AMD Instinct MI350X vs. MI355X: Key Differences, Specs, and TCO Analysis

The fundamental difference between the AMD Instinct MI350X and MI355X comes down to power consumption, thermal management, and clock speeds. Both GPUs share AMD's 3nm CDNA 4 architecture, an identical 288GB HBM3e memory pool, and 8.0 TB/s of memory bandwidth.

The AMD Instinct MI350X Accelerator runs at a 1,000W TDP, built for traditional air-cooled enterprise data centers. The MI355X scales up to 1,400W TDP and requires direct-to-chip liquid cooling, but in exchange delivers up to 10% higher peak compute performance (~5.03 PFLOPS vs. ~4.61 PFLOPS dense FP8).

Both the MI350X and MI355X represent AMD's competitive push against the NVIDIA Grace Blackwell Superchip (GB200). Transitioning from CDNA 3 to CDNA 4 on TSMC's 3nm process node brings significant architectural enhancements aimed at large language models (LLMs) and generative AI workloads:

• Native Low-Precision Formats: Full hardware support for FP4 and FP6, doubling matrix math efficiency over legacy FP8 and FP16 formats.
• Unified Memory Footprint: 288GB of ultra-fast HBM3e memory across 8 stacks, eliminating memory bottleneck constraints during multi-billion parameter model execution.
• Infinity Fabric Interconnect: Next-generation scale-out bandwidth operating at up to 1,075 GB/s bi-directional links per GPU.

Rather than forcing a single thermal profile across all data center layouts, AMD engineered two physical variants to accommodate different data center enterprise solutions and cooling limits.

When reviewing the hardware matrix, the shared memory subsystem ensures model capacity parity, while the power envelope dictates maximum compute throughput.

Read across any row other than power and cooling and the two parts are effectively twins: same process node, same memory capacity, same bandwidth, same fabric. The 400W of extra headroom on the MI355X is the only lever AMD pulls to separate the two SKUs.

1. Compute Density vs. Power Headroom

The 400W additional power allowance in the MI355X gives AMD the headroom to drive higher base and boost clocks across its Compute Units. In training clusters running distributed FP8 or FP4 matrix multiplications, the MI355X reduces wall-clock execution time by roughly 8% to 10%.

For memory-bound inference, however, the real-world gap narrows significantly. Before deciding on multi-GPU deployments, you can use our GPU Calculator for LLM Training to calculate exact VRAM requirements. Because both GPUs share the same 8.0 TB/s memory bus and 288GB footprint, key-value (KV) caching capacity and batch size limits remain identical.

2. Air Cooling vs. Direct Liquid Cooling (DLC)

MI350X (1,000W): Engineered to integrate into legacy enterprise racks. It allows organizations to deploy state-of-the-art CDNA 4 hardware without re-engineering facility plumbing or investing in expensive Coolant Distribution Units (CDUs).

MI355X (1,400W): Designed exclusively for modern high-density environments. At 1.4 kW per socket, standard air cooling is physically unviable; deployment requires direct-to-chip liquid loops, manifold connections, and dedicated heat exchange systems.

Evaluating the AMD MI350X and MI355X AI GPUs price structure requires looking beyond raw OEM silicon costs to total operational expenditure (TCO). On the software stack side, both GPUs leverage ROCm vs CUDA compatibility to ensure seamless software deployment.

Hardware & Retrofit Costs

While contract pricing for enterprise 8-GPU node systems varies by vendor (e.g., Dell, HPE, Supermicro), total deployment cost differs substantially:

• MI350X Systems: Lower overall deployment cost. Standard chassis designs lower upfront rack integration expenses and eliminate liquid loop maintenance routines.
• MI355X Systems: Higher capital expenditure (CapEx). Facilities must budget for fluid management, CDU infrastructure, leak detection monitoring, and higher power delivery per rack.

Cloud On-Demand Rates

On major hyperscaler and cloud GPU networks, instance pricing reflects this infrastructure tax. For more guidance on hardware procurement, consult our GPU Buying Guide:

• MI350X Instances: Typically range from $3.50 to $5.50 per GPU/hour, offering a cost-effective sweet spot for fine-tuning, mid-tier training, and standard model serving.
• MI355X Instances: Command premium rates ranging from $5.00 to $8.00+ per GPU/hour, targeted at enterprise workloads where time-to-convergence takes absolute priority.

Select the AMD Instinct MI350X if: You operate within traditional air-cooled facilities, seek to minimize rack retrofit CapEx, or primarily host large-model inference workloads that rely heavily on memory bandwidth rather than peak TFLOPS. Check the full AMD GPU Enterprise Catalog for specific accelerator models.

Select the AMD Instinct MI355X if: Your data center features direct liquid cooling infrastructure, and your goal is maximizing FLOPS-per-square-foot for massive LLM training runs.

Both accelerators are the same silicon wearing two different thermal budgets, so the decision is really an infrastructure decision. If you are still sizing the cluster, read how many GPUs you actually need for LLM training first, then request a quote and we will price the configuration your facility can actually cool.