MI355X vs B200: memory, software and cost compared

The MI355X vs B200 choice comes down to memory against software. AMD's Instinct MI355X has 288 GB of HBM3e per GPU; NVIDIA's B200 has 180 GB of HBM3e. Both are listed at 8 TB/s of memory bandwidth, and their vendor-published peak compute figures are about 10% apart. What separates them is capacity per GPU, the software stack (ROCm or CUDA), the configurations we rent and the monthly price.

MI355X vs B200: the short answer

Choose the MI355X when a model, its context or its batch needs more than 180 GB per GPU, and your code runs on frameworks that support ROCm, such as PyTorch and vLLM. Choose the B200 when you depend on CUDA-only components, when one GPU is enough, or when you want NVIDIA's tooling for NVFP4 models.

The price gap is large. We rent the MI355X as an 8-GPU server at $19,900 a month, about $3.41 per GPU-hour. The HGX B200 server is $34,900 a month, about $5.98 per GPU-hour. We also rent a single B200 at $4,590 a month; the MI355X comes only as a full 8-GPU server.

Memory: 288 GB vs 180 GB and what fits

Weights take roughly parameters × bytes per parameter: 2 bytes in BF16, 1 in FP8, 0.5 in FP4. Plan another 10–30% for KV cache, activations and runtime overhead. The table counts weights only, so read each figure as a floor.

Tensor-parallel groups usually come in 2, 4 or 8 GPUs, so three B200s in theory often means four in practice. The pattern holds either way: the MI355X needs fewer GPUs for the same model and leaves more memory spare on each one.

That spare memory is serving capacity. A 70B-class model with grouped-query attention (80 layers and 8 KV heads of dimension 128, as in Llama 3 70B) stores about 0.33 MB of KV cache per token in 16-bit, so one 8k-token conversation takes about 2.7 GB. With BF16 weights loaded, the B200's 40 GB of headroom holds roughly 15 such conversations and the MI355X's 148 GB roughly 55, before overhead. A single 128k-token context needs about 43 GB, more than the B200 has left. Storing the KV cache in FP8 halves the per-token cost on both GPUs.

Compute and precision: MXFP4 and MXFP6 vs NVFP4

AMD lists the MI355X at 5.0 PFLOPS of dense FP8 and 2.5 PFLOPS of dense BF16. NVIDIA lists the B200 at 4.5 and 2.25 PFLOPS for the same formats. Both vendors roughly double those figures with structured sparsity, which only applies to models pruned to that pattern. On paper the MI355X leads by about 10%. Peak figures are not delivered throughput, though: kernels, libraries and communication decide what you get, and that is where software maturity shows.

The 4-bit formats differ more than the headline numbers suggest. AMD's CDNA 4 adds hardware support for MXFP6 and MXFP4, the microscaling formats standardized through the Open Compute Project, and AMD lists 10.1 PFLOPS for MXFP4. Blackwell supports the MX formats too, and adds NVFP4, NVIDIA's own 4-bit format: it scales values in blocks of 16 with an FP8 scale factor, where MXFP4 uses blocks of 32 with a power-of-two scale. NVIDIA says the finer scaling keeps 4-bit models closer to their original accuracy. NVIDIA lists 9 PFLOPS of dense FP4 for the B200, 18 with sparsity.

In practice, if you plan to serve published NVFP4 checkpoints with NVIDIA's tooling, the B200 is the direct path. For FP8, BF16 or MXFP4 models, both GPUs support the format in hardware, and support in your serving framework is what to check. Our guide to FP8 and FP4 precision formats covers the accuracy trade-offs.

Bandwidth, interconnect and multi-GPU scaling

Memory bandwidth is a tie: both vendors list 8 TB/s. Token generation is usually limited by how quickly weights and KV cache stream from memory, so on a model that fits both GPUs, bandwidth does not separate them. Capacity does.

Between GPUs, the designs differ. In the HGX B200, each GPU has 1.8 TB/s of NVLink 5 bandwidth through NVLink switches, so any pair of GPUs communicates at full NVLink speed. The MI355X platform connects each GPU directly to the other seven over Infinity Fabric: AMD lists seven links of 153.6 GB/s each, about 1.08 TB/s per GPU in total. Collectives such as all-reduce spread traffic over the whole mesh, while a transfer between two specific GPUs runs over the one link that joins them.

For communication-heavy training with wide tensor parallelism, the B200's switched NVLink has more headroom. For inference, the MI355X often sidesteps the issue: a model that needs four B200s may fit on two MI355X GPUs, so there is less traffic to begin with. Across servers, both have eight 400G network ports, one per GPU: ConnectX-7 on the HGX B200, RoCE on the MI355X. Private networking between your servers is available on request.

Cost per month and value

An 8× MI355X server costs $19,900 a month and an HGX B200 costs $34,900. The difference is $15,000 a month, or $180,000 over a year. Per gigabyte of GPU memory, that is about $8.64 a month on the MI355X against $24.24 on the B200: nearly three times as much memory per dollar.

The B200's advantage is granularity. If one GPU covers your workload, a single B200 at $4,590 a month is the only option of the two. Four single B200s cost $18,360, close to one MI355X server, but they are four separate virtual machines with no NVLink between them: good for independent jobs, not for one model split across GPUs.

If you need 288 GB per GPU and must stay on CUDA, the NVIDIA B300 also carries 288 GB of HBM3e; the HGX B300 server is $39,900 a month. To compare totals over your own term, use the GPU cost calculator.

Software: ROCm vs CUDA in practice

This is the part to test rather than assume. PyTorch and vLLM both support ROCm, and PyTorch's ROCm builds keep the familiar torch.cuda device interface, so most model code runs unchanged. Collectives run on RCCL, AMD's counterpart to NCCL. A stack built on these pieces usually moves to the MI355X through installation and configuration, not rewrites.

The work is at the edges. Custom CUDA kernels must be ported to HIP, AMD's CUDA-like programming interface; AMD's HIPIFY tools translate much of the source, and the result still needs testing and tuning. NVIDIA-only software such as TensorRT-LLM does not run on AMD GPUs, and some CUDA-only libraries have no ROCm build. Check every dependency, not only the framework.

On the B200, none of this applies: CUDA is the first target of almost every AI library. Our Ubuntu images ship with NVIDIA drivers, CUDA, Docker and the NVIDIA Container Toolkit, and we provide ROCm images for AMD servers. Start with our notes on running ROCm and on PyTorch and vLLM.

How to decide

1. List your dependencies. If anything is CUDA-only and cannot be replaced, choose the B200.
2. Size your memory with the bytes-per-parameter rule plus KV cache. If one model instance needs more than 180 GB per GPU, the MI355X needs fewer GPUs.
3. Match the scale. One GPU means a single B200; a full node makes the MI355X server worth pricing.
4. Validate before you commit. The minimum term is one month and you can cancel any month with no fee, so you can run your real workload on the MI355X before agreeing to a longer term.

MI355X stock is limited. Deploy it in one form: choose the configuration, create a free account or log in, and pay the first month from your balance; the server is delivered in under 10 minutes after payment. The order form has optional notes where you can describe your workload for our engineers.