NVIDIA H200 vs AMD MI325X: memory, not FLOPS, decides large-model inference

A hardware comparison published on dev.to by GPUYard examines what happens when very large open-weight models — the post cites 400-billion-parameter Llama-class models and a 671B-parameter Mixture-of-Experts architecture like DeepSeek — are served on today's flagship inference GPUs. Its central argument: at that scale, memory capacity and memory bandwidth, not raw compute, decide whether a rack runs efficiently or becomes a bottleneck.

The headline specifications

According to the post, the two accelerators line up as follows. The NVIDIA H200 provides 141 GB of HBM3e, 4.8 TB/s of memory bandwidth, 1,979 TFLOPS of FP8 compute and a 700W TDP. The AMD Instinct MI325X counters with 256 GB of HBM3e, 6.0 TB/s of bandwidth, 2,615 TFLOPS of FP8 compute and a 1000W TDP. AMD leads every raw column, at the cost of roughly 43 percent higher power draw.

Why capacity reshapes cluster design

The post frames the core problem as a VRAM wall: a 400B-parameter model needs hundreds of gigabytes just to hold its weights, before any space is reserved for the KV cache that prompt processing requires. That framing favors the MI325X. Larger per-GPU memory means bigger model shards fit on a single chip, which reduces the degree of tensor parallelism needed and, with it, the cross-GPU traffic — and its latency — that sharding creates. The H200's 141 GB ceiling pushes the same workload the other way: more physical GPUs must be linked to host an equivalent DeepSeek deployment, multi-node topologies arrive sooner, and networking complexity climbs.

Decode is memory-bound

During the decode phase of inference, the article explains, token generation is throttled by how quickly data reaches the compute cores rather than by the cores themselves — a stalled fetch stalls everything. That makes the MI325X's 6.0 TB/s versus the H200's 4.8 TB/s directly relevant to tokens per second. AMD's higher FP8 compute figure counts for less, in the author's assessment, because the workload at this scale is constrained by memory rather than math.

Software remains NVIDIA's moat

On developer experience, the comparison comes down firmly on NVIDIA's side. With CUDA and TensorRT-LLM, new Llama releases are described as running on day one, with no custom kernels and no waiting on community patches. AMD's ROCm has closed much of the gap, particularly when paired with open-source inference engines such as vLLM and SGLang, but teams deploying brand-new models should still budget for a debugging period marked by occasional compile failures and workarounds.

The recommendations

GPUYard points teams toward the MI325X if they are serving the largest models — Llama 400B+ or DeepSeek 671B — and prioritize VRAM density, provided they have engineers who can absorb occasional ROCm troubleshooting; on pure hardware economics, the post argues AMD wins. The H200 gets the nod for mixed training-and-inference workloads and for teams that want stability the moment a new model is released. One caveat worth flagging: the dev.to post ends by directing readers to a longer deep-dive with total-cost-of-ownership calculations on GPUYard's own site, so it also functions as a teaser for that fuller analysis.

Why it matters

Open-weight models keep outgrowing the memory budget of a single accelerator. Once a model's weights alone approach what one GPU can hold, per-GPU capacity stops being a spec-sheet detail and starts dictating cluster topology: how many chips you buy, how they are wired together, how much networking you provision, and how much engineering time goes into the serving stack rather than the product. This comparison lays out the practical trade for anyone planning inference infrastructure: AMD's larger, faster memory against NVIDIA's mature software, with a 1000W versus 700W power bill attached to each side. Spec-sheet arithmetic alone will not settle it — total cost of ownership, networking spend and debugging time all belong in the decision, which is exactly what the longer analysis promises to cover.