Best Local AI Models for NVIDIA RTX 3050 Ti / 3050 Laptop (4 GB VRAM)

Q: What is the VRAM and memory bandwidth of the NVIDIA RTX 3050 Ti / 3050 Laptop?

The NVIDIA RTX 3050 Ti / 3050 Laptop features 4 GB of VRAM and a memory bandwidth of 192 GB/s (GDDR6 128-bit). In local language model inference, VRAM determines which model sizes fit on the card, while memory bandwidth dictates how many tokens per second the GPU generates.

The NVIDIA RTX 3050 Ti / 3050 Laptop has 4 GB of VRAM. Here are the popular AI models it can run locally (4,096-token context, ~16.0 GB system RAM assumed), ranked by popularity.

Verdict: Entry Ampere laptop GPUs: with 4 GB of GDDR6 at 192 GB/s, only models up to about 3B–4B fit in VRAM in 4-bit quantization, while 7B–8B models spill into system RAM and slow down sharply. Each laptop maker sets the power limit (35–80 W).

This page also covers the RTX 3050 Laptop GPU: the 4 GB version has the same 128-bit bus and 192 GB/s as the RTX 3050 Ti Laptop, so both reach the same local-LLM generation speed; the Ti's extra cores (2,560 vs 2,048) mainly speed up prompt processing. Some RTX 3050 Laptop models have 6 GB of VRAM on a narrower 96-bit bus, which gives a little more headroom; this page assumes 4 GB.

VRAM & Memory

4 GB

GDDR6 128-bit

Bandwidth

192 GB/s

Ampere

Fits in VRAM

20 models

Zero offload

TDP / Assumed RAM

80W

RAM: 16.0 GB

At a glance — 20 of the 36 most popular models run fully in VRAM on the NVIDIA RTX 3050 Ti / 3050 Laptop (4 GB), with about 32 tokens/s estimated on an 8B model in Q4. A solid pick is Qwen/Qwen2.5-7B-Instruct-GGUF (Q2_K, 3.83 GB, ~68.4 t/s estimated).

The NVIDIA RTX 3050 Ti / 3050 Laptop is built on NVIDIA's Ampere architecture featuring GDDR6 128-bit delivering 192 GB/s of raw memory bandwidth. Entry Ampere laptop GPUs: with 4 GB of GDDR6 at 192 GB/s, only models up to about 3B–4B fit in VRAM in 4-bit quantization, while 7B–8B models spill into system RAM and slow down sharply. Each laptop maker sets the power limit (35–80 W).

Equipped with 4 GB of dedicated VRAM, the NVIDIA RTX 3050 Ti / 3050 Laptop can run 20 popular open-source models completely in GPU memory without offloading. This includes full-speed execution for weights like Qwen2.5-7B-Instruct-GGUF, Qwen3-4B-GGUF, LFM2.5-2.6B-GGUF.

With a memory bandwidth of 192 GB/s, this card can generate tokens at an estimated peak rate of ~32.0 tokens/second on an 8B parameter model (Q4_K_M). Its rated power draw is 80W TDP, so ensure your system's power supply and case ventilation are adequate for sustained local inferencing.

New to this? Read: How much VRAM do you need?

