Best Budget GPU for Local LLMs
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Best Budget GPU for Local LLMs

The best budget GPUs for local LLMs ranked by VRAM per dollar. Used RTX 3090 leads at $450 for 24 GB. RTX 4070 Ti Super and RTX 5080 for new-card buyers.

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April 24, 2026

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Maximum VRAM per dollar — the budget local LLM guide

For local LLMs, VRAM is the primary bottleneck. Budget does not mean you have to settle for a bad experience. Used flagship GPUs often deliver more VRAM per dollar than new midrange cards. This guide covers the best budget options from $150 to $1,000, prioritizing VRAM capacity over raw compute performance.

Quick Verdict

Under $500

Used RTX 3090 (24 GB) — The budget king. More VRAM than any new card under $800. CUDA support. Runs warm but performs well for inference.

$500-$1,000

RTX 4070 Ti Super (16 GB) — Best new budget card with 16 GB. CUDA, FP8, reasonable power draw. Limited by 16 GB for larger models.

Stretch Budget

RTX 5080 (16 GB GDDR7) — Fastest 16 GB card with 960 GB/s bandwidth. Best token generation speed in the 16 GB tier.

Budget GPU Tiers for Local LLMs

TierBest PickVRAMModels You Can Run
$150-300Used RTX 3060 12 GB12 GB7B-13B at Q4
$300-500Used RTX 3090 24 GB24 GBUp to 35B at Q4, 70B at Q3
$500-800RX 7900 XTX / RTX 4070 Ti Super24 GB / 16 GBVaries by card
$800-1,000RTX 5080 16 GB16 GB GDDR7Up to 13B at Q8, 34B at Q3

Recommended Budget GPUs

Editor's Pick
GeForce RTX 3090

GeForce RTX 3090

24 GB VRAM — Cheapest 24 GB CUDA

VRAM24 GB GDDR6X
Bandwidth936 GB/s
TDP350 W
Best ForBudget entry to 24 GB CUDA
ReviewPart
GeForce RTX 4070 Ti Super

GeForce RTX 4070 Ti Super

16 GB VRAM — New Budget CUDA

VRAM16 GB GDDR6X
Bandwidth672 GB/s
TDP285 W
Best ForBudget new-build for 7B-13B models
ReviewPart
GeForce RTX 5080

GeForce RTX 5080

16 GB GDDR7 — Fastest Budget 16 GB

VRAM16 GB GDDR7
Bandwidth960 GB/s
TDP360 W
Best For7B-13B models at full speed
ReviewPart
Best Value
GeForce RTX 3090

GeForce RTX 3090

$749.99
View on Amazon
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Key Specifications

VRAM24 GB GDDR6X
Bandwidth936 GB/s
ArchitectureAmpere
PSU750 W recommended

The RTX 3090 is the cheapest way to get 24 GB of VRAM with CUDA support. On the used market it costs a fraction of the 4090 while offering the same VRAM capacity. For builders who want to run larger models and cannot justify the cost of a new GPU, the 3090 is the entry ticket to 24 GB inference.

At 936 GB/s bandwidth it is slightly slower than the 4090 and 7900 XTX, but the difference in token generation speed is modest — typically 10-15% slower for the same model. You still get CUDA, you still get 24 GB, and the Ampere architecture supports Flash Attention and most quantization formats through llama.cpp and ExLlamaV2.

The main compromises are generational. Ampere lacks FP8 support (that is an Ada Lovelace and Blackwell feature), so you lose one potential speedup for quantized inference. The 3090 also draws 350 W and runs warm, especially on reference coolers. An aftermarket model with a good cooler is worth the small price premium on the used market.

If you are experimenting with local LLMs and want to see what 24 GB VRAM unlocks without spending GPU-launch money, the used 3090 is the lowest-risk option. It handles everything from 7B to 35B models on GPU, and even 70B models with partial offloading. Just make sure the card you buy has been tested and has clean VRAM.

Why it wins

  • Cheapest 24 GB VRAM card with CUDA support
  • Runs all major inference frameworks without issue
  • Good enough bandwidth for comfortable inference speeds
  • Ampere architecture still well-supported

Skip if

  • No FP8 support — misses a quantization speedup
  • Ampere is two generations behind Blackwell
  • Runs warm; needs good case cooling
  • Used market risks: no warranty, potential wear
Best New Budget
GeForce RTX 4070 Ti Super

GeForce RTX 4070 Ti Super

$799.99
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Key Specifications

VRAM16 GB GDDR6X
Bandwidth672 GB/s
ArchitectureAda Lovelace
PSU700 W recommended

The RTX 4070 Ti Super is the cheapest new NVIDIA GPU that makes sense for local LLMs. At 16 GB GDDR6X with 672 GB/s bandwidth, it targets the same model range as the RTX 5080 (7B-13B models) but at a significantly lower price. If you are building a new system for local LLMs and your budget does not stretch to $999, this is where you land.

The 4070 Ti Super gets you into the Ada Lovelace generation with FP8 support, DLSS 3, and good power efficiency at 285 W. For inference specifically, FP8 is the feature that matters — it allows certain quantized models to run faster than they would on Ampere cards like the 3090, even though the 3090 has more VRAM.

