Deploy Beam on GPU Cloud: Reflection AI's 501B MoE (2026)

Reflection AI published the full architecture and training story for Beam, a 501-billion-parameter mixture-of-experts model, in an October 2026 announcement, and the specs are detailed enough to plan a deployment around before a single checkpoint reaches Hugging Face. If your plan is to deploy Beam on GPU cloud hardware the moment weights ship, here's the VRAM math, the training story behind the model, and how it actually stacks up against the open-weight models already running in production.

TL;DR: Deploying Beam, Reflection AI's 501B MoE, on GPU Cloud

• What it is: Reflection AI's Beam is a 501B-parameter sparse MoE with 23B active parameters per token, announced October 2026 and licensed Apache 2.0.
• VRAM at FP8: roughly 576 GB (501B x 1 byte + 15% overhead), fitting one 8x H200 on-demand node on Spheron with room to spare.
• VRAM at NVFP4: roughly 288 GB on Blackwell-native tensor cores, small enough for a 2x B200 192GB node once a quantized checkpoint exists.
• Training: Reflection AI pretrained Beam on 6,144 NVIDIA GB300 NVL72 GPUs in under four weeks at 92.3% goodput.
• Availability: as of October 10-14, 2026, Beam's weights are not on Hugging Face, so no deployment step below can be verified end to end yet.

What Beam Actually Is: 501B Total, 23B Active, Apache 2.0

Beam is a sparse mixture-of-experts model with 501 billion total parameters and 23 billion active parameters per token, built across 52 layers with an interleaved local and global attention pattern that Reflection AI reports as roughly a 3:1 ratio of sliding-window to global attention layers.

Context length was extended to 1 million tokens during a dedicated midtraining stage, though the high-compute reinforcement learning phase that followed ran at up to 256K context, a gap worth knowing if you're planning to serve Beam at its full advertised window. Pretraining covered 23.8 trillion tokens of curated web and proprietary licensed data, and Reflection AI released the model under an Apache 2.0 license, meaning no usage restrictions on commercial deployment and no per-token API dependency once you're running it yourself.

The Apache 2.0 choice matters for the same reason every other open-weight release on this blog matters: self-hosting means your prompts and outputs don't leave infrastructure you control, and your cost structure shifts from per-token API billing to a fixed hourly GPU rate you can actually forecast. That trade-off only pays off once you've sized the GPU correctly, which is the harder part of this guide.

Trained Entirely on GB300 NVL72: The Infrastructure Story Behind the Weights

Beam's training run is as newsworthy as the model itself. Reflection AI pretrained Beam on 6,144 NVIDIA GB300 NVL72 GPUs, finishing in under four weeks at 92.3% goodput, a cluster utilization figure that's unusually high for a run at that scale. The reinforcement learning stage that followed scaled up further in GPU count while running shorter: 10.5K GB300 GPUs over four weeks, generating more than 100 million rollouts and roughly 1.3 billion sandbox executions, the kind of agentic RL environment scale that's become standard for frontier coding and reasoning models.

GB300 NVL72 (marketed as Blackwell Ultra) is the direct successor rack to GB200 NVL72, and our GB200 NVL72 rack-scale guide covers the architecture GB300 builds on: 72 GPUs in a single NVLink domain, unified memory across the rack, and the liquid-cooling requirements that come with that density. On Spheron, GB300 NVL72 is listed as a reservation product rather than an hourly on-demand instance, pricing and availability on request, which matches how every cloud provider currently sells this class of hardware: it's sold by commitment, not by the hour, because the supply is still constrained.

That's also why GB300 training numbers and GB300 inference economics are two separate conversations. Reflection AI trained Beam on a rack built for massive synchronous compute across thousands of GPUs. Serving Beam once weights exist is a different problem: inference needs a handful of GPUs with enough combined VRAM to hold the model, not a cluster of thousands.

VRAM Math at FP8 and NVFP4: What It Takes to Run Beam

Beam's VRAM requirement is driven entirely by its 501B total parameter count, not its 23B active parameters. Every expert has to sit in GPU memory at all times so the router can dispatch any token to any expert, which means the formula is the same one that applies to every MoE model: total parameters times bytes per parameter, plus roughly 15% overhead for activations and framework memory, plus a separate KV cache budget.

