Ok so… 8GB VRAM is *actually* practical for inference + learning, just not “max everything” mode. Think of it like 3 lanes:

Option A (keep 8GB): cheapest, works well for 7B-ish LLMs with 4-bit quant (watch KV cache… long context = surprise OOM). For image gen (yeah, like Stable Diffusion), you’ll live in smaller res / fewer steps / attention slicing land. Fine-tuning: LoRA/QLoRA only, small batch, gradient checkpointing.

Option B (12GB): way less fiddly. If you can swing used NVIDIA GeForce RTX 3060 12GB it’s stupid good value.

Option C (16GB+): comfy for bigger contexts + higher-res, but $$.

If you stay 8GB: cap context, use bf16/fp16 carefully, and set PyTorch memory allocator tweaks. You’ll be satisfied if you accept the knobs tho. cheers

For your situation, 8GB is *practical*, just not “set it and forget it.”

- Inference: 7B-ish LLMs in 4-bit usually fit fine; biggest wall is KV cache (long context = sudden OOM) so keep context modest.
- Fine-tuning: full fine-tune is a nope; but LoRA/QLoRA is doable if you go tiny batch + grad accum + checkpointing (still kinda slow, unfortunately).
- Image models: you’ll fight resolution/batch, yeah… I had issues with random OOM spikes.

If you want less pain long-term, 12–16GB is nicer, but 8GB won’t be wasted. gl!

For your situation, 8GB is actually workable long-term for learning + side projects, you just have to accept you’ll tweak settings sometimes. I ran a bunch of stuff on a NVIDIA GeForce RTX 3060 8GB for a while and it was fine for Stable Diffusion if you keep resolution sane (512-ish) and dont expect huge batches; it’s slower, not impossible. For 7B-ish LLMs, quantized inference is usually OK on 8GB (GGUF/4-bit vibes), but “real” fine-tuning can get annoying fast unless you do LoRA + small batch + checkpointing. Tip: optimize for workflow—use quantization for LLMs and keep SD at lower res first; if you’re constantly OOM, that’s your sign to save for 12–16GB. What GPU are you looking at??

> I’m also on a limited budget, so upgrading to a higher VRAM card isn’t automatically an option. Yeah, I totally get the budget struggle - hardware prices are still kinda wild. Honestly, if you stick with an 8GB card, you should look at it from a DIY hybrid perspective. Use your local setup as a dev environment to write your Python scripts and test small-scale inference, then basically offload the heavy stuff to a professional service when needed. Instead of buying a massive card, you can rent a NVIDIA RTX 4090 on a platform like RunPod or Vast.ai for literally cents an hour. It is a super cost-effective way to handle the occasional fine-tuning or high-res generation without the upfront cost of new hardware. (at least thats what worked for me when I was starting out) For keeping things lean locally on 8GB:

• try Ollama for LLM inference, it handles the memory management and quantization really well
• use ComfyUI instead of heavier interfaces since it has much better VRAM tiling and management
• keep your batch sizes at 1 and just use gradient accumulation to simulate larger batches It is definitely a viable path for learning without going broke tho.

Jumping in here with a quick question... are you planning to run your dev environment on a bare-metal Linux distro or via Windows/WSL2? I ask because Windows takes a notable chunk of VRAM just for the UI, and when you are dealing with an 8GB limit, every 500MB is basically gold for your batch sizes. Honestly, 8GB is totally usable if you focus on efficiency. Instead of just basic inference, you should look into tools like AutoGPTQ or bitsandbytes for 4-bit quantization. If you are buying a card now, the NVIDIA GeForce RTX 4060 8GB is decent for the price, but if you can find a used NVIDIA GeForce RTX 3060 12GB, that extra 4GB is a total lifesaver for larger context windows in LLMs. For side projects, maybe look into computer vision stuff like YOLOv8 or even hosting a local vector database for RAG (Retrieval-Augmented Generation). Those workloads are super lean on 8GB and will teach you a ton about the AI pipeline without the OOM headaches of larger generative models. You just gotta be smart about the memory footprint, but dont let the 8GB cap discourage you too much.

Yep, this is the way

Yeah, 8GB is totally workable for learning, but you're gonna be living on the edge. I agree that it's a solid starting point if you're careful. One thing you might want to consider for the long haul is memory fragmentation... basically, after swapping models a few times, your VRAM gets messy and you'll hit OOM errors even if it looks like you have enough free space. I would suggest getting used to clearing your cache or restarting the kernel often to keep things stable. It's a bit of a technical headache, but it honestly helps you understand how the hardware actually manages the weights. If you want something less frustrating to experiment with on that budget, maybe look into Whisper for speech-to-text; it's super efficient and runs like a dream on 8GB compared to the beefier LLMs. Just watch those temps if you're doing long runs tho, dont want to cook the card.