Best Local AI Models for NVIDIA RTX 5070 Ti Laptop (12 GB VRAM)
The NVIDIA RTX 5070 Ti Laptop has 12 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.
See also: Best GPU for running local LLMs.
The NVIDIA RTX 5070 Ti Laptop is built on NVIDIA's Blackwell architecture featuring GDDR7 192-bit delivering 672 GB/s of raw memory bandwidth. With 12 GB of GDDR7, 7B–12B models fit entirely in VRAM in 4-bit quantization, 14B is borderline once the context grows, and larger models spill into system RAM. Each laptop maker sets the power limit (60–115 W).
Equipped with 12 GB of dedicated VRAM, the NVIDIA RTX 5070 Ti Laptop can run 33 popular open-source models completely in GPU memory without offloading. This includes full-speed execution for weights like Qwen2.5-32B-Instruct-GGUF, Qwen3-8B-GGUF, Qwen2.5-7B-Instruct-GGUF. For a comprehensive breakdown of compatible model weights, see our guide to the best LLMs for 12 GB VRAM.
With a memory bandwidth of 672 GB/s, this card can generate tokens at an estimated peak rate of ~112.0 tokens/second on an 8B parameter model (Q4_K_M). Its rated power draw is 115W TDP, so ensure your system's power supply and case ventilation are adequate for sustained local inferencing.
How the NVIDIA RTX 5070 Ti Laptop compares with similar cards
- Compared with the NVIDIA RTX 4080 Laptop, the NVIDIA RTX 5070 Ti Laptop has the same 12 GB of VRAM, so the same models fit fully on the GPU; about 56% faster token generation (~112 vs ~72 tokens/s on an 8B model in Q4, from 672 vs 432 GB/s); a lower maximum power limit (115 vs 150 W) and a newer architecture (Blackwell vs Ada Lovelace). Side-by-side comparison
- Compared with the NVIDIA RTX 4090 Laptop, the NVIDIA RTX 5070 Ti Laptop has 4 GB less VRAM (12 vs 16 GB), so fewer models fit fully on the GPU; about 17% faster token generation (~112 vs ~96 tokens/s on an 8B model in Q4, from 672 vs 576 GB/s); a lower maximum power limit (115 vs 150 W) and a newer architecture (Blackwell vs Ada Lovelace). Side-by-side comparison
- Compared with the NVIDIA RTX 3080 Ti Laptop, the NVIDIA RTX 5070 Ti Laptop has 4 GB less VRAM (12 vs 16 GB), so fewer models fit fully on the GPU; about 31% faster token generation (~112 vs ~85 tokens/s on an 8B model in Q4, from 672 vs 512 GB/s); a lower maximum power limit (115 vs 150 W) and a newer architecture (Blackwell vs Ampere). 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.
33 fit fully in VRAM · 3 run with offload
| Model | Size | Quant. | Quality | Memory | Speed~ | Verdict |
|---|---|---|---|---|---|---|
| bartowski/Qwen2.5-32B-Instruct-GGUF | 32.76B | IQ2_S | Very low |
11.47 GB
|
69.5 t/s | Fits in VRAM |
| Qwen/Qwen3-8B-GGUF | 8.19B | Q8_0 | Excellent |
9.47 GB
|
82.8 t/s | Fits in VRAM |
| Qwen/Qwen2.5-7B-Instruct-GGUF | 7.62B | Q8_0 | Excellent |
8.56 GB
|
89.1 t/s | Fits in VRAM |
| Qwen/Qwen3-4B-GGUF | 4.02B | Q8_0 | Excellent |
5.35 GB
|
168.6 t/s | Fits in VRAM |
| unsloth/gpt-oss-20b-GGUF | 20.91B | Q4_K_XL | Good |
11.95 GB
|
60.8 t/s | Fits in VRAM |
| unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF | 30.53B | Q2_K_L | Low |
11.73 GB
|
63.7 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-2.6B-GGUF | 2.7B | BF16 | Excellent |
5.89 GB
|
133.5 t/s | Fits in VRAM |
| Qwen/Qwen2.5-1.5B-Instruct-GGUF | 1.54B | GGUF | Excellent |
4.23 GB
|
202.7 t/s | Fits in VRAM |
| Qwen/Qwen2.5-0.5B-Instruct-GGUF | 0.49B | GGUF | Excellent |
2.03 GB
|
569.8 t/s | Fits in VRAM |
| unsloth/Ornith-1.0-9B-GGUF | — | Q8_0 | Excellent |
9.8 GB
|
75.7 t/s | Fits in VRAM |
| bartowski/Meta-Llama-3.1-8B-Instruct-GGUF | 8.03B | Q8_0 | Excellent |