At FP8, Beam's ~576 GB footprint technically fits on 8x H100 80GB (640 GB total), but that leaves only around 64 GB for KV cache, activations, and request batching, tight enough that it's a smoke-test configuration, not a production one. An 8x H200 141GB node gives roughly 550 GB of headroom beyond the weights, which is the realistic floor for serving concurrent requests at any meaningful context length.

At BF16, the ~1,152 GB requirement exceeds a single 8x H200 node's 1,128 GB total, so BF16 serving needs either a full 8x B200 192GB node or a multi-node setup, the same boundary that shows up for every MoE model in this size class. NVFP4 is the tightest option by far, but it only runs on Blackwell tensor cores: H100 and H200 have no FP4 tensor core support, so NVFP4 inference is a B200, B300, or RTX PRO 6000 story specifically. Our NVFP4 vs MXFP4 quantization guide covers the block-size and scale-format tradeoffs between the two 4-bit formats in more depth, including which toolkits produce production-ready checkpoints.

Here's the arithmetic worked through for the node this guide actually recommends, the same check worth running before committing a budget to any MoE deployment. An 8x H200 141GB node totals 1,128 GB of VRAM. Reserve it conservatively with vLLM's --gpu-memory-utilization 0.9, which caps usable memory at roughly 1,015 GB and leaves the remaining 10% as a buffer against fragmentation and CUDA graph capture. Subtract Beam's ~576 GB FP8 footprint (weights plus the 15% overhead) and about 439 GB remains for KV cache, activations, and request batching, which is the number that actually decides how many concurrent sessions or how long a context window the node can serve. That 439 GB figure is what you'd plug into vLLM's --max-num-seqs and --max-model-len tuning once a checkpoint exists; it's also why the ~64 GB headroom on 8x H100 80GB flagged above counts as a smoke-test configuration rather than a production one, the margin is more than six times thinner.

None of these numbers are usable until Reflection AI ships an FP8 or NVFP4 checkpoint. A BF16-only release would force every deployment through the heavier end of this table on day one.

Lower Active Parameters Don't Mean Lower VRAM: Why Compute Efficiency Isn't Deployment Cost

Beam's 23B active parameters make it cheap to compute against relative to its 501B total size, but compute cost and memory cost are two different line items, and conflating them is the most common mistake in MoE deployment planning. As eWeek's editorial analysis of the Beam release put it, "Lower compute does not establish lower deployment costs." The same piece goes further: "Buyers should compare cost per successfully completed task, latency, reliability, and hosting requirements before assuming compute efficiency will reduce their bills."

That distinction holds structurally, not just as a caveat. Our MoE inference optimization guide walks through why: every expert's weights have to be resident in VRAM because the router can send any token to any expert on any forward pass, so you provision for the full 501B parameters regardless of how few of them fire on a given request. The 23B active parameter count only tells you how fast inference runs once the model is loaded, it says nothing about how many GPUs you need to load it in the first place. A dense 23B model and Beam's 23B-active MoE compute at similar speed, but Beam needs roughly 22x more VRAM (501B total parameters against 23B) to hold its full parameter set.

KV cache is the one place the active-parameter efficiency actually shows up directly: cache size scales with Beam's 23B-active architecture (its 52 layers and attention head configuration), not its 501B total, the same pattern every sparse MoE model follows. That's real savings, just not the kind that changes which GPU tier you're buying.

Beam vs DeepSeek Open-Weight Model: Where It Actually Stacks Up

There's no single answer to which open-weight model is "best" right now, and Beam's own disclosed comparison table makes that explicit: it wins some rows, loses others, and ties on at least one. Here's the benchmark comparison Reflection AI published against GLM-5.2, GLM-5.3, Kimi K3, and DeepSeek V4.1 Flash, as reported by Latent.Space's coverage of the release:

NR means the figure wasn't reported in Beam's disclosed comparison table. The pattern across these four rows: Kimi K3 leads on every benchmark where both models are reported, GLM-5.3 beats Beam on SWE-Bench Pro and GPQA Diamond but wasn't reported on AIME, and DeepSeek V4.1 Flash's Terminal-Bench score is the widest gap on the table at 10.5 points ahead of Beam. GPQA Diamond is close to a wash: 90.5 for Beam against 90.9 for DeepSeek V4.1 Flash.