Bandwidth is the limitation. At 672 GB/s it is noticeably slower than the 5080 (960 GB/s) or 4090 (1,008 GB/s). Token generation speeds for the same model will be lower. For smaller models (7B) this difference is less noticeable, but for 13B models the slower bandwidth becomes more apparent.

This card makes the most sense for someone building a new workstation who wants CUDA support, does not need to run 70B models, and wants to keep the total GPU cost reasonable. Pair it with 32 GB of system RAM and you can even offload larger models, albeit at reduced speed.

Why it wins

  • Cheapest new NVIDIA GPU that is viable for local LLMs
  • FP8 support from Ada Lovelace generation
  • Low 285 W power draw — easy on PSUs and cooling
  • Great for 7B-13B models at comfortable speeds

Skip if

  • Only 16 GB VRAM — cannot run models above ~14B fully on GPU
  • 672 GB/s bandwidth is slowest in this comparison
  • Not competitive with used 24 GB cards for large models
Best Performance
GeForce RTX 5080

GeForce RTX 5080

$999.99
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Key Specifications

VRAM16 GB GDDR7
Bandwidth960 GB/s
ArchitectureBlackwell
PSU850 W recommended

The RTX 5080 hits the price-performance sweet spot for local LLMs. At 16 GB GDDR7 with 960 GB/s bandwidth, it runs 7B models at or near their full potential and handles 13B models at 4-bit quantization comfortably. If your workflow centers on Llama 3.1 8B, Mistral 7B, or Phi-3 medium, this card delivers without the premium tax of the 5090.

GDDR7 memory is the key upgrade over the previous generation. The bandwidth is competitive with the RTX 4090 despite having less total VRAM, which means token generation speeds for models that fit in 16 GB are very fast. You are not sacrificing speed — you are sacrificing capacity.

Power draw is reasonable at 360 W with an 850 W PSU recommendation. That is within the comfort zone of most modern PSUs and cases, unlike the 5090 which needs a significant power infrastructure upgrade for many builders.

The limitation is 16 GB of VRAM. Models like Llama 3.1 70B at 4-bit quantization need roughly 38 GB, which does not fit. You can still run it with offloading to system RAM, but inference speed drops significantly. If your goal is running the largest models locally, step up to the 5090 or consider a used 24 GB card.

Why it wins

  • Best price-to-performance for 7B-13B model inference
  • GDDR7 bandwidth competitive with much more expensive cards
  • Reasonable 360 W power draw — no PSU upgrade needed for most
  • Full CUDA and Blackwell feature set

Skip if

  • 16 GB VRAM limits you to models under ~14B at full precision
  • Cannot run 70B-class models without CPU offloading
  • Less future-proof than 24 GB or 32 GB alternatives

What You Give Up on a Budget

Under $500: You are limited to used cards or 12 GB new. The RTX 3090 at ~$400-500 used is the standout value, but you get no warranty, no FP8, and higher power draw. New cards in this range top out at 12 GB (RTX 4070, RTX 5070), which limits you to 7B-13B models.

$500-$1,000: You can get 16 GB new (RTX 4070 Ti Super, RTX 5080) or 24 GB used (RTX 4090). The trade-off is warranty and FP8 support versus raw VRAM capacity. For local LLMs, VRAM usually wins — the used RTX 4090 runs larger models that new 16 GB cards simply cannot.

What to avoid: Do not buy an 8 GB card for local LLMs. Even 7B models at Q4 leave almost no room for context. Avoid new cards under $300 — they all have 8-12 GB and will bottleneck you immediately.

When used is smarter: Almost always for budget builds. The used RTX 3090 and RTX 4090 offer VRAM that no new card matches at the same price. Test thoroughly on arrival and buy from sellers with return policies.

Related Guides

Used RTX 3090 vs New Midrange

24 GB used vs 16 GB new — which is the smarter buy?

Best GPU Under $500

Maximum LLM performance on a tight budget.

Best GPU Under $800

Used 24 GB vs new 16 GB at the midrange crossover.

Best Used GPUs for LLMs

Used flagships with buying checklist and tips.

Frequently Asked Questions

What is the absolute cheapest GPU for local LLMs?
A used RTX 3060 12 GB (~$150-200) is the cheapest GPU that provides a meaningful local LLM experience. It can run 7B models at Q4 with context. Below 12 GB, expect heavy compromises on model size and context length.
Is the RTX 3090 worth it for budget LLM use?
Absolutely. A used RTX 3090 at $400-500 gives you 24 GB of VRAM — more than any new card under $800. For inference at 4-bit quantization, it performs nearly as well as much more expensive options.
Can I use a 12 GB GPU for local LLMs?
Yes, with limits. 7B models at Q4 fit comfortably (~4 GB). 13B models at Q4 need ~7 GB. Anything larger requires CPU offloading. 12 GB is viable for coding assistants and smaller chat models.
Should I buy new or used for budget LLM builds?
Used is almost always better value for LLMs. VRAM is the primary bottleneck, and used flagship cards (RTX 3090, RTX 4090) offer more VRAM per dollar than any new midrange card.

Looking for specific GPU recommendations? Our main guide covers every budget and VRAM tier.

Best GPU for Local LLMs →

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