TL;DR: 16GB VRAM is the current sweet spot for local AI, and there are three real contenders at this tier. The RTX 4060 Ti 16GB is the safest buy today. The Arc B580 undercuts it by $150 but comes with software caveats. The RTX 5060 Ti looks promising but isn't shipping yet — don't wait on it.
Bottom line: Buy the RTX 4060 Ti 16GB now. Wait for the 5060 Ti only if you're 3+ months out from needing a GPU.
Why 16GB Is the Sweet Spot Right Now
You can run 13B to 20B parameter models comfortably in 16GB. That's the range where local AI starts feeling genuinely useful — not just a toy. At 7B you're leaving capability on the table. At 24GB+ you're paying a significant premium.
The practical ceiling for 16GB: Llama 3.1 13B runs smoothly at Q4_K_M (4-bit quantization — a technique that compresses model weights to fit in less memory while preserving most quality). You can push to Mistral 22B with aggressive quantization (Q2 or Q3), but quality degrades noticeably. For 30B models, 16GB is technically possible but barely — you'll need Q2_K which sacrifices too much.
If your use case requires 30B+ models regularly, skip 16GB and go straight to 24GB. A used RTX 3090 at ~$800 is a better fit.
The Contenders
RTX 4060 Ti 16GB — ~$400 as of February 2026
GDDR6 memory, 288 GB/s bandwidth. Not the fastest bandwidth in this tier, but it works with everything: Ollama, LM Studio, llama.cpp, ComfyUI, all of it. Zero compatibility headaches.
Benchmark: ~43 tokens/second on Llama 3.1 8B Q4_K_M. Drops to ~28 t/s on a 13B Q4_K_M. (Tokens per second = how fast the model generates text — higher is faster.)
The weak spot: bandwidth. At 288 GB/s, it's slower than you'd hope for a $400 card. The 4070 Ti Super at ~$750 does 672 GB/s and handles 13B noticeably faster. But if $400 is the cap, the 4060 Ti is reliable.
Intel Arc B580 — ~$250 as of February 2026
12GB GDDR6, 456 GB/s bandwidth. Yes, 12GB — not 16GB. Intel markets this as a budget AI card but the VRAM drops you from 16GB territory to something closer to 12GB territory. That matters.
Where it wins: bandwidth per dollar. At $250, the memory speed is surprisingly competitive.
Where it loses: software support. ROCm (the open-source GPU compute platform for non-NVIDIA hardware) has compatibility gaps. Ollama and llama.cpp work, but you'll hit edge cases — certain model formats, certain quantization types, some fine-tunes. If you're running strictly standard GGUF files (the most common local LLM format), the B580 is fine. If you want to experiment broadly, it'll frustrate you.
Benchmark: ~38 t/s on Llama 3.1 8B Q4_K_M (with Ollama). Results vary more than NVIDIA cards depending on driver version. If you're shopping strictly under $300, we ranked the best budget GPUs for local AI — the B580 tops that list too.
RTX 5060 Ti — ~$399–449 (leaked price, not yet shipping as of February 2026)
16GB GDDR7, higher bandwidth than the 4060 Ti. On paper, this beats the 4060 Ti at a similar price point. Probably true when it ships.
But it doesn't exist yet. "Not yet shipping" means no real benchmarks, no confirmed availability, no launch date locked in. If you need a GPU in the next 2–3 months, this isn't an option. If you're planning a build 4+ months out, it's worth waiting for real reviews first. We're tracking RTX 5060 Ti pricing and availability here.
The Verdict
| Scenario | Pick |
|---|---|
| Need it now, want zero hassle | RTX 4060 Ti 16GB (~$400) |
| Tight budget, okay with some friction | Arc B580 (~$250) |
| Can wait 3–6 months | Hold for RTX 5060 Ti reviews |
When to skip 16GB entirely: if you want to run 30B+ models regularly, or if you're doing anything with large context windows (100K+ tokens), you need 24GB. See our full GPU comparison guide for 24GB options.
Not sure how much VRAM your target models actually need? See our VRAM breakdown by model size.