NVIDIA RTX 2060 Super - 8GB usable

8GB Turing with tensor cores, so prompts process far quicker than on any Pascal card. 8GB is the hard limit on what you can load.

Specifications

BrandNVIDIA
ModelRTX 2060 Super
Usable VRAM8GB
ArchitectureTuring
CUDA / Stream Processors2,176
Memory Bandwidth448 GB/s
TDP175W
FP32 TFLOPS7.2

Current Offers

Used from $195New from $250

Prices last updated:

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Lowest tracked offers by retailer

US · USD · Weekly since Aug 2026 · Shipping may be extra

  • eBay $195 at 90-day high
  • Amazon $250 current
  • Newegg (no current offer)
$100$200$300$400$500$600Aug 21Sep 8Sep 27Oct 15Nov 3Newegg $599Amazon $250eBay $195
About these prices

Each line is the lowest offer we tracked from that retailer at weekly snapshots, ending at today's offer. The condition can vary between snapshots (new, used, or unrecorded for older ones). eBay prices exclude shipping. Tax is excluded. Amazon shows a current price only.

Weekly offers table
Lowest tracked offer per retailer · US · USD
DateeBayAmazonNewegg
Today, Nov 3, 2026 $195.00$249.97–
Oct 30, 2026 $179.99––
Oct 23, 2026 $189.99––
Oct 9, 2026 $176.99––
Oct 2, 2026 $189.99–$599.00
Sep 25, 2026 $159.99–$599.00
Sep 18, 2026 $163.25–$599.00
Sep 11, 2026 $180.00–$279.99
Sep 4, 2026 $175.01–$279.99
Sep 1, 2026 $157.99–$559.00
Aug 28, 2026 $165.00–$559.00
Aug 21, 2026 $169.99–$599.00

For AI / LLM Use

Limited VRAM restricts you to 7B quantized models.

What Models Can It Run?

  • 7B Q6_K, 14B Q3_K (tight)
  • 7B Q4_K_M only

Estimated Performance

Generation: ~34 tokens/sec

Prefill: ~129 tokens/sec

Recommended Quantisations

  • Q4_K_M for 7B models
  • Q3_K for larger experiments

Pros & Cons

Pros

  • Turing architecture: broad CUDA software support
  • Consumer card: easy to install, display output

Cons

  • Only 8GB usable VRAM: limited to small models
  • Moderate memory bandwidth: not the fastest for inference

Community Verdict

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