NVIDIA GeForce RTX 3050 Ti (Mobile)
Mobile-only Ampere with 4 GB VRAM at 192 GB/s. The 4 GB ceiling is the bottleneck — 1-3B Q4 only with no headroom for context. CUDA + Tensor cores work, but VRAM keeps the workload tiny. Common in mid-range gaming laptops from 2021-2022; the operator's honest move is to use CPU offload for anything beyond 3B.
Extrapolated from 192 GB/s bandwidth — 23.0 tok/s estimated. No measured benchmarks yet.
Plain-English: Doesn't fit modern chat models usefully — vision models won't fit.
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.
This card is for the operator who already owns a laptop with a 3050 Ti and wants to run the smallest local models—1B to 3B at Q4—for lightweight chat or code completion. It is not a purchase target; it is a constraint to work around.
At 192 GB/s, the 3050 Ti can push 25-40 tok/s on a 1B Q4 model, but the 4 GB VRAM is the hard ceiling. A 3B Q4 model (2.5 GB weights) fits with minimal context, leaving no room for larger models or substantial conversation history. Anything beyond 3B forces CPU offload, which tanks performance to single-digit tok/s.
What breaks: 7B models are impossible without full CPU offload, and even 3B models choke on long contexts. The mobile form factor means no upgrade path. CUDA and Tensor cores are present but irrelevant when VRAM is the bottleneck.
Pass on this card if you are buying a machine for local AI. The 4 GB VRAM is a dead end for any serious workload. For existing owners, the honest move is to treat the GPU as a coprocessor for tiny models and offload everything else to CPU or a cloud API.
Price/value note: This card is not sold standalone; in a used laptop, the GPU adds negligible value—pay only for the laptop's other features.
›Why this rating
The 4 GB VRAM is the decisive limiter, making this card unsuitable for any model larger than 3B. Even for tiny models, the mobile form factor and lack of upgrade path reduce its utility. It scores low because it fails the primary local AI requirement: fitting useful models with context.
Overview
Mobile-only Ampere with 4 GB VRAM at 192 GB/s. The 4 GB ceiling is the bottleneck — 1-3B Q4 only with no headroom for context. CUDA + Tensor cores work, but VRAM keeps the workload tiny. Common in mid-range gaming laptops from 2021-2022; the operator's honest move is to use CPU offload for anything beyond 3B.
Specs
| VRAM | 4 GB |
| Power draw | 80 W |
| Released | 2021 |
| Backends | CUDA Vulkan |
Models that fit
Open-weight models small enough to run on NVIDIA GeForce RTX 3050 Ti (Mobile) with usable context.
Hardware worth comparing
Same VRAM tier and the one step above and below — so you can frame the buying decision against real options.
- 8.8/10NVIDIA GeForce RTX 3080 16GB (Mobile)nvidia · 16 GB VRAM
- 8.9/10Framework Laptop 16 (RX 7700S)amd · 8 GB VRAM
- 10.0/10MacBook Pro 16" M4 Maxapple · 0 GB VRAM
- 9.3/10Lenovo Legion 5 Pro Gen 7 (RTX 3080 16GB)nvidia · 16 GB VRAM
- 7.3/10NVIDIA GeForce RTX 4090 Mobilenvidia · 16 GB VRAM
- 8.6/10NVIDIA GeForce RTX 5090 Mobilenvidia · 24 GB VRAM
Frequently asked
What models can NVIDIA GeForce RTX 3050 Ti (Mobile) run?
Does NVIDIA GeForce RTX 3050 Ti (Mobile) support CUDA?
Where next?
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify hardware specifications.