RUNLOCALAIv38
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OP·Fredoline Eruo
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Some links on this site are affiliate links (Amazon Associates and other first-class retailers). When you buy through them, we earn a small commission at no extra cost to you. Affiliate links do not influence our verdicts — there are cards we rate highly that we don't have affiliate relationships with, and cards that sell well that we refuse to recommend. Read more →

SYS · ONLINEUPTIME · 100%2026 · operator-owned
RUNLOCALAI · v38
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  4. /NVIDIA GeForce GTX 1660 Super
UNIT · NVIDIA · GPU
6 GB VRAMmid·Reviewed May 2026

NVIDIA GeForce GTX 1660 Super

Turing mid-range with GDDR6 — bandwidth jumps to 336 GB/s vs the base 1660. Same 6 GB VRAM ceiling but ~30-45 tok/s on 7B Q4 thanks to the bandwidth bump. Still no Tensor cores. Strong used-market value in 2026 ($140-160).

Released 2019·~$150 street·336 GB/s memory bandwidth
RUNLOCALAI SCORE
See full leaderboard →
261/ 1000
DD-tier
Estimated
Throughput
117/ 500
VRAM-fit
30/ 200
Ecosystem
200/ 200
Efficiency
26/ 100

Extrapolated from 336 GB/s bandwidth — 40.3 tok/s estimated. No measured benchmarks yet.

WORKLOAD FIT
Try other hardware →

Plain-English: Edge-of-fit for 7B; expect compromises.

7B chat~
Tight
14B chat✗
Doesn't fit
32B chat✗
Doesn't fit
70B chat✗
Doesn't fit
Coding agent✗
Doesn't fit
Vision (≤8B VLM)~
Tight
Long context (32K)✗
Doesn't fit
✓Comfortable — fits with headroom
~Tight — works, no slack
△Marginal — needs aggressive quant
✗Doesn't fit usefully

Verdicts extrapolated from catalog VRAM + bandwidth + ecosystem flags. Hover any chip for the rationale. Want measured numbers? Submit your own run with runlocalai-bench --submit.

BLK · VERDICT

Our verdict

OP · Fredoline Eruo|VERIFIED MAY 10, 2026
2.8/10

This card is for the operator who needs a cheap inference runner for small models and already has a CUDA stack in place. The 6 GB VRAM ceiling and lack of Tensor cores mean it's strictly a budget workhorse, not a platform for serious local AI.

On 7B Q4 models, the 336 GB/s bandwidth delivers roughly 30-45 tok/s — usable for chat and code completion, but not for real-time streaming. 13B Q4 models (9 GB) won't fit at all, and even 8B Q4 models (5.5 GB) leave almost no room for context.

What breaks: anything above 6 GB VRAM. No Tensor cores means no FP8 or INT4 acceleration, so the card relies entirely on CUDA cores for compute. Software stack is limited to CUDA-only runtimes; no ROCm or Vulkan support out of the box.

Pass on this card if the workload includes 13B+ models, long-context inference, or any training/fine-tuning. The 6 GB ceiling is a hard wall, and the lack of Tensor cores makes it obsolete for modern quantization formats.

At $150 used, it's a fair price for a disposable inference node, but a used RTX 3060 12 GB for $180 is a far better long-term investment.

›Why this rating

The GTX 1660 Super earns a 4.5 for its niche as a cheap, low-power inference runner for small models. However, the 6 GB VRAM and missing Tensor cores severely limit its usefulness for modern local AI workloads, making it a poor choice for any serious operator.

BLK · OVERVIEW

Overview

Turing mid-range with GDDR6 — bandwidth jumps to 336 GB/s vs the base 1660. Same 6 GB VRAM ceiling but ~30-45 tok/s on 7B Q4 thanks to the bandwidth bump. Still no Tensor cores. Strong used-market value in 2026 ($140-160).

Retailers we'd check:Amazon

Search-fallback links. Editorial hasn't yet curated retailer URLs for this card. Approx. $150.

Some links above are affiliate links. We may earn a commission at no extra cost to you. How we make money.

BLK · SPECS

Specs

VRAM6 GB
Power draw125 W
Released2019
MSRP$229
Backends
CUDA
Vulkan

Models that fit

Open-weight models small enough to run on NVIDIA GeForce GTX 1660 Super with usable context.

Llama 3.2 3B Instruct
3B · llama
Llama 3.2 1B Instruct
1B · llama
Gemma 4 E2B (Effective 2B)
2B · gemma
Gemma 3 1B
1B · gemma
Qwen 2.5 Coder 3B
3B · qwen
Qwen 2.5 Coder 1.5B
1.5B · qwen
DeepSeek R1 Distill Qwen 1.5B
1.5B · deepseek
Granite 3.0 2B Instruct
2B · granite

Frequently asked

What models can NVIDIA GeForce GTX 1660 Super run?

With 6GB VRAM, the NVIDIA GeForce GTX 1660 Super runs 7B models comfortably in Q4 quantization. See the model list below for tested combinations.

Does NVIDIA GeForce GTX 1660 Super support CUDA?

Yes — NVIDIA GeForce GTX 1660 Super is an NVIDIA card with full CUDA support, the most mature local-AI backend. llama.cpp, Ollama, vLLM, and ExLlamaV2 all run natively.

How much does NVIDIA GeForce GTX 1660 Super cost?

Current street price for NVIDIA GeForce GTX 1660 Super is around $150 (MSRP $229). Prices vary by region and supply.

Where next?

Buyer guides
  • Best GPU for local AI →
  • Best laptop for local AI →
  • Best Mac for local AI →
  • Best used GPU for local AI →
Troubleshooting
  • CUDA out of memory →
  • Ollama running slowly →
  • ROCm not detected →
  • Model keeps crashing →

Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify hardware specifications.

Compare alternatives

Hardware worth comparing

Same VRAM tier and the one step above and below — so you can frame the buying decision against real options.

Same VRAM tier
Cards in the same memory band
  • AMD Radeon RX 5600 XT
    amd · 6 GB VRAM
    1.7/10
  • NVIDIA GeForce RTX 2060
    nvidia · 6 GB VRAM
    2.8/10
  • NVIDIA GeForce GTX 1660 Ti
    nvidia · 6 GB VRAM
    2.8/10
  • AMD Radeon RX 6600 XT
    amd · 8 GB VRAM
    4.8/10
  • AMD Radeon RX 6600
    amd · 8 GB VRAM
    4.8/10
  • Intel Arc B570
    intel · 10 GB VRAM
    5.8/10
Step up
More VRAM — bigger models, more context
  • AMD Radeon RX 6600 XT
    amd · 8 GB VRAM
    4.8/10
  • NVIDIA GeForce GTX 1070 Ti
    nvidia · 8 GB VRAM
    5.1/10
  • Intel Arc B570
    intel · 10 GB VRAM
    5.8/10
Step down
Less VRAM — cheaper, more constrained
  • AMD Radeon RX 580 8GB
    amd · 8 GB VRAM
    3.8/10
  • AMD Radeon RX 5500 XT 8GB
    amd · 8 GB VRAM
    3.5/10
  • NVIDIA GeForce GTX 1650 Super
    nvidia · 4 GB VRAM
    1.8/10