consumer
Thuê Nvidia GTX 1050.
Pascal
2GB VRAM
Where to rent it
All providers carrying this GPU.
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FAQ
Frequently asked.
What AI models can I run on a Nvidia GTX 1050?
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 2GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia GTX 1050?
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 1050 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 1050.
Qwen 2.5 3B
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GTX 1050
Total VRAM
2 GB
Parameters
3B
Context
33K tokens
Qwen2.5 3B Instruct
by Alibaba (Qwen Team)
Required GPUs
1× Nvidia GTX 1050
Total VRAM
2 GB
Parameters
3B
Context
33K tokens
Mistral: Ministral 3 3B 2512
by Mistral AI
Required GPUs
1× Nvidia GTX 1050
Total VRAM
2 GB
Parameters
3B
Context
131K tokens
Meta Llama 3.2 3B Instruct
by Meta AI
Required GPUs
1× Nvidia GTX 1050
Total VRAM
2 GB
Parameters
3B
Context
131K tokens
Gemma 2 2B
by Google DeepMind
Required GPUs
1× Nvidia GTX 1050
Total VRAM
2 GB
Parameters
3B
Context
8K tokens
Stable Diffusion 3.5 Medium
by Stability AI
Required GPUs
1× Nvidia GTX 1050
Total VRAM
2 GB
Parameters
3B
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FAQ
AI models on this GPU.
What AI models can I run on a Nvidia GTX 1050?
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 2GB of VRAM.
What's the VRAM minimum to run a model on the Nvidia GTX 1050?
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 1050 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.