What GPU do you need to run Reflection Beam 1.0 501B-A23B?
No single GPU. Reflection Beam 1.0 501B-A23B needs about 303.9 GB at Q4, more than any GPU, system or build we price.
501.27B parameters, 23B active (mixture-of-experts) (Hugging Face, accessed 2026-10-18). Q4_K_M GGUF: 302.41 GB (source). Architecture config (Hugging Face config.json): 9 global attention layers × 8 KV heads × 128 head dim, plus 43 sliding-window layers (512-token window), so about 36 KB of FP16 KV cache per token of context (how we estimate context).
Card: 501B total, 23B active, gated GQA + sliding-window attention, 1M context. Release date is the Hugging Face repo creation date.
Other places to get it
Checked Nov 14, 2026. We list only copies we opened and checked. Check file hashes for copies from other uploaders.
- Ollama: Cloud only (runs on Ollama's servers)
ollama run beam:cloud
No single GPU in our database fits it. Use multiple GPUs (see calculator).
Every device that runs it
Graphics cards, Macs and mini PCs from the wider hardware list behind My rig, smallest usable memory first. Not sure about yours? Check your exact rig.
Show all 1 devices that run it
Speed calculator
estimateEstimate = bandwidth ÷ GB read per token (the model's listed GGUF file: Q4_K_M for most models, otherwise the quant named in the list; scaled by active/total parameters for MoE), scaled by a per-platform efficiency and fixed per-token costs calibrated to 45 measured llama.cpp benchmarks (2025–26). Fit = file size + 1.5 GB headroom ≤ total VRAM (on Macs, the part of unified memory macOS lets the GPU use by default), and room for at least 4K tokens of context with an 8-bit KV cache. For a model with no KV cache data, only the file and headroom are checked, so a fit says nothing about context. Multi-GPU: VRAM adds, speed stays one card's (a layer split runs the cards in turn); max context uses the total. Speed is for short context. Max context = what is left after the file and a 1 GB runtime reserve ÷ KV cache bytes per token, default FP16 and 8-bit (q8_0) cache. How speed works · how context works