VRAM Reality Check: Hardware Limits Shaping the Next LLM Models

VRAM limits are actively steering the next wave of LLMs. A VRAM-savvy discussion highlights that 8GB GPUs push model choices toward MOE architectures in the 15-30B range, while dense 12B variants still deliver solid performance in some tasks ^[1].

Model lineup under memory pressure • Gemma 4 — 30B MOE; vision-focused paths and Gemma 4’s 30B MOE are repeatedly called out as a sweet spot for 8GB VRAM ^[1]. • Gemma3-27B — MOE, a smaller MOE option discussed for tighter VRAM budgets ^[1]. • Qwen3-30B-A3B — a 30B variant noted for its MOE-style efficiency ^[1]. • GPT-OSS-20B — another 20B option that travels the same 8GB VRAM lane ^[1]. • Qwen3 VL — vision-capable entry that keeps showing up in memory-constrained plans ^[1]. • Magistral 24B — top-notch vision capabilities surface in the same discussions ^[1]. • “More MOE models in 15-30B size for 8GB VRAM” and “More Coding models in 10-20B size for 8GB VRAM” are repeatedly cited as deployment targets ^[1].

Dense vs MOE, and what actually runs fast The thread pushes a core takeaway: MOE goes fast, and dense 12B models can still punch above their weight class in some scenarios ^[1]. Deployment chatter also emphasizes speed vs capacity tradeoffs when offloading to RAM to fit bigger models, with performance hit often accepted for bigger capability ^[1]. Vision-heavy stacks aren’t ignored, thanks to Qwen3 VL and Magistral 24B in the mix ^[1].

Closing thought Memory limits aren’t just bottlenecks—they’re shaping which models designers actually deploy and how they layer MOE, dense, and vision stacks in production ^[1].

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