Used RTX 3090 for LLMs in 2026: Still King?
February 15, 2026 ·D-Central Technologies·⏱ 13 min read
Last updated: April 16, 2026
TL;DR — For the pleb running LLMs under 70B parameters at Q4–Q5 quants, a used RTX 3090 at ~$600–$800 remains the best $/VRAM play on the consumer market in 2026. The 4090 wins raw tok/s by ~30–50% on smaller models; the 5090 dominates flagship workloads but costs roughly 3× a used 3090.
The question lands in our inbox every week. Pleb has a shed, 240V service he ran for an S19, a heap of [Bitcoin mining](/content/mining-glossary/mining/ "Glossary: Bitcoin mining"/index.html) instincts, and now he wants to run his own LLMs.
The 3090’s unfair advantage — 24 GB VRAM
If you’re new to LLM hardware, forget everything you know about “faster = better.” For inference, VRAM is king. Think of it like the RAM on your Bitcoin node: if your chainstate doesn’t fit in memory, your node crawls. If your LLM weights don’t fit in VRAM, the model either won’t load at all, or it spills into system RAM and slows to a crawl that makes you question your life choices.
Modern open-source models are measured in billions of parameters. Each parameter, unquantized (FP16), takes 2 bytes. Llama 3.1 70B at FP16 wants ~140 GB of memory. No consumer GPU has that. So we quantize — compress the weights down to 4 or 5 bits per parameter — and suddenly that 70B model fits in ~40 GB. Still doesn’t fit on one 3090. But on two? Comfortable.
The 3090 specs that still matter:
| Spec | RTX 3090 |
|---|---|
| VRAM | 24 GB GDDR6X |
| Memory bandwidth | 936 GB/s |
| Memory bus | 384-bit |
| CUDA cores | 10,496 |
| Tensor cores | 328 (3rd gen) |
| TDP | 350W |
| Architecture | Ampere (GA102) |
| PCIe | 4.0 x16 |
| Release | September 2020 |
What the 3090 actually runs
Numbers below are typical community-benchmark ranges on a single 3090 unless noted.
| Model | Params | Quant | VRAM used | Tok/s (3090) | Notes |
|---|---|---|---|---|---|
| Llama 3.1 8B | 8B | Q4_K_M | ~6 GB | 90–120 | Snappy. Leaves plenty of VRAM for big context. |
| Llama 3.1 70B | 70B | Q4_K_M | ~43 GB | 15–22 (dual 3090) | The classic dual-3090 use case. |
| Gemma 3 27B | 27B | Q5_K_M | ~20 GB | 28–38 | Google’s open-weights champ fits comfortably. |
3090 vs 4090 vs 5090 — head-to-head
| Spec | RTX 3090 (used) | RTX 4090 (used/new) | RTX 5090 (new) |
|---|---|---|---|
| Typical price (2026) | $600–$850 | $1,400–$1,800 | $2,200–$2,800 |
| VRAM | 24 GB GDDR6X | 24 GB GDDR6X | 32 GB GDDR7 |
| Memory bandwidth | 936 GB/s | 1,008 GB/s | 1,792 GB/s |
| TDP | 350W | 450W | 575W |
Honest read:
- The 3090 is untouched on $/VRAM. If your workload is “I want to run a 30B class model at Q5 or a 70B at Q4 with two cards,” you’re paying less per usable gigabyte than with any other option NVIDIA sells.
Buying a used 3090 — pleb checklist
Where to source:
- eBay — widest selection; favor sellers with return policies and “local pickup available” (fewer shipping-damage cases).
- Facebook Marketplace / Craigslist / Kijiji — best prices, test before paying.
- Local classifieds — gold for hands-on inspection.
Red flags on inspection:
- Rattling bearings — spin the fans by hand. Any grinding or wobble = fan replacement.
- Corroded contacts — PCIe connector pins should be bright gold.
- Burn-in before trusting: Prove it’s stable for at least four hours under real load.
Closing
The dual-3090 rig is the sovereign AI sweet spot: 48 GB of combined VRAM, roughly 1,300W at the wall when both are working, and comfortable coverage of every meaningful open-source LLM workload from coding assistants to 70B-class reasoning models. If you’ve got the shed, the breaker, and a working knowledge of PCIe risers, you’ve got the bones of a self-hosted AI Hashcenter that will serve you for years.