AMD delivers 288GB of HBM3e memory on its Instinct MI350X, a figure that exceeds the 192GB found on the NVIDIA Blackwell B200. This 96GB capacity gap makes the MI350X a specialized tool for large language model inference where model weights must fit entirely in GPU memory. While the B200 reaches 20 petaFLOPS of FP4 AI performance, the MI350X provides 10 PFLOPS of sparse FP4 compute. DataMonsters reports that the MI355X variant carries 1.6 times the memory capacity and double the FP64 throughput of the NVIDIA GB200/B200 platform.
Hyperscalers face massive capital expenditures to secure these chips. Four major US technology companies forecast spending reaching $650 billion in 2026 to build data centers and buy equipment. Amazon plans to spend $200 billion in 2026, while Alphabet plans to spend $185 billion. Meta expects its full-year spending to reach $135 billion. These companies compete for finite supplies of chips coming from Taiwan Semiconductor Manufacturing Co. factories.
| Specification | NVIDIA Blackwell B200 | AMD Instinct MI350X |
|---|---|---|
| Memory Capacity | 192 GB HBM3e | 288 GB HBM3e |
| Memory Bandwidth | 8 TB/s | 8 TB/s |
| Compute (FP4) | 20 PetaFLOPS | 10 PFLOPS |
| TDP | 1000W | 750W |
| Price (Estimated) | $30,000 – $40,000 | $18,000 – $22,000 |
The MI350X targets cost-sensitive training and large model inference. For a 7B model at FP8 precision, the B200 serves 1,800 to 2,400 tokens per second, while the MI350X reaches 3,000 to 5,000 tokens per second. The MI350X provides 35x higher inference performance than the previous MI300 generation. I find the price gap between these two accelerators most telling for procurement teams.
Software frameworks and training performance
NVIDIA maintains a massive lead in training speed through the CUDA ecosystem. A DGX GB200 NVL72 cluster trained GPT-3 175B in 3.1 minutes, a record that no AMD system matched in MLPerf Training v4.1. An 8-GPU MI350X node trained the same GPT-3 175B model in 4.8 minutes. This 55% speed difference favors NVIDIA for frontier model training where time is the primary constraint.
AMD uses the ROCm software stack to compete. ROCm 7.2.4 provides support for PyTorch, TensorFlow, JAX, and MosaicML. For standard LLM inference with PyTorch and vLLM, the MI355X reaches 90% to 95% of H100 throughput. The MI355X also posts server inference results within single-digit percentage points of the B200 in MLPerf Inference 6.0. However, AMD lacks full ROCm equivalents for TensorRT-LLM and FlashAttention 3.
The choice between CUDA and ROCm remains a matter of ecosystem depth. CUDA leads by 20% to 40% on specific libraries like TensorRT-LLM and FlashAttention 3. AMD offers the HIP interface, which allows developers to compile a single source code for both AMD and NVIDIA hardware. Developers using the HIPIFY package can convert CUDA source code to be compatible with AMD. If you rely on NVIDIA NIM containers, you will find the transition to AMD difficult.
The economics of inference at scale
Inference economics favor the MI350X because it handles larger models more efficiently. A single MI350X can hold the entire Llama 3.1 405B model in memory at FP8 precision. The B200 requires at least two GPUs to host the same model. Using a $30,000 GPU to wait for a third-party API response creates capital inefficiency.
The MI350X provides better value for production serving. For Llama 3.3 70B, AMD says the MI350P delivers up to 4.2x more tokens per dollar than the NVIDIA H200 NVL. The B200 serves the GPT-OSS-120B model for $0.02 per million tokens, while the GB300 NVL72 rack costs $0.123 per million tokens.
| Model Type | B200 Inference (Tokens/s) | MI350X Inference (Tokens/s) |
|---|---|---|
| 7B Model (FP8) | 1,800 – 2,400 | 3,000 – 5,000 |
| 70B Model | 22,000 (requires 2 GPUs) | 18,000 (single GPU) |
The training gap can flip in specific scenarios. AMD reported that the MI355X edged out the NVIDIA GB200 platform by roughly 1.12x to 1.13x on Llama-2 70B LoRA fine-tuning runs. This performance makes the MI350X an attractive option for companies running frequent fine-tuning jobs where budget matters more than absolute speed.
I would skip the MI350X if your workload depends on proprietary NVIDIA kernels. The ROCm installation process remains more difficult than the CUDA setup because it requires manual dependency resolution and specific kernel parameter modifications. AMD’s customer support receives criticism in Trustpilot reviews, where users report slow or unhelpful RMA handling. Will hyperscalers continue to accept these support risks to gain memory capacity? Most procurement teams will prioritize the B200 for stability unless the memory requirements of a specific model force a switch to AMD.