How the NVIDIA RTX 3050 Ti / 3050 Laptop compares with similar cards

Compared with the NVIDIA RTX 4050 Laptop, the NVIDIA RTX 3050 Ti / 3050 Laptop has 2 GB less VRAM (4 vs 6 GB), so fewer models fit fully on the GPU; the same 192 GB/s of memory bandwidth, so the same estimated ~32 tokens/s on an 8B model in Q4; a lower maximum power limit (80 vs 115 W) and an older architecture (Ampere vs Ada Lovelace). Side-by-side comparison
Compared with the NVIDIA RTX 3060 Laptop, the NVIDIA RTX 3050 Ti / 3050 Laptop has 2 GB less VRAM (4 vs 6 GB), so fewer models fit fully on the GPU; about 43% slower token generation (~32 vs ~56 tokens/s on an 8B model in Q4, from 192 vs 336 GB/s) and a lower maximum power limit (80 vs 115 W). Side-by-side comparison
Compared with the NVIDIA RTX 4070 / 4060 Laptop, the NVIDIA RTX 3050 Ti / 3050 Laptop has 4 GB less VRAM (4 vs 8 GB), so fewer models fit fully on the GPU; about 25% slower token generation (~32 vs ~43 tokens/s on an 8B model in Q4, from 192 vs 256 GB/s); a lower maximum power limit (80 vs 115 W) and an older architecture (Ampere vs Ada Lovelace). Side-by-side comparison

Token speeds here are a conservative estimate (memory bandwidth ÷ 6, for an 8B model in Q4 that reads about 5 GB of weights per token, plus overhead); prompt processing depends on GPU compute instead.

20 fit fully in VRAM · 16 run with offload

Only show models that fit fully in VRAM

Model	Size	Quant.	Quality	Memory	Speed~	Verdict
Qwen/Qwen2.5-7B-Instruct-GGUF	7.62B	Q2_K	Low	3.83 GB	68.4 t/s	Fits in VRAM
Qwen/Qwen3-4B-GGUF	4.02B	Q5_0	Very good	3.99 GB	73.0 t/s	Fits in VRAM
LiquidAI/LFM2.5-2.6B-GGUF	2.7B	Q8_0	Excellent	3.54 GB	71.7 t/s	Fits in VRAM
Qwen/Qwen2.5-1.5B-Instruct-GGUF	1.54B	Q8_0	Excellent	2.67 GB	108.8 t/s	Fits in VRAM
Qwen/Qwen2.5-0.5B-Instruct-GGUF	0.49B	GGUF	Excellent	2.03 GB	162.8 t/s	Fits in VRAM
Qwen/Qwen2.5-Coder-7B-Instruct-GGUF	7.62B	Q2_K	Low	3.83 GB	68.4 t/s	Fits in VRAM
janhq/Jan-v3.5-4B-gguf	4.41B	Q4_K_M	Good	3.89 GB	75.9 t/s	Fits in VRAM
LiquidAI/LFM2.5-1.2B-Instruct-GGUF	1.17B	BF16	Excellent	3.03 GB	88.0 t/s	Fits in VRAM
Qwen/Qwen2.5-3B-Instruct-GGUF	3.09B	Q6_K	Excellent	3.54 GB	73.8 t/s	Fits in VRAM
ggml-org/gpt-oss-120b-GGUF	116.83B	BF16	Very low	2.42 GB	129.9 t/s	Fits in VRAM
LiquidAI/LFM2.5-230M-GGUF	0.23B	BF16	Excellent	1.28 GB	446.3 t/s	Fits in VRAM
unsloth/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF	8.0B	Q8_0	Very low	1.23 GB	516.8 t/s	Fits in VRAM
unsloth/Qwen3-1.7B-GGUF	1.72B	Q8_K_XL	Excellent	3.41 GB	88.4 t/s	Fits in VRAM
Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF	1.54B	Q8_0	Excellent	2.67 GB	108.8 t/s	Fits in VRAM
Qwen/Qwen3-0.6B-GGUF	0.75B	Q8_0	Excellent	1.83 GB	322.4 t/s	Fits in VRAM
unsloth/Llama-3.2-3B-Instruct-GGUF	3.21B	Q6_K	Excellent	3.7 GB	78.0 t/s	Fits in VRAM
unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF	8.19B	IQ2_XXS	Very low	3.79 GB	79.2 t/s	Fits in VRAM
bartowski/Llama-3.2-1B-Instruct-GGUF	1.24B	F16	Excellent	3.3 GB	83.1 t/s	Fits in VRAM
bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF	7.62B	Q2_K	Low	3.83 GB	68.4 t/s	Fits in VRAM
LiquidAI/LFM3-1.7B-GGUF	1.73B	Q8_0	Excellent	2.57 GB	111.7 t/s	Fits in VRAM
bartowski/Qwen2.5-32B-Instruct-GGUF	32.76B	Q4_K_S	Good	19.29 GB	2.9 t/s	Offload
Qwen/Qwen3-8B-GGUF	8.19B	Q8_0	Excellent	9.47 GB	6.2 t/s	Offload
unsloth/gpt-oss-20b-GGUF	20.91B	F16	Very good	13.74 GB	3.9 t/s	Offload
unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF	30.53B	Q4_1	Very good	19.05 GB	2.8 t/s	Offload
unsloth/Ornith-1.0-9B-GGUF	—	BF16	Excellent	17.61 GB	3.0 t/s	Offload
bartowski/Meta-Llama-3.1-8B-Instruct-GGUF	8.03B	Q8_0	Excellent	9.23 GB	6.3 t/s	Offload
Qwen/Qwen3-14B-GGUF	14.77B	Q8_0	Excellent	16.05 GB	3.4 t/s	Offload
bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF	34.66B	Q4_0	Good	19.45 GB	2.7 t/s	Offload
unsloth/GLM-4.7-Flash-GGUF	31.22B	Q4_1	Good	18.68 GB	2.8 t/s	Offload
unsloth/flint-GGUF	39.79B	Q3_K_XL	Fair	18.13 GB	2.9 t/s	Offload
unsloth/Qwen-AgentWorld-35B-A3B-GGUF	34.66B	IQ4_NL	Fair	17.75 GB	3.0 t/s	Offload
LiquidAI/LFM2.5-24B-A2B-GGUF	23.97B	Q6_K	Excellent	19.22 GB	2.7 t/s	Offload
ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF	31.58B	Q4_0	Good	18.42 GB	2.8 t/s	Offload
Qwen/Qwen2.5-Coder-14B-Instruct-GGUF	14.77B	Q8_0	Excellent	16.17 GB	3.4 t/s	Offload
bartowski/Qwen2.5-14B-Instruct-GGUF	14.77B	Q8_0	Excellent	16.17 GB	3.4 t/s	Offload
unsloth/Qwen3-Coder-Next-GGUF	79.67B	TQ1_0	Very low	18.82 GB	2.8 t/s	Offload

