consumer
Alquilar Nvidia GTX 1650 Super.
Turing
4GB VRAM
Where to rent it
All providers carrying this GPU.
No fresh prices yet.
FAQ
Frequently asked.
What AI models can I run on a Nvidia GTX 1650 Super?
The grid above lists every open-weights model with a recommended GPU configuration for this card. Each row tells you the minimum GPU count and the quantization level (FP16, FP8, INT8, INT4) needed to load the model in 4GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia GTX 1650 Super?
Rule of thumb: a model needs roughly (parameters × bytes-per-weight × 1.2) of VRAM to load, plus headroom for the KV cache during inference. FP16 = 2 bytes/weight, FP8/INT8 = 1 byte, INT4 = 0.5 bytes. A 70B model at FP16 needs ~168GB; at INT4 it drops to ~42GB and fits a single high-VRAM card.
How does quantization (FP16 vs FP8 vs INT4) affect what fits?
Lower-precision quantization shrinks the memory footprint nearly linearly with the bit count. The trade-off is output quality: FP16 is the reference, FP8 is usually indistinguishable for most prompts, INT8 introduces small quality losses, INT4 is noticeably degraded on reasoning-heavy tasks but fine for chat. The badge on each row tells you which level the recommendation assumes.
Can I fine-tune on the Nvidia GTX 1650 Super or only do inference?
Fine-tuning needs 4–8× more VRAM than inference at the same model size — gradients, optimizer state, and activations all live in memory. LoRA / QLoRA cut that overhead dramatically (often 4–10×). The notes column flags whether a row is an inference-only recommendation or includes a fine-tuning path.
Where do these GPU-count recommendations come from?
We curate them from official model cards, community benchmark threads (r/LocalLLaMA, HuggingFace forum), and known-good configurations published by the model makers. Each recommendation has been verified to load at the stated quantization on the listed GPU count — though throughput and context-length still vary by workload.
AI models
INT4
INT4
INT4
INT4
INT4
INT4
Models that run on this GPU.
GPU-count + quantization recommendations covering fine-tuning, inference, and run-it-yourself scenarios on the Nvidia GTX 1650 Super.
Qwen3 0.6B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GTX 1650 Super
Total VRAM
4 GB
Parameters
6B
Context
41K tokens
Qwen3 0.6B Base
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GTX 1650 Super
Total VRAM
4 GB
Parameters
6B
Context
33K tokens
Arize AI Qwen 2 1.5B Instruct
by Togethercomputer
Required GPUs
1× Nvidia GTX 1650 Super
Total VRAM
4 GB
Parameters
5B
Context
33K tokens
Qwen 2 (1.5B)
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GTX 1650 Super
Total VRAM
4 GB
Parameters
5B
Context
33K tokens
Qwen 2 Instruct (1.5B)
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GTX 1650 Super
Total VRAM
4 GB
Parameters
5B
Context
33K tokens
Qwen2.5 1.5B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GTX 1650 Super
Total VRAM
4 GB
Parameters
5B
Context
131K tokens
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FAQ
AI models on this GPU.
What AI models can I run on a Nvidia GTX 1650 Super?
The grid above lists every open-weights model with a recommended GPU configuration for this card. Each row tells you the minimum GPU count and the quantization level (FP16, FP8, INT8, INT4) needed to load the model in 4GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia GTX 1650 Super?
Rule of thumb: a model needs roughly (parameters × bytes-per-weight × 1.2) of VRAM to load, plus headroom for the KV cache during inference. FP16 = 2 bytes/weight, FP8/INT8 = 1 byte, INT4 = 0.5 bytes. A 70B model at FP16 needs ~168GB; at INT4 it drops to ~42GB and fits a single high-VRAM card.
How does quantization (FP16 vs FP8 vs INT4) affect what fits?
Lower-precision quantization shrinks the memory footprint nearly linearly with the bit count. The trade-off is output quality: FP16 is the reference, FP8 is usually indistinguishable for most prompts, INT8 introduces small quality losses, INT4 is noticeably degraded on reasoning-heavy tasks but fine for chat. The badge on each row tells you which level the recommendation assumes.
Can I fine-tune on the Nvidia GTX 1650 Super or only do inference?
Fine-tuning needs 4–8× more VRAM than inference at the same model size — gradients, optimizer state, and activations all live in memory. LoRA / QLoRA cut that overhead dramatically (often 4–10×). The notes column flags whether a row is an inference-only recommendation or includes a fine-tuning path.
Where do these GPU-count recommendations come from?
We curate them from official model cards, community benchmark threads (r/LocalLLaMA, HuggingFace forum), and known-good configurations published by the model makers. Each recommendation has been verified to load at the stated quantization on the listed GPU count — though throughput and context-length still vary by workload.