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Beam is Reflection AI’s 501-billion-parameter sparse mixture-of-experts (MoE) model, but its launch announcement does not provide enough information to specify a supported GPU count or an exact deployment recipe. Its 23 billion active parameters describe computation, not a smaller checkpoint: the model’s total weights still need to be stored. As a rough arithmetic floor, those weights alone would take about 501 GB at one byte per parameter or 1,002 GB at two bytes per parameter, before runtime memory and serving overhead.
Which 501B model is this, and what is known?
The title refers to Beam, announced by Reflection AI on October 5, 2026. Reflection describes it as a sparse MoE model with 501 billion total parameters and 23 billion active parameters, built for coding, reasoning, and agentic workloads. Those figures are the company’s description, not independent evaluation results. Read Reflection AI’s announcement.
Beam is not DeepSeek-V3. DeepSeek-V3 is a separate model with 671 billion total parameters and 37 billion active parameters, according to DeepSeek AI’s repository. Its deployment examples can illustrate the demands of serving a large MoE model, but they are not Beam requirements.
At announcement, Reflection said Beam was in final red-teaming, with weights, a technical report, model card, and developer artifacts expected later in October 2026. The announcement did not specify Beam’s checkpoint formats or size, supported inference runtimes, minimum hardware, context settings, or serving commands. Until Reflection publishes those details, an exact supported configuration cannot be stated.
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How much memory do the weights alone require?
A simple estimate multiplies total parameters by bytes per parameter. Using Beam’s announced 501 billion total parameters, the arithmetic floors are:
| Assumed weight storage | Arithmetic weight-size floor | What the figure means |
|---|---|---|
| 1 byte per parameter | About 501 GB | Weight data only; not a Beam-supported precision or checkpoint size. |
| 2 bytes per parameter | About 1,002 GB | Weight data only; not a Beam-supported precision or checkpoint size. |
These are decimal arithmetic estimates, not Reflection-recommended hardware specifications. The actual checkpoint may use a different representation, and its size cannot be confirmed until the weights and their format are available. Metadata and runtime allocations also add to the amount of memory needed.
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Why 23B active parameters do not mean a 23B-sized model
In an MoE model, only a subset of experts is active for a given token. That can reduce the computation required for each token compared with activating every parameter, but it does not establish that the other expert weights can be left out of the model checkpoint. Beam’s 23B active-parameter figure is not evidence of a smaller 23B-weight deployment.
What else must fit beyond the weights?
Usable accelerator memory must accommodate more than model weights. Runtime workspaces, activations, the key-value (KV) cache, and serving overhead consume capacity too. KV-cache demand depends in part on context length and serving workload; batch size also affects the memory budget. Beam’s announcement does not publish values for these factors, so the weight arithmetic alone cannot determine a working GPU configuration.
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For a multi-accelerator setup, capacity is only one part of the decision. The serving engine must support Beam’s architecture, and the system’s accelerator interconnect, networking, and software stack must meet the engine’s requirements. Beam-specific compatibility and minimums remain unannounced in Reflection’s launch post.
What other large-model deployments can—and cannot—tell you
Published DeepSeek-V3 configurations show why deployment numbers must be tied to a particular model, precision, and serving stack. The following are examples for DeepSeek-V3, not estimates or requirements for Beam.
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| Source and example | Reported configuration | Scope |
|---|---|---|
| NVIDIA TensorRT-LLM DeepSeek-V3 guide | About 671 GB of GPU memory for FP8 weights, with additional memory needed for activations and KV cache; example minimums include 16 H100 80GB GPUs for its FP8 configuration and 8 H100 80GB GPUs for W4A8. | DeepSeek-V3/R1 and the configurations documented by NVIDIA, not Beam. |
| vLLM DeepSeek-V3 recipes | 8 H200 or 8 MI300X/MI325X/MI355X GPUs for its FP8 recipe; 4 B200 GPUs for an FP4 example. | DeepSeek-V3 examples in the vLLM recipes, not Beam. |
DeepSeek AI’s repository also documents a two-node demo command using eight processes per node and lists inference options including SGLang, LMDeploy, TensorRT-LLM, vLLM, and LightLLM, plus AMD GPU support through SGLang and Huawei Ascend support. Those are DeepSeek-V3 repository details; they do not establish that Beam works with those frameworks or devices.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to plan a Beam deployment when its files are available
- Start with Reflection’s official artifacts. Get Beam’s released weights, model card, technical report, and developer documentation. Confirm that the files are official and check any stated release or compatibility notes.
- Verify the checkpoint format and size. Use the actual downloaded checkpoint size and documented precision or quantization format rather than treating the 501 GB or 1,002 GB arithmetic floor as a deployment target.
- Compare the checkpoint with usable accelerator memory. Reserve additional capacity for runtime workspaces, activations, KV cache, batch size, context length, and serving overhead. Follow Beam-specific recommendations if Reflection publishes them.
- Confirm framework and hardware support. Check that the documented inference engine supports Beam’s architecture and the selected accelerator models. For multiple devices or nodes, verify the required interconnect, networking, drivers, and software versions.
- Use Beam’s documented serving procedure. Follow the released commands and settings, then validate that the model loads and serves under the intended context length and workload. Do not substitute a DeepSeek-V3 recipe merely because both models are large MoEs.
What Reflection has reported about Beam’s development
Reflection reports that Beam was pretrained on 23.8 trillion tokens. The company also says its reinforcement-learning run involved more than 100 million rollouts on 10.5K NVIDIA GB300 GPUs over four weeks. These are reported training figures, not inference requirements or independent audits of model quality.
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