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// 45d agoINFRASTRUCTURE
Dual GPUs vs Bigger Card for 31B LLMs
A LocalLLaMA user asks whether it is better to run 27B to 31B models on a single larger-VRAM GPU or on two 16 GB cards, given the overhead of multi-GPU inference. The post compares a single 32 GB class card against dual 7800 XT-style setups and asks whether the extra VRAM is worth the added PCIe and software complexity in llama.cpp, with vLLM mentioned as a possible alternative for dual-GPU runs.
// ANALYSIS
Hot take: for interactive local inference, more VRAM on one card is usually cleaner and more predictable than splitting across two consumer GPUs unless the model simply will not fit.
- –`llama.cpp` supports multi-GPU splitting, but that does not remove synchronization overhead; it mainly helps fit larger models.
- –`vLLM` has first-class tensor-parallel inference, so it is the more natural choice if the goal is multi-GPU serving rather than single-user tinkering.
- –A 32 GB card is a meaningful step up because it reduces or eliminates the need to shard weights and leaves more room for KV cache and longer contexts.
- –Dual 16 GB cards can be a practical capacity play, but they usually trade simplicity and latency consistency for extra tuning and bus overhead.
- –For 27B to 31B models, the real decision is often “fit comfortably on one device” versus “fit at all across devices,” not “scale linearly with more GPUs.”
// TAGS
local-llmllama-cppvllmmulti-gpuvramamd-gpuinferencehardware
DISCOVERED
45d ago
2026-04-26
PUBLISHED
45d ago
2026-04-26
RELEVANCE
8/ 10
AUTHOR
rebelSun25