Quick answer: The used RTX 3090 (~$820) is the best GPU under $1,000 for local LLM. Its 24GB VRAM and 936 GB/s bandwidth handle 34B models that no 16GB card can touch. Both cards that used to headline this tier — the RTX 5070 Ti and RTX 5080 — now sell above $1,000 and are listed below only for contrast.
NVIDIA GeForce RTX 3090
24GB GDDR6X24GB VRAM at ~$820 used — the only card under $1,000 that runs 34B models like DeepSeek-R1 32B.
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Under $1000 GPU comparison for LLM
| GPU | VRAM | Bandwidth | Tok/s (13B Q4) | Price | Best For |
|---|---|---|---|---|---|
| RTX 3090 (used) | 24GB | 936 GB/s | ~35 tok/s | ~$820 | Best value, 34B capable |
| RX 7900 XTX | 24GB | 960 GB/s | ~25 tok/s | ~$900 | 24GB new, if ROCm suits you |
| RTX 5070 | 12GB | 672 GB/s | ~20 tok/s | ~$875 | Newest architecture, least VRAM |
| RTX 4070 Ti Super | 16GB | 672 GB/s | ~24 tok/s | ~$800 | Reliable, proven, warranty |
| RTX 5060 Ti 16GB | 16GB | 448 GB/s | ~22 tok/s | ~$630 | Cheapest new 16GB |
| RTX 5070 Ti — over budget | 16GB | 896 GB/s | ~28 tok/s | ~$1,050 | Was the sweet spot until 2026 |
| RTX 5080 — over budget | 16GB | 960 GB/s | ~32 tok/s | ~$1,400 | Fastest 16GB, one tier up |
The $700-1000 tier explained
This budget range is the most interesting in 2026 for LLM users because it creates a real choice: 16GB new vs 24GB used. If you are also weighing whether the RTX 5070 makes sense against the 4090 at this tier, see RTX 5070 vs 4090 for LLM for a direct performance comparison.
The tier also lost its two headline cards this year. The RTX 5070 Ti sat at about $750 and the RTX 5080 at $999; GDDR7 supply moving to AI accelerators pushed them to roughly $1,050 and $1,400. Nothing replaced them at the old prices, so the honest picture below leans harder on the used market than this guide once did.
- 16GB cards (RTX 4070 Ti Super, 5060 Ti 16GB) give you modern architecture, lower power, better efficiency, and warranty — but cap out at 13B-14B models at good quantization
- 24GB cards (used RTX 3090, or the RX 7900 XTX new) give you access to 34B models and comfortable 13B at high quantization — but draw 350W+, and the 3090 has no warranty while the AMD card asks you to live with ROCm
Your decision depends on what models you want to run.
VRAM capacity memory bandwidth Specs are manufacturer figures. Bar lengths are scaled independently per metric.
#1: RTX 3090 (used) — best under $1000
The RTX 3090 dominates this tier for one reason: 24GB VRAM at $820.
What 24GB unlocks that 16GB cannot:
- CodeLlama 34B at Q4_K_M (~20GB) — fits with headroom
- Qwen 2.5 32B at Q4_K_M (~19GB) — comfortable
- DeepSeek-R1 32B at Q4_K_M (~19GB) — runs well
- Llama 2 13B at Q8 (~14.5GB) — near-perfect quality
- Any 7B model at FP16 — full precision, no compromises
The 936 GB/s bandwidth is also excellent — faster than every new card under $1,000 except the RX 7900 XTX, which edges it at 960 GB/s but gives that back to ROCm’s software gap.
The downsides are real: 350W TDP requires a 750W+ PSU, the card runs hot (plan for good case airflow), and used cards carry risk. Buy from reputable sellers with return policies.
Check NVIDIA GeForce RTX 3090 on Amazon→Buy on Shopee SG→#2: RTX 4070 Ti Super — best new card that still fits
At ~$800 the RTX 4070 Ti Super is the strongest new card left under the ceiling:
- 16GB GDDR6X with 672 GB/s bandwidth
- 285W TDP — 65W less than the RTX 3090
- Mature Ada Lovelace drivers, full warranty, no used-market risk
- Around 40 tok/s on a 7B at Q4, and 24 tok/s on a 13B
The limitation is the same one every 16GB card has here: 34B models are out of reach. If you know you will stay within 13B, this is the low-risk buy. If you want to run anything larger, only 24GB gets you there.
Check NVIDIA GeForce RTX 4070 Ti Super on Amazon→Buy on Shopee SG→#3: RX 7900 XTX — 24GB without the used market
The other way to 24GB under $1,000 is AMD’s RX 7900 XTX at ~$900:
- 24GB GDDR6 with 960 GB/s — nominally the most bandwidth in this tier
- New card, full warranty, no mining-wear risk
- Runs Ollama and llama.cpp fine once ROCm is set up, on Linux
The catch is that throughput does not follow the spec sheet: around 25 tok/s on a 13B against the 3090’s 35, because the software stack is less mature. You are paying $80 more than a used 3090 for a warranty and losing roughly a third of the speed. Our ROCm vs CUDA comparison covers where that trade is worth making.
The two cards that left this tier
The RTX 5070 Ti and RTX 5080 headlined earlier versions of this guide at about $750 and $999. They now sell for roughly $1,050 and $1,400, which puts both above the ceiling rather than inside it. Neither got worse — 16GB at 896 and 960 GB/s is still fast — but neither belongs in an under-$1,000 recommendation any more. If your budget can stretch, they are covered in our best GPU for LLM under $1,500 guide.