9.23 GB
|
84.5 t/s | Fits in VRAM |
| Qwen/Qwen3-14B-GGUF | 14.77B | Q5_K_M | Very good |
11.22 GB
|
68.6 t/s | Fits in VRAM |
| Qwen/Qwen2.5-Coder-7B-Instruct-GGUF | 7.62B | Q8_0 | Excellent |
8.56 GB
|
89.1 t/s | Fits in VRAM |
| janhq/Jan-v3.5-4B-gguf | 4.41B | GGUF | Excellent |
9.59 GB
|
81.7 t/s | Fits in VRAM |
| bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF | 34.66B | IQ2_S | Very low |
11.13 GB
|
65.5 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-1.2B-Instruct-GGUF | 1.17B | BF16 | Excellent |
3.03 GB
|
307.9 t/s | Fits in VRAM |
| Qwen/Qwen2.5-3B-Instruct-GGUF | 3.09B | GGUF | Excellent |
7.27 GB
|
106.1 t/s | Fits in VRAM |
| ggml-org/gpt-oss-120b-GGUF | 116.83B | BF16 | Very low |
2.42 GB
|
454.7 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-230M-GGUF | 0.23B | BF16 | Excellent |
1.28 GB
|
1562.2 t/s | Fits in VRAM |
| unsloth/GLM-4.7-Flash-GGUF | 31.22B | Q2_K_L | Low |
11.64 GB
|
63.2 t/s | Fits in VRAM |
| unsloth/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF | 8.0B | Q8_K_XL | Excellent |
8.48 GB
|
88.1 t/s | Fits in VRAM |
| unsloth/Qwen3-1.7B-GGUF | 1.72B | BF16 | Excellent |
4.45 GB
|
209.3 t/s | Fits in VRAM |
| Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF | 1.54B | Q8_0 | Excellent |
2.67 GB
|
380.9 t/s | Fits in VRAM |
| unsloth/flint-GGUF | 39.79B | IQ2_XXS | Very low |
11.95 GB
|
60.9 t/s | Fits in VRAM |
| unsloth/Qwen-AgentWorld-35B-A3B-GGUF | 34.66B | IQ2_M | Low |
11.65 GB
|
62.4 t/s | Fits in VRAM |
| Qwen/Qwen3-0.6B-GGUF | 0.75B | Q8_0 | Excellent |
1.83 GB
|
1128.4 t/s | Fits in VRAM |
| Qwen/Qwen2.5-Coder-14B-Instruct-GGUF | 14.77B | Q5_K_M | Very good |
11.34 GB
|
68.7 t/s | Fits in VRAM |
| unsloth/Llama-3.2-3B-Instruct-GGUF | 3.21B | F16 | Excellent |
7.23 GB
|
112.2 t/s | Fits in VRAM |
| bartowski/Qwen2.5-14B-Instruct-GGUF | 14.77B | Q5_K_L | Very good |
11.78 GB
|
65.7 t/s | Fits in VRAM |
| unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF | 8.19B | Q8_K_XL | Excellent |
11.44 GB
|
66.7 t/s | Fits in VRAM |
| bartowski/Llama-3.2-1B-Instruct-GGUF | 1.24B | F16 | Excellent |
3.3 GB
|
291.0 t/s | Fits in VRAM |
| bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF | 7.62B | Q8_0 | Excellent |
8.56 GB
|
89.1 t/s | Fits in VRAM |
| LiquidAI/LFM3-1.7B-GGUF | 1.73B | BF16 | Excellent |
4.08 GB
|
208.1 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-24B-A2B-GGUF | 23.97B | Q8_0 | Excellent |
24.62 GB
|
2.1 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 |
| unsloth/Qwen3-Coder-Next-GGUF | 79.67B | IQ3_XXS | Low |
27.7 GB
|
1.9 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 5070 Ti Laptop's 672 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 5070 Ti Laptop?
The NVIDIA RTX 5070 Ti Laptop features 12 GB of VRAM and a memory bandwidth of 672 GB/s (GDDR7 192-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 5070 Ti Laptop?
The best overall model for the NVIDIA RTX 5070 Ti Laptop is bartowski/Qwen2.5-32B-Instruct-GGUF using the recommended IQ2_S quantization (11.47 GB total memory). With 12 GB of VRAM, this GPU typically runs a 14B model at Q5, comfortably at full GPU speed. Check our guide to the best LLMs for 12 GB VRAM for details.
Can the NVIDIA RTX 5070 Ti Laptop run 14B models locally?
Yes. The NVIDIA RTX 5070 Ti Laptop's 12 GB VRAM is the exact sweet spot for running 14B models (such as Qwen 2.5 Coder 14B) in Q4_K_M quantization (~9.2 GB footprint) with full 4,096-token context entirely in VRAM.
NVIDIA RTX 5070 Ti Laptop Head-to-Head Comparisons
Compare specs, memory bandwidth, and AI model capability against other graphics cards.