8GB is the entry floor for local AI, and plenty of capable models run on it if you pick the right size and quantization. Here's what actually works, and the trap to avoid.
8GB of VRAM is the entry floor for local AI, and while it can't run the big models, it runs real, useful ones — a fact that surprises people who assume you need a $1,000 card to start. An 8GB GPU comfortably runs 7–8B models at Q4 quantization — Llama 8B, Mistral 7B, Qwen3-8B and their kin — which are genuinely capable for chat, coding assistance, summarization and everyday tasks. The trick is matching quantization and context to the memory you have. Here's exactly what fits on 8GB, what to skip, and how to squeeze the most out of a small card.
What actually runs on 8GB
An 8GB card lives in the 7–8B world, and that world is more capable than it used to be. Modern 8B models handle coding help, Q&A, writing assistance and general chat well — a year ago this quality was frontier-grade. At Q4 quantization, a 7–8B model plus a modest context window fits inside 8GB. Push the context too far or try a 13B model and you'll spill over, so the discipline is: 7–8B model, Q4, sensible context. Do that and 8GB delivers a genuinely useful local assistant. It's the same VRAM-first logic as every tier — just with a smaller ceiling.
An 8B model on 8GB handles coding help, writing and chat well — real local AI, just in the smaller-model tier. · Unsplash
Getting the most from 8GB
Two levers stretch a small card. First, quantization discipline: stick to Q4 for 7–8B models — it's the best balance of fitting and quality, and dropping to Q3 to force a bigger model usually isn't worth the quality loss. Second, context management: long context windows consume VRAM fast, so keep context modest (4–8k) unless you specifically need more. If you find yourself constantly wanting 13B models or long context, that's the signal to move to a 12GB card, which opens up the next tier. But for the 7–8B world, 8GB with good discipline works well.
Quick answers
What's the best local LLM for 8GB VRAM?
A 7–8B model at Q4 quantization — Qwen3-8B, Llama 8B, or Mistral 7B are all strong choices that fit comfortably and handle chat, coding help and everyday tasks well. Qwen3-8B is a good default for its balance of capability and efficiency. Keep the context window modest (4–8k) to stay within 8GB. These models are genuinely useful; 8GB is a real local-AI tier, not a toy.
Can 8GB run a 13B model?
Not comfortably. A 13B model at Q4 needs more than 8GB, so you'd have to use very aggressive quantization (hurting quality) or offload to system RAM (slow). It's better to run a strong 7–8B model that fits well than to force a 13B that struggles. If you want the 13B tier, a 12GB card is the right hardware — it's a modest step up that opens real headroom. On 8GB, stay in the 7–8B world.
Is 8GB enough for local AI in 2026?
For the 7–8B tier, yes — and those models are capable enough for a lot of real work. 8GB is the entry floor: it won't run the big models, but it runs useful ones reliably. It's a fine place to start or to run lightweight local AI. If your needs grow toward larger models or long context, 12GB and then 16GB are the upgrade path. But don't dismiss 8GB — it does real local AI.
8GB runs real local AI in the 7–8B tier — pick a good 8B model at Q4, keep context modest, and it works. When you want more, a 12GB card is the next step. Size specific models in the VRAM calculator.
