MI355X Vs B200: Memory per GPU Against a Deeper Software Stack

Summary

MI355X carries 288 GB of HBM3E per GPU against 180 GB on B200, both at about 8 TB/s. On MLPerf Inference v6.1 submissions, MI355X led on gpt-oss-120b and was close on Llama 2 70B. B200 runs on NVIDIA's CUDA stack at up to 1 kW per GPU against MI355X's 1,400 W liquid-cooled maximum. Choose MI355X when models need more memory per GPU and your team runs ROCm, or B200 for CUDA-dependent workloads and sites built for 1 kW-class GPUs.

Comparison Table

MI355X belongs to AMD's MI350 series, introduced on June 12, 2025. Each row sets one GPU against one GPU, or one 8-GPU system against another. MI355X memory, bandwidth and power figures come from AMD's MI355X platform brochure, dated September 2026.

B200 figures come from NVIDIA's HGX reference architecture, updated August 2026, with per-GPU FP4 derived from NVIDIA's 8-GPU system figures. On a dense basis, the 4-bit figures read side by side: about 10.1 PF MXFP4 per MI355X GPU against 9 PF FP4 per B200 GPU. Both are theoretical peaks in different formats, MXFP4 for AMD and FP4 for NVIDIA, so measured results are the better guide.

Memory and Low-precision Compute

MI355X holds 1.6 times the memory of B200 per GPU, 288 GB against 180 GB, at the same bandwidth of about 8 TB/s. That gap matters when a model's weights plus its KV cache, the memory that stores context for each active request, don't fit comfortably on B200.

HBM capacity decides which models fit on one GPU or one 8-GPU server, and how many long-context requests each server holds at once. An 8-GPU MI355X platform carries 2.3 TB against 1.4 TB on an 8-GPU B200 system. For a large model, that can mean fewer GPUs per model replica and more concurrent requests per server. Both add sellable capacity to the same rack.

The extra memory decides less when your customers' models already fit on B200 with headroom. Check the memory footprint of the models in your offtake contract at their real context lengths before you count the extra capacity as revenue.

Both chips support 4-bit floating point: AMD lists its 4-bit format as MXFP4, and NVIDIA lists FP4. NVIDIA's DGX B200 specifications list 72 PF dense and 144 PF sparse FP4 per 8-GPU system, which works out to 9 PF dense and 18 PF sparse per GPU. AMD's MI355X GPU brochure lists about 10.1 PF MXFP4 per GPU without sparsity, and gives no sparsity figure for MXFP4. It lists OCP-FP8 at about 5.0 PF without sparsity and about 10.1 PF with sparsity.

On a dense basis, that puts MI355X at about 10.1 PF MXFP4 against 9 PF FP4 on B200. The formats differ and both figures are theoretical peaks, so measured results are the better guide. The MI355X hardware reference collects AMD's full spec list in one place.

What MLPerf Inference V6.1 Shows for MI355X and B200

In MLCommons' MLPerf Inference v6.1, the MI355X submission posted higher gpt-oss-120b throughput than the B200 submission compared. The two were close on Llama 2 70B in a round whose results were released September 16, 2026, according to AMD's write-up. Both sets of results below come from the closed division, where every submitter runs the same model under the same accuracy rules, on 8-GPU systems.

The Server scenario sends requests at random intervals under a latency limit, close to live API traffic. Offline sends every request at once to measure batch throughput. On these submissions, MI355X delivered about 26% more gpt-oss-120b throughput in Server and about 33% more in Offline. On Llama 2 70B, the gap was under 1% in Server and about 4% in Offline.

AMD's system was a Supermicro AS-4126GS-NMR-LCC, and each submitter ran its own software, so the figures describe these submissions only. In AMD's MLPerf write-up, AMD reports that MI355X leads both B200 and B300 across Offline and Server on gpt-oss-120b.

Two models in one round are a narrow sample. Benchmark your own models, at your own sequence lengths and latency targets, on both chips before you commit capacity to a customer.

Software Stack and Facility Needs

After the specs, two practical differences decide most deployments: the software your customers run and the power and cooling your site provides. Both carry into the offtake contract, because they set the throughput you can promise and the sites where you can deliver it.

