TL;DR: Yes, 8GB of VRAM can run local LLMs in 2026 — but only small ones. You're limited to 7B-parameter models at standard quantization, and context windows will be short. If you already have an 8GB card, try it before buying anything new. If you're buying new, spend the extra $50-150 and get 12GB or 16GB instead.
The Honest Answer
8GB works. It's not ideal, and you'll hit walls, but calling it "dead for AI" would be wrong.
Here's what 8GB of VRAM actually runs:
-
7B-8B models at Q4_K_M: ~4.5GB for the model, leaving 3.5GB for context and overhead. Comfortably runs Llama 3.1 8B, Mistral 7B, Gemma 3 4B, and Phi-3.5 Mini. These models are surprisingly capable for chat, summarization, and light coding.
-
Autocomplete coding models: Qwen 2.5 Coder 1.5B uses under 2GB. StarCoder2 3B fits easily. If you just want Copilot-style tab completions, 8GB is overkill.
-
Small reasoning models: Phi-4 Mini 3.8B (if available at Q4) fits well and handles reasoning tasks decently for its size.
Speed on an RTX 4060 8GB (272 GB/s bandwidth): expect 40-55 tokens per second on Llama 3.1 8B at Q4. That's fast — well above comfortable reading speed. The 8GB limitation isn't about speed, it's about model size.
What 8GB Struggles With
13B-14B models: A 13B model at Q4_K_M needs about 8-9GB. That's your entire VRAM budget with nothing left for the KV cache (the memory used to track your conversation). In practice, 13B models either won't load or crash mid-conversation on 8GB cards.
Long conversations: Even with a 7B model, the KV cache grows as the conversation gets longer. After 2,000-4,000 tokens of context, you'll start hitting memory limits. The model either slows dramatically (spilling to system RAM) or errors out.
Any model over 14B: Completely off the table. No amount of quantization will squeeze a 27B or 32B model into 8GB at usable quality.
Multimodal models: Vision-enabled models (Gemma 3 with image support, LLaVA) need extra VRAM for image processing. Most won't fit on 8GB alongside the text model.
When to Save Up for 12GB or 16GB
Get 12GB if: You want to run 13B-14B models comfortably. A used RTX 3060 12GB goes for about $250 as of March 2026 — one of the best deals in local AI hardware. 12GB lets you run Phi-4 14B and Qwen 2.5 14B at Q4, which are dramatically better than any 7B model. See our Phi-4 hardware guide for details.
Get 16GB if: You want to run 20B-27B models or keep 13B models loaded with long context. The RTX 5060 Ti 16GB ($430 new) or RTX 4060 Ti 16GB ($350 used) opens up Gemma 3 27B at Q3, Mistral 22B at Q4, and 14B models at Q8 (near-lossless quality). Check our VRAM guide for the full breakdown of what each tier unlocks.
The jump from 8GB to 12GB is the single biggest quality upgrade you can make in local AI. Going from 7B to 14B models transforms the experience from "neat toy" to "genuinely useful tool." The $250 for a used RTX 3060 12GB is the best money you can spend if you're serious about local LLMs.
Making the Most of 8GB
If you're sticking with 8GB for now, here's how to maximize it:
Use aggressive quantization. Q4_K_M is the default, but Q3_K_M or even IQ3_XS will squeeze a slightly larger model into your VRAM. The quality drop from Q4 to Q3 is noticeable but not devastating for casual use.
Enable KV cache quantization. In llama.cpp, use --cache-type-k q8_0 --cache-type-v q4_0 to compress the conversation cache. This can save 1-2GB, giving you meaningfully longer conversations.
Use Ollama with num_ctx set low. Run ollama run llama3.1:8b --num-ctx 2048 to limit context length and reduce VRAM usage. You'll lose conversational memory faster, but the model stays responsive.
Don't run other VRAM-hungry apps simultaneously. Close your browser's hardware acceleration. Close any video players using GPU decode. Every MB of VRAM claimed by another app is a MB your model can't use.
The Bottom Line
8GB VRAM in 2026 is the minimum viable setup for local AI. You can run useful 7B models at good speeds. You can't run anything larger without painful compromises.
If you're just starting out and want to experiment, your 8GB card is fine — don't spend money until you know you want more. But if you've been using local AI for a while and find yourself wanting better responses, the upgrade to 12GB or 16GB is the single highest-impact hardware change you can make.
For the full picture of what every VRAM tier unlocks, read our complete VRAM guide. For buying recommendations at every budget, check our GPU rankings.