--- title: "Reflection Beam Hardware Requirements: GPU, Memory, and What to Plan For (2026)" slug: reflection-beam-hardware-requirements-gpu-memory-2026 description: "Reflection's Beam is a 501B-parameter open-weight model with Apache 2.0 weights due in October. The memory math at each precision, which GPU configs fit it, and what to have ready." author: "Yotta Labs" date: 2026-10-13 categories: ["Inference"] canonical: https://www.yottalabs.ai/post/reflection-beam-hardware-requirements-gpu-memory-2026 --- # Reflection Beam Hardware Requirements: GPU, Memory, and What to Plan For (2026) ![](https://cdn.sanity.io/images/wy75wyma/production/4ce81f5cfc11d6d050024c26fc2b8db4d0d6779b-1200x627.png) *Beam is announced, the weights are due later this month, and the license is already known. Here's the memory math you can do today and the hardware to line up before the download exists.* Reflection AI announced Beam on October 5, 2026: a 501 billion-parameter open-weight model from a US lab, aimed at coding, reasoning, and agent workloads. Reflection says the weights will be released later in October under Apache 2.0, along with a model card and a technical report. Until then the model is in early access through a waitlist. The checkpoint isn't public, so nobody can give you a verified GPU count yet. What you can do is the arithmetic. Parameter count times bits per parameter gives you the size of the weights, and the size of the weights decides the hardware. This post walks through that math, maps it to real GPU configs, and flags what is still unknown. It will be updated on this URL when the weights are out. ## The numbers that matter Two things stand out. The license is settled before the weights arrive, which is unusual and makes planning easier: Apache 2.0 means you can host it, fine-tune it, and ship it commercially. And the three "not published" rows are what this whole post hinges on. Until Reflection says what precision the checkpoint comes in, every hardware figure below is an estimate from arithmetic, and it's labeled that way. One more note. The announcement talks about thousands of NVIDIA GB300 GPUs. Those numbers describe how Beam was trained, not what it takes to serve it. ## Why 23B active doesn't make it small Beam is a Mixture-of-Experts model, which means only 23B of its 501B parameters fire for any given token. That keeps the compute cost per token low, and it's the basis for Reflection's efficiency claim. It doesn't help you with memory. Every expert has to sit in GPU memory and be ready, because the router picks a different set for each token. Low active parameters save compute, not VRAM. The same rule shaped every large open release this year. [Kimi K3](https://www.yottalabs.ai/post/kimi-k3-hardware-requirements-gpu-memory-2026) has 104B active parameters and still needs 1.56 TB for its weights. [DeepSeek V4.1 Flash](https://www.yottalabs.ai/post/deepseek-v4-1-flash-hardware-requirements-gpu-memory-2026) runs far fewer active parameters than its total and still has about 510 GB of weights. Beam will follow the same pattern. ## The memory math, by precision Here is what 501 billion parameters weigh at each precision a lab might release. The "plan for" column adds about 20% on top of the weights for activations, runtime overhead, and cache, which is the margin the vLLM recipe for DeepSeek V4.1 Flash used (510 GB of weights, about 614 GB to serve). For a sense of scale, an FP8 Beam checkpoint would be close to the size of DeepSeek V4.1 Flash and about two thirds the size of [GLM 5.3](https://www.yottalabs.ai/post/glm-5-3-hardware-requirements-gpu-memory-2026), which is about 744 GB in FP8. It's roughly half the size of [Mistral Large 4](https://www.yottalabs.ai/post/mistral-large-4-hardware-requirements-gpu-memory-2026) at the same precision. Reflection hasn't released an open model of this size before, so there is no earlier release to read the checkpoint format from. Plan for FP8 as the likely middle case and treat 4-bit as the best case. ## Which GPU configurations fit Mapping those estimates onto the configs people rent. "Fits" means the planning figure from the table above fits in total GPU memory. None of these are tested configurations, because there is nothing to test yet. The practical reading: Beam is a single-node model at every precision. If Reflection releases an FP8 checkpoint, an 8x H100 node clears it on paper and an 8x H200 node or a 4-GPU Blackwell config gives you working room. If there's a 4-bit checkpoint, four H200s or two B200s are enough. Full BF16 weights, for fine-tuning or for making your own quantization, need an 8-GPU Blackwell node. That is a lighter requirement than most of this year's flagship open models, and it's the practical upside of a 501B design. There is no workstation path. A 32 GB RTX 5090 holds about 13% of the smallest estimate. ## What's still unknown Four things decide the final answer, and Reflection hasn't published any of them yet. **Checkpoint precision.** Covered above. It moves the requirement by 4x. **Serving engines.** Reflection says Beam will launch with integrations across open-source libraries, but hasn't named them. Expect the major engines to have support near release, and check the release notes before you build an image. Our [vLLM vs SGLang comparison](https://www.yottalabs.ai/post/vllm-vs-sglang-which-inference-engine-should-you-use-in-2026) covers how to choose between the two for a model this size. **Long-context memory cost.