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 Mobile architecture featuring GDDR6 delivering 384 GB/s of raw memory bandwidth. NVIDIA RTX 5070 Ti Laptop with 12 GB VRAM for local language model execution.
Equipped with 12 GB of dedicated VRAM, the NVIDIA RTX 5070 Ti Laptop can run 30 popular open-source models completely in GPU memory without offloading. This includes full-speed execution for weights like Qwen3-Coder-30B-A3B-Instruct-GGUF, LFM2.5-2.6B-GGUF, LFM2.5-230M-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 384 GB/s, this card can generate tokens at an estimated peak rate of ~64.0 tokens/second on an 8B parameter model (Q4_K_M). Its rated power draw is 150W TDP, so ensure your system's power supply and case ventilation are adequate for sustained local inferencing.
30 fit fully in VRAM · 6 run with offload
| Model | Size | Quant. | Quality | Memory | Speed~ | Verdict |
|---|---|---|---|---|---|---|
| unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF | 30.53B | Q2_K_L | Low |
11.73 GB
|
37.9 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-2.6B-GGUF | 2.7B | BF16 | Excellent |
5.89 GB
|
79.5 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-230M-GGUF | 0.23B | BF16 | Excellent |
1.28 GB
|
929.9 t/s | Fits in VRAM |
| unsloth/Ornith-1.0-9B-GGUF | — | Q8_0 | Excellent |
9.8 GB
|
45.1 t/s | Fits in VRAM |
| unsloth/gpt-oss-20b-GGUF | 20.91B | Q4_K_XL | Good |
11.95 GB
|
36.2 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-8B-A1B-GGUF | 8.47B | Q8_0 | Excellent |
9.24 GB
|
47.7 t/s | Fits in VRAM |
| unsloth/Qwen3-4B-GGUF | 4.02B | BF16 | Excellent |
8.86 GB
|
53.3 t/s | Fits in VRAM |
| bartowski/DeepSeek-Coder-V2-Lite-Instruct-GGUF | 15.71B | Q5_K_S | Very good |
11.85 GB
|
38.5 t/s | Fits in VRAM |
| unsloth/Qwen-AgentWorld-35B-A3B-GGUF | 34.66B | IQ2_M | Low |
11.65 GB
|
37.1 t/s | Fits in VRAM |
| Qwen/Qwen3-8B-GGUF | 8.19B | Q8_0 | Excellent |
9.47 GB
|
49.3 t/s | Fits in VRAM |
| bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF | 34.66B | IQ2_S | Very low |
11.13 GB
|
39.0 t/s | Fits in VRAM |
| LiquidAI/LFM2.5-1.2B-Instruct-GGUF | 1.17B | BF16 | Excellent |
3.03 GB
|
183.3 t/s | Fits in VRAM |
| Qwen/Qwen2.5-3B-Instruct-GGUF | 3.09B | GGUF | Excellent |
7.27 GB
|
63.2 t/s | Fits in VRAM |
| bartowski/Meta-Llama-3.1-8B-Instruct-GGUF | 8.03B | Q8_0 | Excellent |
9.23 GB
|
50.3 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-0.6B-GGUF | 0.75B | GGUF | Excellent |
2.64 GB
|
284.6 t/s | Fits in VRAM |
| Qwen/Qwen2.5-Coder-7B-Instruct-GGUF | 7.62B | Q8_0 | Excellent |
8.56 GB
|
53.0 t/s | Fits in VRAM |
| janhq/Jan-v3.5-4B-gguf | 4.41B | GGUF | Excellent |
9.59 GB
|
48.6 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-14B-GGUF | 14.77B | Q5_K_M | Very good |
11.22 GB
|
40.8 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-1.7B-GGUF | 2.03B | GGUF | Excellent |
5.03 GB
|
105.5 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-30B-A3B-GGUF | 30.53B | Q2_K | Low |
11.66 GB
|
38.1 t/s | Fits in VRAM |
| unsloth/Llama-3.2-3B-Instruct-GGUF | 3.21B | F16 | Excellent |
7.23 GB
|
66.8 t/s | Fits in VRAM |
| MaziyarPanahi/Yi-Coder-9B-Chat-GGUF | 8.83B | Q5_K_M | Very good |
7.0 GB
|
68.6 t/s | Fits in VRAM |
| Qwen/Qwen2.5-1.5B-Instruct-GGUF | 1.54B | GGUF | Excellent |
4.23 GB
|
120.6 t/s | Fits in VRAM |
| Qwen/Qwen2.5-0.5B-Instruct-GGUF | 0.49B | GGUF | Excellent |
2.03 GB
|
339.1 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-4B-Instruct-2507-GGUF | 4.02B | GGUF | Excellent |
8.86 GB
|
53.3 t/s | Fits in VRAM |
| bartowski/Qwen2.5-7B-Instruct-GGUF | 7.62B | Q8_0 | Excellent |
8.56 GB
|
53.0 t/s | Fits in VRAM |
| MaziyarPanahi/Yi-Coder-1.5B-Chat-GGUF | 1.48B | GGUF | Excellent |
4.3 GB
|
145.4 t/s | Fits in VRAM |
| MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF | 8.03B | Q8_0 | Excellent |
9.23 GB
|
50.3 t/s | Fits in VRAM |
| MaziyarPanahi/Phi-3.5-mini-instruct-GGUF | 3.82B | Q8_0 | Excellent |
6.08 GB
|
105.8 t/s | Fits in VRAM |
| MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF | 7.25B | Q8_0 | Excellent |
8.47 GB
|
55.8 t/s | Fits in VRAM |
| ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF | 31.58B | Q4_0 | Good |
18.42 GB
|
2.8 t/s | Offload |
| MaziyarPanahi/Qwen3-32B-GGUF | 32.76B | Q6_K | Excellent |
26.84 GB
|
2.0 t/s | Offload |
| bartowski/Qwen_Qwen3-Next-80B-A3B-Thinking-GGUF | 81.32B | Q2_K_L | Low |
27.69 GB
|
1.9 t/s | Offload |
| unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF | 70.55B | IQ3_XXS | Low |
27.81 GB
|
1.9 t/s | Offload |
| unsloth/Qwen3-Coder-Next-GGUF | 79.67B | IQ3_XXS | Low |
27.7 GB
|
1.9 t/s | Offload |
| bartowski/Hermes-3-Llama-3.1-70B-GGUF | 70.55B | Q2_K_L | Low |
27.57 GB
|
2.0 t/s | Offload |
"Fits in VRAM" = fast, fully on GPU. "Offload" = part on system RAM, slower. Speed is a rough estimate.
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 384 GB/s (GDDR6). 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 unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF using the recommended Q2_K_L quantization (11.73 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.
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