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OP·Fredoline Eruo
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SYS · ONLINEUPTIME · 100%2026 · operator-owned
RUNLOCALAI · v38
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  4. /NVIDIA GeForce GTX 1650 Super
UNIT · NVIDIA · GPU
4 GB VRAMentry·Reviewed May 2026

NVIDIA GeForce GTX 1650 Super

Turing entry refresh with GDDR6. 4 GB VRAM is below the practical AI floor — 1-3B Q4 only. No Tensor cores. Common in pre-built office PCs from 2019-2021 that are now hitting the AI question. The honest answer for this card is usually 'try CPU offload or upgrade.'

Released 2019·~$140 street·192 GB/s memory bandwidth
RUNLOCALAI SCORE
See full leaderboard →
221/ 1000
DD-tier
Estimated
Throughput
67/ 500
VRAM-fit
30/ 200
Ecosystem
200/ 200
Efficiency
18/ 100

Extrapolated from 192 GB/s bandwidth — 23.0 tok/s estimated. No measured benchmarks yet.

WORKLOAD FIT
Try other hardware →

Plain-English: Doesn't fit modern chat models usefully — vision models won't fit.

7B chat✗
Doesn't fit
14B chat✗
Doesn't fit
32B chat✗
Doesn't fit
70B chat✗
Doesn't fit
Coding agent✗
Doesn't fit
Vision (≤8B VLM)✗
Doesn't fit
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
1.8/10

This card is for the operator who already owns a GTX 1650 Super in a pre-built office PC and wants to know if it can run any local AI at all. The answer is marginal: 1-3B Q4 models run at ~20-35 tok/s, usable for simple chat or code completion. 7B models are out of reach due to 4 GB VRAM, though CPU offload with llama.cpp can squeeze a 7B Q2 at ~2-4 tok/s — painful but technically possible. What breaks: anything above 3B parameters, any model requiring 4-bit or higher quantization, and any workload needing Tensor Cores (none present). Operators should pass if they plan to run 7B+ models, want reasonable inference speed, or can spend a bit more on a used RTX 3060 12 GB. At ~$140 used, this card is only worth it if the budget is absolutely zero and the task is strictly 1-3B models.

›Why this rating

The 4 GB VRAM is below the practical floor for most local AI workloads, and the lack of Tensor Cores limits performance even on small models. It barely qualifies for 1-3B Q4 models, but the value is poor compared to similarly priced used options with more VRAM.

BLK · OVERVIEW

Overview

Turing entry refresh with GDDR6. 4 GB VRAM is below the practical AI floor — 1-3B Q4 only. No Tensor cores. Common in pre-built office PCs from 2019-2021 that are now hitting the AI question. The honest answer for this card is usually 'try CPU offload or upgrade.'

Retailers we'd check:Amazon

Search-fallback links. Editorial hasn't yet curated retailer URLs for this card. Approx. $140.

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

BLK · SPECS

Specs

VRAM4 GB
Power draw100 W
Released2019
MSRP$159
Backends
CUDA
Vulkan

Models that fit

Open-weight models small enough to run on NVIDIA GeForce GTX 1650 Super with usable context.

Llama 3.2 1B Instruct
1B · llama
Gemma 4 E2B (Effective 2B)
2B · gemma
Gemma 3 1B
1B · gemma
Qwen 2.5 Coder 1.5B
1.5B · qwen
Moondream 2
1.9B · other
RWKV 7 'Goose' 1.5B
1.5B · rwkv
DeepSeek R1 Distill Qwen 1.5B
1.5B · deepseek
Granite 3.0 2B Instruct
2B · granite
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 5500 XT 8GB
    amd · 8 GB VRAM
    3.5/10
  • AMD Radeon RX 570
    amd · 4 GB VRAM
    1.0/10
  • NVIDIA GeForce GTX 1650
    nvidia · 4 GB VRAM
    1.8/10
  • AMD Radeon RX 5600 XT
    amd · 6 GB VRAM
    1.7/10
  • AMD Radeon RX 6600
    amd · 8 GB VRAM
    4.8/10
  • NVIDIA GeForce GTX 1660
    nvidia · 6 GB VRAM
    2.8/10
Step up
More VRAM — bigger models, more context
  • AMD Radeon RX 5500 XT 8GB
    amd · 8 GB VRAM
    3.5/10
  • AMD Radeon RX 5600 XT
    amd · 6 GB VRAM
    1.7/10
  • NVIDIA GeForce GTX 1660
    nvidia · 6 GB VRAM
    2.8/10
Step down
Less VRAM — cheaper, more constrained
  • AMD Radeon RX 570
    amd · 4 GB VRAM
    1.0/10
  • AMD Radeon RX 580 8GB
    amd · 8 GB VRAM
    3.8/10
  • NVIDIA GeForce GTX 1060 3GB
    nvidia · 3 GB VRAM
    1.1/10

Frequently asked

What models can NVIDIA GeForce GTX 1650 Super run?

With 4GB VRAM, the NVIDIA GeForce GTX 1650 Super runs small models (3B and under) at modest quantization. See the model list below for tested combinations.

Does NVIDIA GeForce GTX 1650 Super support CUDA?

Yes — NVIDIA GeForce GTX 1650 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 1650 Super cost?

Current street price for NVIDIA GeForce GTX 1650 Super is around $140 (MSRP $159). 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.