** Reflection describes an effective context of 1M tokens. How much memory a long context costs depends on the architecture, and it varies a lot between models. Until there are numbers, leave headroom. **The exact release date.** "Later this month" is all Reflection has said. The model is still in final red-teaming and evaluation. ## What Reflection has reported so far All of these are Reflection's own numbers, and none has independent replication yet. Reflection positions Beam as comparable to GLM 5.2 on advanced reasoning while using 3 to 4 times less inference compute, and says plainly that Kimi K3 remains ahead on raw capability. The compute figure is an estimate based on active parameters and generated tokens. It leaves out prompt processing and serving overhead, so read it as a direction and measure it yourself once the weights are out. ## What to have ready before the weights are out **Disk.** Plan for at least twice the checkpoint size on fast local storage. For an FP8 checkpoint that's about 1 TB, and for BF16 it's about 2 TB. **A config you can get quickly.** Know which config you'd rent, and where, before release day. On Yotta, [H200 and B300 GPUs by the hour](https://www.yottalabs.ai/pricing) cover every precision in the table above. **An evaluation set.** Build this now. If you get early access, run your own prompts through it and you'll know whether the weights are worth a node before they exist. **A baseline.** Decide what Beam has to beat. For most teams that's an open model they already run or call through an API. The [best open-source LLMs roundup](https://www.yottalabs.ai/post/best-open-source-llms-2026) covers the field, and the [Mistral Large 4 comparison](https://www.yottalabs.ai/post/mistral-large-4-vs-deepseek-v4-pro-vs-glm-5-3-vs-kimi-k3-2026) covers the largest open-weight models side by side. ## The route that works today Until the weights are released, early access is the only way to use Beam, and Reflection hasn't published API pricing. If you need an open model in production this week, the usual split holds. Call an API for flagship traffic and self-host the smaller open models where flat cost wins. [GPT-OSS 120B](https://www.yottalabs.ai/post/gpt-oss-120b-hardware-requirements-gpu-memory-2026) and [Gemma 4](https://www.yottalabs.ai/post/gemma-4-hardware-requirements-gpu-memory-2026) are the US open-weight models that fit on far less hardware, and [Yotta AI Gateway](https://www.yottalabs.ai/ai-gateway) puts open models like GLM 5.3, DeepSeek V4 Pro, and Kimi K3 behind one OpenAI-compatible key. ## Frequently asked questions **How big is Reflection Beam?** 501 billion total parameters with 23 billion active per token, per Reflection. The checkpoint size isn't published. By arithmetic it's about 250 GB at 4-bit, 501 GB at FP8, and 1 TB at BF16. **How much VRAM does Beam need?** It depends on the precision Reflection releases. Estimated planning figures are about 300 GB of GPU memory for a 4-bit checkpoint, 600 GB for FP8, and 1.2 TB for BF16. These are estimates until the weights are released. **Can I run Beam on a single GPU?** Not comfortably. The smallest estimate is about 250 GB of weights and about 300 GB with overhead, which is at or past what the largest single GPUs hold. Plan on at least two. **Can I run Beam on 8x H100?** On paper, yes for 4-bit and FP8. Eight H100s hold 640 GB against an estimated 600 GB for FP8, which leaves little room for long context. Full BF16 weights would not fit. **Can I run Beam on 8x H200?** Yes for 4-bit and FP8, with room to spare: 1,128 GB of GPU memory against about 600 GB needed for FP8. BF16 would be at the floor, because the weights alone are about 1 TB. **When are the Beam weights released?** Reflection says later in October 2026. No exact date or download location has been given. **Is Reflection Beam open source?** Reflection says it will release the weights under Apache 2.0, with a model card, a technical report, and the tooling to run, evaluate, and fine-tune it. **Is Beam multimodal?** No. Reflection states that Beam is text-only. **Can I run Beam with Ollama or llama.cpp?** Not today, since there are no weights to convert. Even after release, a model this size needs hundreds of gigabytes of memory at any usable quantization, so it won't be a laptop model. **How does Beam compare to Mistral Large 4 on hardware?** Beam is about half the size: 501B parameters against 1.05T. At the same precision it needs about half the memory, which keeps it on a single node where Mistral Large 4 at FP8 needs an 8-GPU Blackwell node. ## Bottom line Beam is a 501 billion-parameter model, and the weights will need somewhere between about 300 GB and 1.2 TB of GPU memory depending on the precision Reflection releases. At FP8, plan on an 8x H200 node or a 4-GPU Blackwell config. At 4-bit, four H200s or two B200s are enough. Full precision needs an 8-GPU Blackwell node. The checkpoint format and engine support are the two things to watch between now and the end of October. The license is already settled at Apache 2.0. This post will be updated with the verified numbers when the weights are out. In the meantime, build your evaluation set and have [the GPUs](https://www.yottalabs.ai/pricing) picked out for release day.