What can you run under $1000?
Figures below are modelled from memory bandwidth rather than measured — see our methodology for how and why. All three cards fit the budget.
| Model class | RTX 3090 (24GB, used) | RTX 4070 Ti Super (16GB) | RX 7900 XTX (24GB) |
|---|---|---|---|
| 7B at Q4 | ~55 tok/s | ~40 tok/s | ~42 tok/s |
| 13B at Q4 | ~35 tok/s | ~24 tok/s | ~25 tok/s |
| 34B at Q4 | ~20 tok/s | Won’t fit | ~15 tok/s |
The RTX 3090 leads every row it can compete in, and it is the fastest of the two 24GB options despite the 7900 XTX’s higher nominal bandwidth — CUDA’s maturity is the difference.
How to decide
| If you… | Buy this |
|---|---|
| Want to run 34B models | RTX 3090 (used) |
| Want 24GB with a warranty | RX 7900 XTX, if you run Linux |
| Want new hardware and no ROCm | RTX 4070 Ti Super |
| Are watching every dollar | RTX 5060 Ti 16GB |
| Need the lowest power draw | RTX 5060 Ti 16GB (180W) |
Which GPU should you buy under $1000?
- Want to run 34B models like CodeLlama 34B or Qwen 2.5 32B? Get a used RTX 3090 (~$820). No 16GB card can fit these models, and the 24GB VRAM is non-negotiable for this class of model.
- Want new hardware with a warranty? The RTX 4070 Ti Super (~$800) handles every 7B-13B model without asking you to gamble on the used market.
- Want 24GB but not a used card? The RX 7900 XTX (~$900) is the only new route to 24GB here, at the cost of ROCm setup and roughly a third of the throughput.
- Planning to add a second GPU later? Start with the RTX 3090. It becomes an excellent second card alongside a future RTX 5090, giving you 56GB combined VRAM.
Common mistakes to avoid
- Buying a 16GB card when you want to run 34B models. No amount of quantization fits a 34B model into 16GB at usable quality. If 34B is your goal, 24GB is the minimum.
- Working from an older version of this guide. The RTX 5070 Ti and RTX 5080 were the picks here at $750 and $999. Both now sell above $1,000, and a recommendation that quotes those prices is a year out of date.
- Ignoring PSU requirements for the RTX 3090. The 3090 draws 350W under load. If your PSU is under 750W, you need to budget $80-120 for a new one. Factor this into total cost.
- Buying the RX 7900 XTX for its bandwidth number. On paper it beats the 3090 at 960 GB/s. In llama.cpp it lands about a third slower, because ROCm has not caught up with CUDA.
Upgrade path from under $1000
Starting at this tier gives you a clear upgrade path:
- Now: used RTX 3090 (
$820) or RTX 4070 Ti Super ($800) - Next: RTX 5090 ($4,900) for 32GB and 70B at Q2-Q3
- Endgame: Dual GPU or next-gen 48GB+ consumer cards
The RTX 3090 stays useful as a secondary GPU in a dual-card setup, and two of them at roughly $1,640 reach 48GB — enough for 70B at Q4, which is the cheapest route to that class of model.
Wondering how the RTX 5070 Ti stacks up against a used 3090 specifically for LLM inference? See our RTX 5070 Ti vs 3090 for LLM comparison for a head-to-head breakdown. For more options, see our under $500 guide for tighter budgets, our under $300 guide for the absolute floor, our under $1500 guide if you can stretch the budget a bit, or our VRAM requirements guide to match your target model.
At $700-1000, you cross from “can run small models” to “can run most models.” This is the tier where local LLM becomes genuinely useful for productivity.
Frequently Asked Questions
Is a used RTX 4090 worth it for local LLMs?
A used RTX 4090 offers 24GB VRAM and 1,008 GB/s bandwidth, the fastest single consumer GPU for inference. The difficulty in 2026 is price rather than the card: new 4090s sell around $2,200 now that production has ended, and used listings sit close enough to that for the discount to be unreliable. Either way you are above the $1,000 tier. If your budget is firm at $1,000, a used RTX 3090 at around $820 gives you the same 24GB VRAM at lower speed.
RTX 5070 Ti vs RTX 4090 for local LLMs?
The RTX 4090 wins for LLM inference despite being a generation older. Its 24GB VRAM handles 34B models that the 5070 Ti’s 16GB cannot fit at all. The 4090 also has higher memory bandwidth (1,008 GB/s vs 896 GB/s). The 5070 Ti’s advantage is price ($1,050 vs $2,200) and power efficiency (300W vs 450W), though at $1,050 it has itself moved above this guide’s ceiling. Choose the 5070 Ti only if you will stay within 13B models.
Can I run 70B models on a GPU under $1,000?
No, not on a single GPU. 70B models at Q4_K_M quantization require approximately 40GB of VRAM, which exceeds every GPU under $1,000. The cheapest path to 70B is dual RTX 3090s (about $1,640 total used) or renting cloud GPUs on RunPod or Vast.ai for occasional use at under $2 per session.
What’s the best VRAM per dollar GPU for LLMs?
The used RTX 3090 offers the best usable VRAM per dollar at approximately 29GB per $1,000 (24GB for $820). The used RTX 3060 12GB is ahead on the raw ratio at 48GB per $1,000 (12GB for $250), but 12GB caps which models load at all. Among new cards, the RTX 5070 Ti provides 15GB per $1,000 (16GB for $1,050). For pure VRAM-per-dollar, used cards consistently beat new ones.