Why is my local LLM slow even when it fits in memory?

A model can fit but still be slow because prompt prefill, KV cache growth, CPU fallback, low memory bandwidth, runtime overhead, or long context can dominate token generation.

Can a huge MoE model run on 8GB VRAM?

Only as an experimental streaming setup. Sparse MoE expert streaming can avoid loading every expert into VRAM at once, but current consumer implementations are usually far too slow for normal chat.

Is 32GB VRAM better than 24GB VRAM for local LLMs?

Yes for larger quantizations, longer context, and MoE models. A 24GB card remains a strong CUDA ecosystem choice, while 32GB cards give more model and KV-cache headroom.

Local inference triage

Will this local LLM fit, and will it be usable?

RAM and VRAM are only the first filter. This estimator also checks context length, quantization, runtime, model shape, and likely bottlenecks so you do not confuse "loads once" with "usable every day."

Run estimate Read the bottleneck guide

Inputs

Your machine and target model

Hardware class Runtime System RAM GPU VRAM Model target Quantization Context length Workload

Reliability Tool calls failing?

Use a write-ahead log, schema validation, and confirmation checks before blaming the model.

Hardware R9700 or RTX 3090?

32GB VRAM is attractive, but CUDA maturity still matters for agent stacks.

Experimental Huge MoE on 8GB VRAM?

Expert streaming is a real architecture direction, not a normal daily-driver path yet.

Loop Engineering in 5 Minutes

Will this local LLM fit, and will it be usable?

Your machine and target model