B300 SXM6

The B300 SXM6 is a Blackwell Ultra-generation NVIDIA GPU built on the GB300 architecture, manufactured on a TSMC 4NP process node with 208 billion transistors. Released in 2025, it delivers 2800 TFLOPS of FP16 throughput and 2800 TFLOPS BF16 — the two precision formats most commonly used for transformer model training and inference. FP8 precision is supported at 5600 TFLOPS — roughly 2.0× the FP16 rate — enabling near-doubled throughput for inference workloads that can tolerate reduced numerical precision with calibration. The GB300 architecture represents NVIDIA's approach to balancing compute throughput, memory bandwidth, and power efficiency for data center AI workloads. At 1000W TDP, the B300 SXM6 sits in the ultra-high-power data center tier (1000W), requiring specialized rack infrastructure with high-density power delivery.

Memory capacity is 288GB of HBM3e with 8000 GB/s bandwidth. This determines which models can run without quantization: approximately 144B parameters at FP16 (2 bytes/param), 288B at INT8 (1 byte/param), or up to 576B parameters at INT4/GGUF quantization (0.5 bytes/param). These figures are theoretical maximums — actual capacity is reduced by KV cache, framework overhead, and activation memory, typically by 10–20% for inference and 30–40% for training. The arithmetic intensity ceiling — the ratio of compute (2800 TFLOPS) to memory bandwidth (8000 GB/s) — is approximately 350 FLOP/byte. Transformer inference is typically memory-bound below this threshold, meaning the 8000 GB/s bandwidth figure is the primary determinant of tokens-per-second for most LLM serving workloads.

Multi-GPU configurations are a first-class use case for the B300 SXM6. NVLink 5.0 / PCIe 6.0 provides 1800 GB/s of bidirectional NVLink bandwidth between cards, enabling tensor-parallel inference across multiple GPUs with near-linear VRAM scaling. A two-card configuration provides 576GB of pooled VRAM — sufficient for 288B parameter models at FP16 — while a four-card setup reaches 1152GB. NVLink's low-latency, high-bandwidth fabric makes all-reduce operations in data-parallel training significantly faster than PCIe-based alternatives, which top out at ~64 GB/s bidirectional for PCIe 5.0 x16.

The primary workloads for the B300 SXM6 are Frontier LLM training, Massive multi-GPU clusters, Trillion-parameter models. Blackwell Ultra datacenter GPU. 288GB HBM3e with 8 TB/s bandwidth. Successor to B200 with higher memory and compute. Key limitations to factor into your evaluation: Very limited cloud availability — still ramping supply; Extremely high cost — enterprise/hyperscaler pricing only; Requires NVLink 5.0 infrastructure for multi-GPU setups. When comparing this GPU against alternatives at similar price points, the most important metrics are memory bandwidth (for inference throughput), VRAM capacity (for model size), and FP16/BF16 TFLOPS (for training speed). Raw TFLOPS figures can be misleading for inference — a GPU with lower TFLOPS but higher memory bandwidth will often outperform a higher-TFLOPS card on tokens-per-second for memory-bound autoregressive generation.

In the broader GPU market, the B300 SXM6 is a one-year-old design that remains competitive for most workloads. CUDA compatibility is a significant advantage: the B300 SXM6 benefits from the full NVIDIA software ecosystem including cuDNN, TensorRT, FlashAttention, and all major inference frameworks (vLLM, TGI, TensorRT-LLM). CUDA's maturity means optimized kernels are available for virtually every model architecture. For cloud rental, availability varies significantly by provider — some specialize in this GPU tier while others may have limited stock. Compare on-demand and spot pricing across providers using the rental comparison table on this page, and factor in region availability if latency is a concern for your inference workload.