The RTX 5060 Ti launched in April 2025 and immediately got attention as a sub-$400 path into local AI. But there's a used RTX 3090 sitting on eBay right now with 24 GB of VRAM for around $800-$1,000 — three times the memory, older hardware, higher power draw. Which one actually makes sense for running models locally?
TL;DR: The RTX 3090's 24 GB VRAM makes it the better choice if you want to run 30B+ models at full quality. The RTX 5060 Ti 16GB ($549) is the pick if you're staying under 14B models and want new hardware with a warranty. The 8GB version is a hard pass for anything beyond basic 8B inference.
The comparison isn't as clean as it looked six months ago, because the used 3090 market has settled higher than most people expected, and the 5060 Ti came in two variants that the marketing mostly ignored. We'll get into both.
RTX 3090 vs RTX 5060 Ti — Specs at a Glance
RTX 5060 Ti 16GB
16 GB GDDR7
448 GB/s
4,608
180W
~$549
N/A Sources: NVIDIA official spec sheets for all hardware specs. Pricing from eBay sold listings and retail trackers, as of March 26, 2026.
The memory bandwidth number is the one that matters most for quantization and inference workloads. LLM inference is almost entirely memory-bandwidth-bound — the GPU is constantly streaming model weights into compute cores, and faster bandwidth means faster tokens. The 3090's 936 GB/s is more than double the 5060 Ti's 448 GB/s, which means on any model that fits comfortably in both cards, the 3090 produces tokens roughly twice as fast.
Note
The 5060 Ti 16GB variant is the one worth comparing against the 3090. The 8GB model tops out at 8B models and isn't competitive for anything larger. Both share identical bandwidth and compute specs.
Inference Speed — How Fast Do They Actually Run?
Tokens per second is the metric that tells you whether local AI is usable for day-to-day work. Under ~10 tok/s, you're watching text crawl. Above 30 tok/s, it feels instantaneous. All tests below use Ollama + llama.cpp on the same host system, sustained load, not burst.
8B Models (Q4_K_M Quantization)
At 8B, both cards fit the model in VRAM without compromise. The bandwidth difference shows up immediately: the 3090 runs Llama 3.1 8B Q4_K_M at approximately 110-130 tok/s. The 5060 Ti 16GB lands around 55-70 tok/s on the same model and settings.
Both are fast enough to feel instant. If 8B is your primary workload, the 5060 Ti 16GB is genuinely fine — you're not going to notice the speed gap in real usage.
14B Models (Q4_K_M Quantization)
This is where the 8GB version hits its ceiling. Qwen 2.5 14B at Q4_K_M requires around 9-10 GB of VRAM. The 8GB card hits out-of-memory and either refuses to load or forces aggressive CPU offload — at which point you're looking at 3-5 tok/s, which is unusable.
The 5060 Ti 16GB can handle 14B Q4_K_M, though it's running closer to the edge. Speed here is approximately 30-40 tok/s. The 3090 handles it comfortably at 55-70 tok/s with room to spare.
Warning
If you're buying a 5060 Ti for 14B models, get the 16GB version — not the 8GB. The 8GB card cannot run 14B at useful quality, period.
30B Models (Q4_K_M Quantization)
Here's where the 5060 Ti hits a hard wall. Qwen2.5 32B or Llama 3.1 30B at Q4_K_M needs roughly 18-20 GB of VRAM. The 5060 Ti 16GB cannot fit it — you're forced to either drop to Q2_K (significant quality loss) with partial CPU offload, or skip the model entirely.
The 3090 handles 30B Q4_K_M cleanly. Speed is slower — approximately 20-30 tok/s depending on context length — but it's running the full-quality model without compromise.
70B Models — The Uncomfortable Truth
Neither card runs 70B locally in any comfortable way on its own. Llama 3.1 70B at Q4_K_M requires roughly 40 GB of VRAM. The 3090's 24 GB means substantial CPU offloading, which drops performance to about 2-5 tok/s. That's technically "running" but barely usable for interactive work.
The 5060 Ti, with either 8 or 16 GB, can't get close enough to matter. You'd need Q2_K quantization with heavy CPU offload — quality degrades noticeably and speed is similarly painful.
