not support TEP or pure TP.\n- Use `--data-parallel-size 4` on a GB200/GB300 tray or four B200/B300\n GPUs, and `8` across a full 8-GPU node.\n- For low-latency serving, stay on TP4 with `flashinfer_trtllm`.\n\n### DFlash2 speculative decoding\n\nThe DFlash2 drafter\n([`reflection/Beam-1.0-501B-A23B-DFlash2`](https://huggingface.co/reflection/Beam-1.0-501B-A23B-DFlash2))\nshares the target model's embeddings and LM head:\n\n```bash\n--speculative-config '{\"method\":\"dflash\",\"model\":\"reflection/Beam-1.0-501B-A23B-DFlash2\",\"num_speculative_tokens\":7,\"attention_backend\":\"FLASH_ATTN\",\"enable_adaptive_verification\":true}'\n```\n\nWith `enable_adaptive_verification`, vLLM predicts how likely each draft\ntoken is to be accepted and verifies only the ones worth the compute,\nrather than all 7 every step. It makes little difference at low\nconcurrency and raises throughput under load. Remove it to verify the\nfull draft every step.\n\nThe drafter supports up to 15 speculative tokens; 7 is recommended. It\nspeeds up decoding most at low concurrency and adds about 1.7 GB per GPU,\nwhich reduces KV-cache capacity. Acceptance depends on natural-language\ninput, so benchmark it with a real-text dataset rather than\n`--dataset-name random`.\n\n### Output constraints\n\nBy default, the `beam` output grammar constrains every assistant turn from\nits first token (reasoning, then an answer or a tool call). Set\n`VLLM_BEAM_ALWAYS_CONSTRAIN_OUTPUT=0` to disable it.\n\n## Client usage\n\n```bash\ncurl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"reflection/Beam-1.0-501B-A23B-NVFP4\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello.\"}]\n }'\n```\n\nSet `model` to the checkpoint being served: `reflection/Beam-1.0-501B-A23B-NVFP4`,\n`reflection/Beam-1.0-501B-A23B-FP8`, or `reflection/Beam-1.0-501B-A23B` for\nBF16. `/v1/models` lists the served name.\n\n### Reasoning effort\n\nBeam reasons at the maximum effort, `max` (512), unless the request sets\n`reasoning_effort`:\n\n```bash\ncurl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"reflection/Beam-1.0-501B-A23B-NVFP4\",\n \"messages\": [{\"role\": \"user\", \"content\": \"What is 17 * 19?\"}],\n \"reasoning_effort\": \"low\"\n }'\n```\n\nThe Rust frontend (`VLLM_USE_RUST_FRONTEND=1`) serves `/v1/chat/completions`\nand `/v1/completions`, but not yet `/v1/responses` or the Anthropic-compatible\n`/v1/messages` (both return 404). Use the default Python frontend for those\nAPIs.\n\n## References\n\n- [Beam model card](https://huggingface.co/reflection/Beam-1.0-501B-A23B)\n- [Beam FP8](https://huggingface.co/reflection/Beam-1.0-501B-A23B-FP8)\n- [Beam NVFP4](https://huggingface.co/reflection/Beam-1.0-501B-A23B-NVFP4)\n- [DFlash2 drafter](https://huggingface.co/reflection/Beam-1.0-501B-A23B-DFlash2)\n", "source": { "source": "vllm", "repo": "vllm-project/vllm", "ref": "main", "sha": "389ffebe555705c85337025d5df108d0384d365e", "path": "recipes/models/reflection/Beam-1.0-501B-A23B.yaml", "edit_url": "https://github.com/vllm-project/vllm/edit/main/recipes/models/reflection/Beam-1.0-501B-A23B.yaml", "overlays": [] } }