"Fits in VRAM" = fast, fully on GPU. "Offload" = part on system RAM, slower. Speed is a rough estimate from the NVIDIA RTX 3050 Ti / 3050 Laptop's 192 GB/s memory bandwidth (offloaded models are limited by system RAM).

Frequently asked questions

What is the VRAM and memory bandwidth of the NVIDIA RTX 3050 Ti / 3050 Laptop?

The NVIDIA RTX 3050 Ti / 3050 Laptop features 4 GB of VRAM and a memory bandwidth of 192 GB/s (GDDR6 128-bit). In local language model inference, VRAM determines which model sizes fit on the card, while memory bandwidth dictates how many tokens per second the GPU generates.

What is the best local AI model to run on a NVIDIA RTX 3050 Ti / 3050 Laptop?

The best overall model for the NVIDIA RTX 3050 Ti / 3050 Laptop is Qwen/Qwen2.5-7B-Instruct-GGUF using the recommended Q2_K quantization (3.83 GB total memory). With 4 GB of VRAM, this GPU typically runs smaller models, typically up to about 3–4B at full GPU speed.

What models can you run on an 8 GB GPU like the NVIDIA RTX 3050 Ti / 3050 Laptop?

With 8 GB of VRAM, the NVIDIA RTX 3050 Ti / 3050 Laptop comfortably runs 7B and 8B models (such as Llama 3.1 8B, Mistral 7B, or Qwen 2.5 7B) using Q4_K_M or Q5_K_M quantizations (~5.5 to 6.8 GB). Running 14B models requires offloading memory to system RAM, which lowers token generation speed.