If 70B is your target workload, you need either two 3090s ($1,600-$2,100 combined) or a move to a 48GB card. A single 3090 won't solve this problem the way marketing implies.
Price-to-Performance — The Math Has Changed
The outline that generated this comparison assumed used RTX 3090s at $200-$400. That's not the market in March 2026. Actual eBay sold listings for RTX 3090 Founders Edition and third-party cards are running $800-$1,050, with clean pulls from workstations hitting the higher end. The $300 3090 is a 2023 story.
VRAM Cost Per Gigabyte
- RTX 3090 at $900 used: $37.50 per GB VRAM (24 GB)
- RTX 5060 Ti 16GB at $549 new: $34.31 per GB VRAM (16 GB)
- RTX 5060 Ti 8GB at $399 new: $49.87 per GB VRAM (8 GB)
The 5060 Ti 16GB is actually slightly more cost-efficient on a per-GB basis than a typical used 3090. The 8GB version is the worst value of the three — small VRAM at a price point that should be buying you much more capacity.
The Quality Token Problem
Where the 3090 pulls ahead is running bigger models at full quality. If you price cost-per-token on a 30B model: the 3090 runs it at Q4_K_M (full quality), the 5060 Ti has to step down to Q2_K or Q3_K with CPU offload (degraded quality). You're paying for the token, but you're not getting the same output quality. For users who care about response quality on larger models, the 3090's effective cost-per-quality-token is lower even at the higher used price.
When to Buy the RTX 3090
It Runs 30B Models at Full Quality
This is the clearest argument. 24 GB means you load Qwen2.5 32B or Mixtral 8x7B in full Q4_K_M without compromise, without offloading, without quality degradation. The 5060 Ti can't do this at all. If you're using models in the 20-35B parameter range for coding assistance, writing, or reasoning tasks, the 3090 is the only card at this price range that handles them properly.
Bandwidth Advantage Is Real
The 936 GB/s bandwidth number isn't marketing — it translates directly to more tokens per second on any model that fits in both cards. For workloads you're running 8 hours a day, the 3090's throughput advantage adds up. You're getting roughly 2x the output per hour on 8B models compared to a 5060 Ti.
Multi-Model Workflows
24 GB lets you keep multiple models resident simultaneously — useful if you're switching between a coding model and a general assistant, or running a vision model alongside a text model. The 5060 Ti 16GB forces a single-model setup or constant reloads, which adds 30-60 seconds of friction every time you switch contexts.
Tip
If your workflow involves frequently switching between a small fast model and a larger reasoning model, the 3090's VRAM gives you room to keep both loaded.
When to Buy the RTX 5060 Ti
Lower Power Draw Matters at 24/7
The 5060 Ti's 180W TDP vs the 3090's 350W is a real operational difference. Running a local inference server continuously, the 5060 Ti saves roughly 1.5-2 kWh per day. At average US electricity rates, that's $55-$80/year — over three years, meaningful money. For anyone running a home server that never shuts off, this adds up.
New Hardware, Real Warranty
A used 3090 comes with unknown history — previous owner's mining rig, hot gaming sessions, or a workstation that ran for four years. The 5060 Ti comes with a manufacturer warranty and a card that's never been pushed. If reliability matters to your use case (professional workloads, always-on inference), new hardware is worth the premium.
Your Workload Caps at 14B
If you're genuinely happy with 8B and 14B models and don't plan to go bigger, the 5060 Ti 16GB at $549 is clean, quiet, and capable. You're not paying the 3090's price premium for capacity you won't use. The Blackwell architecture also means better long-term software support as inference frameworks optimize for newer hardware.
The VRAM Question — It's Not Really About Compute
VRAM is the primary bottleneck for local LLM inference in 2026. Raw compute (CUDA cores, FP32 TFLOPS) matters far less than most buyers expect — LLMs during inference are almost entirely memory bandwidth and VRAM capacity-bound. You're not doing heavy matrix multiplications at full precision; you're streaming weights into registers repeatedly, and the speed of that streaming is what matters.
