Reflection AI’s Beam has 501 billion total parameters and 23 billion active parameters, but its October 5, 2026 announcement did not publish an inference configuration. That means there is no verified GPU count, memory target, runtime, or installation command to give yet. A rough weight-storage calculation is possible; an exact Beam deployment plan must wait for the company’s promised weights and technical documentation.
Which 501B model is this?
The model in this guide is Beam, announced by Reflection AI on October 5, 2026. Reflection describes it as a sparse mixture-of-experts (MoE) model for coding, reasoning, and agentic workloads, with 501 billion total parameters and 23 billion active parameters. Those are the company’s figures, not independently audited specifications. Reflection AI’s Beam announcement
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“Active” describes the parameters used for a token’s computation; it does not mean the rest of the model weights can be left out of the checkpoint. The 23B active count therefore should not be used to estimate that the model can be stored like an ordinary 23B-parameter model.
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Beam is not DeepSeek-V3. DeepSeek-V3 is a separate MoE model with 671B total parameters and 37B active parameters, according to its publisher. Its deployment examples can illustrate the scale of large-model serving, but they are not Beam hardware requirements. DeepSeek-V3 official repository
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How much memory might Beam’s weights require?
A simple parameter-count calculation gives a lower-bound estimate for storing weights. At one byte per parameter, 501 billion parameters correspond to about 501 GB; at two bytes per parameter, about 1,002 GB (roughly 1 TB). These are arithmetic estimates based on Reflection’s announced parameter count, not measured checkpoint sizes or vendor-recommended configurations. The actual file size depends on the released weight format and any quantization or metadata.
Weight storage is not the same as the total memory needed to serve a model. A deployment also needs room for runtime workspaces, activations, and the key-value (KV) cache used to retain context. KV-cache demand depends on serving details such as context length and batch size. Beam’s launch announcement did not specify these settings or a memory budget, so the estimates above cannot be turned into a reliable GPU count.
Why published GPU counts for other models are not Beam recommendations
Other vendors’ guides show why a parameter count alone does not determine a practical server configuration. NVIDIA’s TensorRT-LLM documentation says DeepSeek-V3 requires about 671 GB of GPU memory for FP8 weights, with additional memory needed for activations and KV cache. Its examples list 16 H100 80GB GPUs for an FP8 configuration and 8 H100 80GB GPUs for W4A8. These figures apply to DeepSeek-V3/R1, not Beam. NVIDIA TensorRT-LLM documentation
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Similarly, the vLLM recipe page lists 8 H200 or 8 MI300X/MI325X/MI355X GPUs for its DeepSeek-V3 FP8 recipe, and 4 B200 GPUs for a DeepSeek-V3 FP4 example. Those are model- and recipe-specific examples, not a shortcut for sizing Beam. vLLM DeepSeek-V3 recipe
DeepSeek-V3’s repository also documents a distributed demo using two nodes with eight processes per node, plus support information for several inference frameworks and accelerator platforms. That demonstrates one publisher’s deployment approach for its own model; it does not establish that Beam supports the same frameworks, hardware, or multi-node setup. DeepSeek-V3 official repository
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What is known about Beam’s release and deployment?
At launch, Reflection said Beam was in final red-teaming and that weights, a technical report, a model card, and developer artifacts would follow later in October 2026. The announcement did not establish checkpoint formats or sizes, supported inference frameworks, recommended GPUs, context settings, networking requirements, or a minimum configuration. Check the official release materials for those specifics before assembling or renting hardware. Reflection AI’s Beam announcement
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Reflection also reported that Beam was pretrained on 23.8 trillion tokens and that its training included more than 100 million reinforcement-learning rollouts on 10.5K NVIDIA GB300 GPUs over four weeks. These are company-reported training figures, not inference requirements; the GB300 count does not indicate how many GPUs are needed to run Beam.
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- Inspect the official model card and weights. Confirm the released checkpoint’s actual size, file format, supported precision, and any quantized variants rather than relying only on the 501B parameter count.
- Match the checkpoint to usable accelerator memory. Compare the documented weight footprint with available GPU memory, leaving additional capacity for runtime allocations, activations, and KV cache.
- Choose serving settings deliberately. Check the supported context length and estimate cache needs for the intended context and batch size; a configuration that loads weights may still be unsuitable for the desired workload.
- Validate the serving stack and hardware topology. Confirm that a documented inference engine supports Beam and that its software versions, multi-GPU interconnect, and any multi-node networking meet the model’s requirements.
- Test the intended workload before scaling. Use the vendor’s recommended configuration and serving commands, then verify memory use and behavior at the context length and concurrency you actually need.
Until Reflection publishes those artifacts, a 501 GB or 1,002 GB weight calculation is useful only as an order-of-magnitude floor. It is not a complete bill of materials or a guarantee that a particular group of accelerators can serve Beam.
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