NVIDIA RTX 4070 Ti vs NVIDIA GTX 1650
Which graphics card is better for running local language models? Here is how the NVIDIA RTX 4070 Ti (12 GB) compares against the NVIDIA GTX 1650 (4 GB) in real memory headroom, supported model architectures, and generation speed.
NVIDIA · Ada Lovelace
12 GB VRAM
NVIDIA RTX 4070 Ti
- Memory Bandwidth: 504 GB/s (GDDR6X 192-bit)
- Fits fully in VRAM: 33 popular models
- Power Draw (TDP): 285W
- Largest recommended (Q4): 14B
NVIDIA · Desktop
4 GB VRAM
NVIDIA GTX 1650
- Memory Bandwidth: 128 GB/s (GDDR6)
- Fits fully in VRAM: 21 popular models
- Power Draw (TDP): 150W
- Largest recommended (Q4): <4B
The AI Local Check Verdict: Which GPU should you buy for AI?
The NVIDIA RTX 4070 Ti is the clear winner for local AI. It provides both 8 GB more VRAM and higher memory bandwidth (504 GB/s vs 128 GB/s).
This allows it to run larger model architectures entirely in video memory while also delivering faster token generation speeds on models of all sizes.
Models that fit on NVIDIA RTX 4070 Ti, but NOT on NVIDIA GTX 1650
These models run entirely in GPU VRAM on the NVIDIA RTX 4070 Ti, but require slow system RAM offload on the NVIDIA GTX 1650:
| Model | Size | Downloads | NVIDIA RTX 4070 Ti | NVIDIA GTX 1650 |
|---|---|---|---|---|
| unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF | 30.53B | 10,762,547 | Fits in VRAM | Offload (Slow) |
| LiquidAI/LFM2.5-8B-A1B-GGUF | 8.47B | 572,846 | Fits in VRAM | Offload (Slow) |
| unsloth/gpt-oss-20b-GGUF | 20.91B | 501,909 | Fits in VRAM | Offload (Slow) |
| Qwen/Qwen3-8B-GGUF | 8.19B | 498,793 | Fits in VRAM | Offload (Slow) |
| unsloth/Ornith-1.0-9B-GGUF | 0.0B | 473,824 | Fits in VRAM | Offload (Slow) |
| bartowski/DeepSeek-Coder-V2-Lite-Instruct-GGUF | 15.71B | 454,533 | Fits in VRAM | Offload (Slow) |
| unsloth/Qwen-AgentWorld-35B-A3B-GGUF | 34.66B | 373,970 | Fits in VRAM | Offload (Slow) |
| bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF | 34.66B | 360,510 | Fits in VRAM | Offload (Slow) |
| bartowski/Meta-Llama-3.1-8B-Instruct-GGUF | 8.03B | 327,315 | Fits in VRAM | Offload (Slow) |
| MaziyarPanahi/Qwen3-14B-GGUF | 14.77B | 271,982 | Fits in VRAM | Offload (Slow) |
| MaziyarPanahi/Qwen3-30B-A3B-GGUF | 30.53B | 260,445 | Fits in VRAM | Offload (Slow) |
| bartowski/Qwen2.5-32B-Instruct-GGUF | 32.76B | 216,296 | Fits in VRAM | Offload (Slow) |
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