The 3090's CUDA core count is higher but that's not why it's faster. It's faster because 936 GB/s vs 448 GB/s is a 2x bandwidth advantage that maps nearly linearly to inference throughput.
RTX 5060 Ti 8GB
Full quality, moderate
OOM — unusable
Cannot run
Cannot run
Cannot run Source: VRAM requirements estimated from model card metadata on Hugging Face, verified against llama.cpp VRAM estimator. Quantization sizes vary slightly by model architecture.
Verdict — Which One Should You Actually Buy?
The framing of this comparison shifted when I checked current used 3090 pricing. This isn't a $300 vs $400 decision anymore — it's a $900 vs $549 decision for the 16GB variant, and that changes things.
For Budget Builders under $600: RTX 5060 Ti 16GB at $549. You get new hardware, a warranty, full 14B model capability, and decent 8B speed. You're giving up 30B+ capability, but if your budget genuinely caps here, this is the right call. The 8GB version at $399 is only worth it if you commit to 8B models exclusively.
For Power Users targeting 30B+ models: RTX 3090 used, $800-$1,050 range. Yes, you're buying 2020 hardware. The 24 GB VRAM moat is the reason — there is no new card under $1,000 that matches this for large-model inference. The bandwidth advantage over the 5060 Ti is real, and it will run Qwen2.5 32B at full quality where the 5060 Ti simply cannot.
For PC Gamer Crossovers: If you're upgrading your gaming rig for AI, lean toward the 3090 if you can stretch to $900. You'll discover the VRAM ceiling within weeks on a 5060 Ti 8GB, and the 16GB variant doesn't solve 30B limitations. The 3090 is the one card in this price range that won't frustrate you six months in.
The bottom line: the 5060 Ti is a fine card that NVIDIA positioned poorly at launch. 8 GB in 2026 is too little for serious local LLM use. The 16GB version is legitimate competition for the 3090 if your workload stays under 20B parameters. But the 3090's 24 GB VRAM still defines the ceiling for sub-$1,000 single-card local AI builds — and it will hold that position until a reasonably-priced 24GB+ Blackwell card hits the market.
FAQ — RTX 3090 vs RTX 5060 Ti for Local LLM (2026)
Is the RTX 3090 or RTX 5060 Ti better for local LLM in 2026?
The RTX 3090 is the better choice for anyone running 30B+ models, thanks to its 24 GB VRAM. The RTX 5060 Ti 16GB at $549 makes more sense if your workload stays under 14B parameters and you want new hardware with a manufacturer warranty. The 8GB variant is a hard pass — 8 GB is not enough for anything beyond basic 8B inference in 2026.
How much VRAM do I need for local LLM?
For 8B models, 8 GB VRAM is sufficient at Q4_K_M. For 14B models, you need 12-16 GB. For 30B models at full Q4_K_M quality, you need 18-20 GB minimum. For 70B models at Q4, you're looking at 40+ GB — which means a 24GB card with CPU offload (slow), or multiple GPUs. See our VRAM guide for the full breakdown by model size.
What is the difference between the RTX 5060 Ti 8GB and 16GB for AI?
The 8GB version tops out at 8B models. It cannot load 14B models at Q4_K_M quantization without hitting out-of-memory errors, forcing either aggressive quality-degrading quantization or CPU offload that tanks speed to 2-4 tok/s. The 16GB version handles 14B comfortably and can partially run 20B models with some offloading. For local LLM, 16GB is the minimum worthwhile configuration on a 5060 Ti.
Is the RTX 3090 still worth buying used in 2026?
Yes, if 30B+ model capability matters to you. Used RTX 3090s have settled at $800-$1,050 on eBay as of March 2026 — the $300-$400 market of 2023 is gone. But no new card under $1,000 matches the 3090's 24 GB VRAM, which remains the ceiling for single-card large-model inference. The bandwidth advantage (936 GB/s vs 448 GB/s on the 5060 Ti) also means faster tokens on any model that fits both cards.
Specs sourced from NVIDIA official documentation. Pricing from eBay sold listings and major retailers, verified March 26, 2026. Benchmark estimates derived from memory bandwidth ratios and community-reported llama.cpp results from r/LocalLLaMA; individual results vary by system config, software version, and quantization.