ROCm and CUDA

ROCm now runs the main open inference engines on MI355X, with an AMD-published ROCm vLLM container. AMD's GPU brochure lists SGLang among supported frameworks, and AMD's MLPerf write-up describes SGLang serving on 8 MI355X GPUs. In our view, CUDA remains the stack most inference tooling is built and tested on, which gives B200 the deeper software base.

Support for an engine falls short of parity for every kernel, quantization path and model version. Check your customers' frameworks, kernels and serving engines on ROCm before you sign an offtake contract that commits to a delivered throughput. If a customer's stack depends on custom CUDA kernels or CUDA-only libraries, porting becomes part of your delivery plan, and the work sits with you.

Power and cooling

MI355X reaches its 1,400 W maximum only with direct liquid cooling, while B200 is configurable up to 1 kW per GPU. AMD also offers an air-cooled MI355X option and lists a maximum liquid inlet temperature of 43°C. B200 server power and cooling follow each system builder's design. Both power figures are per GPU, since server power adds CPUs, networking and storage and varies by OEM build.

Before you order MI355X at full power, check direct liquid cooling readiness, power per rack and your colocation agreement terms. A site built for 1 kW-class GPUs takes B200 servers with fewer changes. Operators moving up from Hopper can see how B200 compares with H200 for the B200 side of that plan.

When to Choose Which

With 108 GB more memory per GPU, MI355X fits workloads where memory sets the replica count. B200 fits where CUDA or a 1 kW-class power budget sets the terms. The deciding facts sit in the offtake customer's models and software and in your site.

• Large models, long context: Choose MI355X when memory per GPU sets the replica count and your team runs ROCm-supported engines such as vLLM or SGLang.
• CUDA-dependent customers: Choose B200 when the offtake customer's stack depends on CUDA libraries or custom kernels.
• Air-cooled site: Choose B200, whose power is configurable up to 1 kW per GPU, or MI355X at reduced power on its air-cooled option, when your colocation site lacks direct liquid cooling.

Whichever case applies, benchmark your own models on both chips before you commit. Spec sheets and MLPerf results narrow the choice, and your own tests settle it.

Financing the Deployment

Lenders look at both the hardware and the offtake contract, and hardware eligibility comes first, as set out in how lenders underwrite GPU loans. Lenders keep approved-collateral lists, which name the GPUs they accept as security.

USD.AI's published approved collateral list, as of October 2026, includes NVIDIA B200 and AMD MI455X and MI430X. If you're planning an MI355X deployment, confirm collateral eligibility with your lender before you order.

Reuters reported on February 19, 2026, citing The Information, that AMD would backstop Crusoe's $300 million Goldman Sachs loan by offering to lease back its chips if Crusoe couldn't find customers. A backstop of that kind speaks to the second question lenders ask, which is whether the GPUs will have paying customers.

GPU financing through USD.AI covers up to 80% of the deployment cost (loan-to-value), with the operator contributing at least 20% as equity. Rates are fixed or floating, and terms run 3–5 years with straight-line monthly amortization. The GPUs sit in Tier III or Tier IV data centers.

Compute IQ scores each deal on one 100-point scale, reviewing the operator behind the cluster, the customer buying the compute and the offtake contract. The score sets financeability, the pricing tier, and the loan's size and structure. The financing is underwritten on contracted cash flow and secured by the hardware.

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FAQ

Which has more memory, MI355X or B200?

MI355X has more memory, with 288 GB of HBM3E per GPU against 180 GB on B200, or 1.6 times as much. Across an 8-GPU system, that's 2.3 TB against 1.4 TB.

Is MI355X faster than B200 for LLM inference?

It depends on the model and the software stack. On MLPerf Inference v6.1 submissions from September 2026, MI355X posted higher gpt-oss-120b throughput than B200 and was close on Llama 2 70B. Test your own models on both before you choose.

Do MI355X and B200 support the same low-precision formats?

Both support 4-bit and 8-bit floating point; AMD labels its 4-bit format MXFP4, and NVIDIA lists FP4 for Blackwell. Software support for each format differs by framework, so confirm your serving engine supports the format on the GPU you choose.

Is ROCm ready for production inference?

ROCm runs vLLM and SGLang on MI355X, with an AMD-published vLLM container. In our view, support still falls short of parity with CUDA, so validate your models and kernels on ROCm before you commit to a delivered throughput.