Our GLM-5.2 deployment guide, GLM-5.3 GPU cloud guide, and Kimi K3 cluster sizing guide each cover the VRAM and vLLM setup for the models Beam is measured against. For the DeepSeek side specifically, our DeepSeek V4-Pro single-node setup guide is the closest existing deployment walkthrough on this site, covering the 1.6T/49B-active sibling architecture's FP8 and FP4 sizing on 8x H200. If you're weighing all of these against each other rather than one at a time, our open-weight frontier model comparison lays out the broader field.

The honest framing is that Beam's pitch isn't benchmark supremacy across the board. It's a 501B open-weight model, Apache 2.0 licensed, trained end to end on hardware most labs haven't finished bringing up yet, and positioned by outlets like eWeek as an American-built alternative to the Chinese open-weight models dominating this comparison. Whether that trade-off is worth it depends on whether your workload needs the specific tasks where Beam is competitive, or the ones where Kimi K3 and DeepSeek V4.1 Flash are ahead.

Step-by-Step: Deploy Beam with vLLM on GPU Cloud

These are the steps that will work the day Reflection AI ships a checkpoint. Everything up to the weights download is something you can do right now; the checkpoint pull is the one step you can't complete yet.

1. Provision a multi-GPU node. On app.spheron.ai, provision an 8x H200 141GB on-demand instance for an FP8 deployment (1,128 GB total VRAM). SSH in and confirm GPU count and interconnect topology with nvidia-smi and nvidia-smi topo -m. The Spheron docs at docs.spheron.ai cover provisioning and networking configuration in more detail.
2. Install vLLM with expert-parallel support. pip install vllm --upgrade gets you a recent build. Confirm --enable-expert-parallel is available before you try to load Beam, since MoE models route through experts rather than a single dense forward pass.
3. Pull the checkpoint once it exists. Check Reflection AI's Hugging Face organization for a Beam release. As covered below, nothing is public as of this writing.
4. Launch the server. vllm serve reflection-ai/beam --tensor-parallel-size 8 --enable-expert-parallel --dtype fp8 --max-model-len 131072 --port 8000. Start at a fraction of the trained 1M-token context and raise it only after confirming KV cache headroom.
5. Validate throughput. Run vLLM's benchmark script against your endpoint and watch nvidia-smi dmon -d 1 across all eight GPUs to confirm expert parallelism is spreading load evenly rather than bottlenecking on a few frequently routed experts.

Weights Status: What's Live Right Now and What's Still a Waitlist

As of October 10-14, 2026, Beam's weights are not public on Hugging Face, and Reflection AI's Hugging Face organization lists zero public models, according to OrcaRouter's analysis of the release. What's live right now is the architecture, the training infrastructure disclosure, and the benchmark comparison table, enough to plan a deployment but not enough to run one end to end.

Reflection AI was founded by former Google DeepMind researchers Misha Laskin and Ioannis Antonoglou, according to Wikipedia's entry on the company, which gives the Beam release a research pedigree that's part of why the open-weight community is watching for the checkpoint. There's no published release date for the weights as of this writing. If you're planning around Beam today, the realistic move is to size your GPU cluster using the VRAM math above and treat the checkpoint pull as the one unknown in an otherwise plannable deployment.

Cost: Spheron Pricing for Beam-Class MoE Inference

Until Beam's weights ship, the practical question is what it costs to have the right hardware ready. Spheron advertises live rates across 5+ providers with no lock-in, covering the GPU tiers Beam's VRAM math points to.

An 8x H200 node covers Beam's ~576 GB FP8 footprint with headroom for KV cache, and is the more mature choice for production serving today. An 8x B200 node covers the BF16 footprint and is the hardware NVFP4 inference will eventually run on once a quantized Beam checkpoint exists. GB300 NVL72, the rack Beam actually trained on, remains a reservation-only product on Spheron, pricing and availability on request, not an hourly rate you can book today.

Pricing fluctuates based on GPU availability. Spheron rates above are live as of 16 Oct 2026; other providers reflect their most recent published rates and may have changed. Check current GPU pricing → for live rates.

Beam's weights aren't out yet, but the hardware it will need already is. Get started on Spheron →