Beam-1.0-501B-A23B GPU Requirements: VRAM & Cheapest GPU
Beam-1.0-501B-A23B has about 501B parameters. See exactly how much GPU memory it needs at FP16, INT8, and INT4, and the cheapest GPU to run it, with live hourly pricing from 5+ data center partners.
To run Beam-1.0-501B-A23B for inference at FP16, you need roughly 1093 GB of VRAM. The cheapest fit on Spheron is 8x H200 141GB from $26.56/hr. Quantize to INT4 to run it on a smaller, cheaper GPU.
Estimated peak VRAM including weights, activations, and KV cache. Add 10% headroom for production traffic.
- 01$4.80/hr$38.40/hr8× H200 141GBCHEAPESTHopper · HBM3e
- 02$12.42/hr$49.68/hr4× B300 288GBBlackwell Ultra · HBM3e
- 03$9.70/hr$77.60/hr8× B200 192GBBlackwell · HBM3e
Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.
VRAM required to run Beam-1.0-501B-A23B
Estimated peak VRAM at context length 4,096 and batch size 1, including weights, activations, and KV cache. Quantizing to INT8 (Q8) or INT4 (Q4) cuts memory roughly in half and in quarter.
| Precision | Inference | LoRA fine-tune | Full fine-tune |
|---|---|---|---|
| FP16 | 1093 GB | 1639 GB | 4371 GB |
| INT8 | 546 GB | 820 GB | 2186 GB |
| INT4 | 273 GB | 410 GB | 1093 GB |
Cheapest GPU to run Beam-1.0-501B-A23B by precision
Full precision. Best quality, highest memory.
8-bit quantized. ~2x smaller, minimal quality loss.
4-bit quantized. ~4x smaller, runs on smaller GPUs.
Inference vs fine-tuning Beam-1.0-501B-A23B
Inference only holds the model weights plus a KV cache, so it is the cheapest setup. LoRA fine-tuning adds a small adapter and roughly 50% more memory. Full fine-tuning holds gradients and optimizer state on top of the weights, which is about 4x the inference footprint, so it often needs multiple GPUs even when inference fits on one. For Beam-1.0-501B-A23B, an on-demand H200 141GB instance covers inference and LoRA, while a full fine-tune needs several times that memory and often spans multiple GPUs. Check the live GPU pricing for current rates.
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Compare GPU requirements for models in the same class.
Beam-1.0-501B-A23B GPU questions
Beam-1.0-501B-A23B has about 501B parameters. At FP16 it needs roughly 1093 GB of VRAM for inference, including weights, activations, and KV cache. Quantized to INT4 that drops to around 273 GB. Leave about 10% headroom for production traffic.
For FP16 inference, the cheapest fit on Spheron is 8x H200 141GB from $26.56/hr. If you quantize to INT4, 4x A100 80GB from $4.76/hr runs it for less. Pricing is aggregated live from 5+ data center partners with per-minute billing.
LoRA fine-tuning adds roughly 50% on top of inference memory, so it usually fits the same class of GPU. Full fine-tuning holds gradients and optimizer state and needs about 4x the inference VRAM, which often means multiple GPUs for Beam-1.0-501B-A23B. The VRAM matrix above shows the exact estimate for each setup.
We read the parameter count directly from the model's safetensors metadata on HuggingFace, then estimate peak VRAM from weights, activations, KV cache, and framework overhead at your chosen precision. The estimate lands within about 15% of real-world use for most transformer models.
Quantized to INT4, Beam-1.0-501B-A23B needs about 273 GB of VRAM, so it is too large for a single 24 GB RTX 4090 and needs a bigger card or multiple GPUs. At FP16 it needs roughly 1093 GB, which usually means a data center GPU. The precision picks above list the cheapest GPU that fits each setup.