MI325X 256GB

The MI325X 256GB is a CDNA 3.5-generation AMD GPU built on the Aqua Vanjaram+ architecture, manufactured on a TSMC 5nm / 6nm process node with 153 billion transistors. Released in 2024, it delivers 1470 TFLOPS of FP16 throughput and 1470 TFLOPS BF16 — the two precision formats most commonly used for transformer model training and inference. FP8 precision is supported at 2940 TFLOPS — roughly 2.0× the FP16 rate — enabling near-doubled throughput for inference workloads that can tolerate reduced numerical precision with calibration. The Aqua Vanjaram+ architecture represents AMD's approach to balancing compute throughput, memory bandwidth, and power efficiency for data center AI workloads. At 750W TDP, the MI325X 256GB sits in the ultra-high-power data center tier (750W), requiring specialized rack infrastructure with high-density power delivery.

Memory capacity is 256GB of HBM3e with 6000 GB/s bandwidth. This determines which models can run without quantization: approximately 128B parameters at FP16 (2 bytes/param), 256B at INT8 (1 byte/param), or up to 512B 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 is approximately 245 FLOP/byte (1470 TFLOPS ÷ 6000 GB/s). Most autoregressive LLM inference falls well below this threshold, making the 6000 GB/s memory bandwidth the binding constraint on tokens-per-second throughput rather than raw TFLOPS.

The MI325X 256GB uses Infinity Fabric / PCIe 5.0 for host connectivity. Without NVLink, VRAM cannot be pooled across multiple cards — the single-card 256GB capacity is the hard ceiling for model size without model sharding over slower PCIe. For workloads that exceed 256GB, the alternative is pipeline parallelism (splitting model layers across cards) rather than tensor parallelism, which introduces inter-card communication overhead at each layer boundary. This makes the MI325X 256GB best suited for workloads that fit within a single card's VRAM budget.

The primary workloads for the MI325X 256GB are Massive context LLM inference, Memory-bound workloads, Large open-source models. Upgraded MI300X with HBM3e and 256GB VRAM — highest memory capacity of any single GPU. Drop-in ROCm upgrade path from MI300X. Key limitations to factor into your evaluation: ROCm software ecosystem lags CUDA maturity; Very limited cloud availability vs NVIDIA equivalents; No FP8 hardware support in current ROCm stack. 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 MI325X 256GB is a 2-year-old architecture that is still widely deployed in cloud data centers. Software compatibility is a key consideration: AMD's ROCm stack supports PyTorch, TensorFlow, and JAX, but the ecosystem is less mature than CUDA. Some libraries (FlashAttention, certain quantization kernels) have limited or no ROCm support, which can affect inference performance and compatibility with popular serving frameworks like vLLM. 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.