A March 22, 2026 r/LocalLLaMA post offered the kind of benchmark write-up the AMD local-LLM community actually needs: not marketing slides, but a single-user comparison of ROCm 7 nightly builds and Vulkan on an Mi50 32GB card running llama.cpp. The author lists a concrete setup including Ubuntu Server 24.04, a Proxmox-virtualized EPYC 7532 host, ROCm 7.13.0a20260321, Vulkan 1.4.341.1, and llama.cpp build 8467. The tested models include Qwen 3.5 9B and 27B, Qwen 3.5 122B with partial CPU offload, and Nemotron Cascade 2.

The main finding

The post does not claim a universal winner. Instead, it argues that Vulkan is reliably faster for short-context prompt processing on dense models, while ROCm becomes more attractive as context length increases or when MoE-style workloads and split GPU/CPU inference enter the picture. That is a useful distinction because many local users conflate “backend speed” into one number, even though prompt processing, token generation, context depth, and model architecture can produce very different outcomes.

For dense models in shorter interactive sessions, Vulkan appears to have the cleaner edge.
For longer contexts and effectively any MoE scenario tested by the author, ROCm is described as faster in combined prompt-processing and generation behavior.
The post also notes that Vulkan prompt-processing performance falls off sharply at deeper context lengths.

Why the discussion is useful

The more valuable part of the thread is that it pairs performance claims with operational caveats. The author says TheRock nightlies are not stable releases and describes a ROCm llama-server issue where the prompt cache keeps trying to allocate into VRAM, causing out-of-memory failures. An earlier nightly also appeared to leak memory under a 100k-plus context workload. Those caveats matter because many AMD users are not just choosing a backend for peak throughput; they are choosing a stack they can actually compile, keep running, and debug.

The comments strengthen that point rather than contradict it. One commenter shared additional Mi60 results showing Nemotron Cascade 2 Q4_1 at roughly 726 prompt-processing tokens per second at 65K context, which supports the idea that ROCm can pay off on longer-context workloads. At the same time, another commenter said Vulkan had been much easier to compile and significantly more stable across multiple AMD cards, while another noted that results could shift on newer GPU generations such as RDNA 4.

How to read this benchmark

This is still a hobbyist benchmark on a single system with nightly software, so it should not be treated as a definitive backend ranking. What it does provide is a grounded community signal: Vulkan remains the simpler and often safer choice for straightforward dense-model use, while ROCm may justify the extra friction if your priority is long-context work or MoE inference on AMD hardware. That is a practical decision frame, and it is why the post is worth tracking.

The main finding

Why the discussion is useful

How to read this benchmark

Sources

#vulkan

LocalLLaMA Shares Mi50 ROCm 7 vs Vulkan Benchmarks for llama.cpp