Buying guide
Best Budget GPUs for Running LLMs Locally in 2026 (Under $700)
The best used and budget GPUs under $700 for local AI inference — RTX 3090, 3080 Ti, RX 7900 XTX, and when to skip the GPU entirely.
The used GPU market is one of the best deals in local AI hardware right now. A 2020-generation RTX 3090 with 24GB of VRAM sells used for $450–700 — a fraction of what a new 24GB card costs — and for local LLM inference, that 24GB is what matters. The RTX 3090 can fit every popular 7B–34B model at Q4_K_M quantization and can pair with a second 3090 via NVLink to comfortably run 70B models.
The key insight for budget buyers: VRAM capacity matters more than GPU generation for local LLM inference. A used card from 2020 with 24GB of VRAM will out-perform a new card with 8–12GB of VRAM for any model that needs more than those 12GB. Buying the newest card with the smallest VRAM is the most common budget mistake in this space. This guide focuses on what actually works under $700.
Why Used GPUs Win at This Price Point
Local LLM inference is dominated by VRAM capacity and memory bandwidth, not the latest tensor cores or ray tracing features. A model's weights are fixed in size — quantization reduces them, but the size of those weights does not change based on how new the GPU is. An RTX 3090 with 24GB (released 2020) fits all the same models as an RTX 4090 with 24GB (released 2022), just with lower bandwidth and therefore lower throughput.
The math is stark: an RTX 4060 Ti 16GB costs approximately $450 new and runs Llama 3.1 8B comfortably, but cannot fit a 24B model. A used RTX 3090 at the same price has 24GB and runs 24B models without issue. If your models fit in 16GB, the new card is fine. If you want 24GB — and most people who are serious about local AI do — the used 3090 is the clear winner at this budget.
RTX 3090: The 24GB Value King (~$450–650 Used)
The RTX 3090 is the most recommended budget card for local LLMs and has been for several years. At $450–650 used in 2026, it offers the same 24GB VRAM capacity as the RTX 4090, with community-estimated throughput of approximately 55–65 tok/s on Llama 3.1 8B at Q4_K_M. These are third-party community estimates, not measurements run by ClankerBuilder.
The 3090 also supports consumer NVLink, which means two 3090s with an NVLink bridge (around $120–200) pool 48GB of VRAM as a single logical device — the most affordable path to comfortable 70B inference on consumer hardware.
Where to find used 3090s: eBay and Facebook Marketplace are the main channels. Mining cards are common on eBay; check seller reputation and look for cards with photos of the cooler. Stress test before accepting: run a GPU load test for 15–30 minutes and monitor temperatures. Gaming-used cards are preferable to mining-used cards, though many mining cards are perfectly functional.
- 24GB VRAM — fits all 7B–34B models at Q4_K_M
- NVLink-capable for dual-GPU 48GB builds
- Community-estimated ~55–65 tok/s on Llama 3.1 8B Q4_K_M
- Street price: $450–650 used (eBay, Facebook Marketplace)
RTX 3080 Ti: 12GB for $300–400 Used
The RTX 3080 Ti sits one VRAM tier down at 12GB, which makes it a solid card for 7B through 13B models — the size range most developers and hobbyists actually use for coding assistants, chat, and summarization. Community estimates suggest approximately 45–55 tok/s on 8B models at Q4_K_M, which is comfortably fast for single-user use.
The limitation is firm: 12GB cannot fit a 24B model at Q4_K_M, and 70B is out of reach. If you know your workload stays in the 7–13B range for the foreseeable future, the RTX 3080 Ti at $300–400 used is an excellent entry point. If you anticipate wanting larger models, stretch the budget to a 3090 — you will not regret the extra 12GB.
- 12GB VRAM — ideal for 7B–13B models
- Community-estimated ~45–55 tok/s on 8B models
- Street price: $300–400 used
- Cannot fit 24B+ models — upgrade path is a second card, not this one
AMD RX 7900 XTX: 24GB New for ~$850
The AMD RX 7900 XTX falls slightly over the $700 cutoff at approximately $850 new, but it is worth including because it delivers 24GB of GDDR6 at a lower price than any new NVIDIA 24GB card. ROCm, AMD's open-source GPU compute platform, has improved significantly and supports llama.cpp and other major inference runtimes, though the ecosystem is less mature than CUDA and some edge cases still require workarounds.
For buyers who prefer new hardware with a warranty, dislike the used-card risk, and want to stay under $1,000, the RX 7900 XTX is a reasonable option. Community reports suggest performance is broadly similar to the RTX 3090 for inference workloads, though fine-tuning workflows are less well supported on AMD hardware due to the CUDA ecosystem's dominance in training tools.
Skip the GPU: When a Mac Makes More Sense
If your priorities are silence, portability, no power/cooling concerns, and you need to run 70B models without building a dual-GPU rig, a Mac with Apple Silicon unified memory is worth serious consideration. An M3 Max MacBook Pro or Mac Studio with 128GB of unified memory can run a 70B model at Q4_K_M in its entirety in memory, with no PCIe bus overhead and no separate cooling to manage.
The tradeoffs: Macs generate lower throughput per model (memory bandwidth is lower than a discrete GPU in absolute terms), and the CUDA-centric fine-tuning ecosystem does not run on Apple Silicon. For inference-only use cases where simplicity and quietness matter, a Mac can beat a dual-GPU PC on total cost of ownership when you factor in electricity and the complexity tax of the dual-GPU setup. See the Mac comparison page for a direct breakdown.
Frequently asked questions
- Is a mining RTX 3090 safe to buy?
- Often yes, but with due diligence. Mining puts sustained load on the GPU, which can accelerate thermal pad degradation and capacitor wear. The GPU silicon itself is typically fine — mining runs at stable loads, not spikes. What to check: ask for photos of the card, verify the cooler is intact, and run a 15–30 minute GPU stress test immediately after receiving it to confirm temperatures are normal. Cards from reputable eBay sellers with return policies significantly reduce the risk.
- Can I run Llama 3 70B on an RTX 3090?
- Not comfortably on a single card. A single RTX 3090 has 24GB of VRAM; Llama 3.3 70B at Q4_K_M needs approximately 43GB. You can load a Q3 or Q2 quantized version of a 70B model on a single 3090, but quality degrades noticeably. For proper 70B inference, two RTX 3090s with an NVLink bridge is the recommended path — combined 48GB at near-single-card speeds.
- RTX 3090 vs RTX 4080 for local AI?
- It depends on the VRAM tier that matters to you. The RTX 4080 has 16GB of VRAM and notably higher bandwidth and compute than the 3090 — for 7B–13B models it is faster. But the RTX 3090's 24GB allows it to fit models the 4080 cannot. If your models are firmly 13B and under, the 4080 is the faster card. If you want headroom for 24B models or a future dual-card 48GB build, the 3090 is the better choice.
Related reading
Performance figures are estimates aggregated from third-party benchmarks — we don't benchmark hardware ourselves. See our methodology and sources.
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