Expanding context length drastically increases the KV cache memory footprint, risking Out-Of-Memory (OOM) failures. NVIDIA GB10 Superchip hardware overcomes bandwidth limits via specific quantization formats:
256GB VRAM: INT4 AutoRound
4-bit integer compression. Delivers ~3x higher throughput than FP16. Maximizes speed but may slightly impact complex reasoning accuracy.
384GB VRAM: NVFP4 (Native GB10 4-bit)
NVIDIA's native 4-bit floating-point format (E2M1). Offers 4x memory reduction like INT4, but retains floating-point semantics for near-FP16 accuracy natively on Blackwell hardware.
512GB VRAM: FP8
Mature, stable production format. Predictably halves VRAM vs FP16 with excellent accuracy retention. The reliable fallback before full NVFP4 ecosystem adoption.
Hardware Density and Throughput Scaling on GB10 Architecture
Required VRAM (GB)
Theoretical Maximum Throughput (TPS)
Analysis of hardware requirements and theoretical throughput for the GLM-5 model on Grace Blackwell (GB10) clusters. Extreme quantization down to 2-bit reduces the cluster requirement to just two nodes, although memory bandwidth severely constrains generation speed compared to smaller, dense models.