Best Local AI Models for NVIDIA RTX 5070 Ti (16 GB VRAM)
The NVIDIA RTX 5070 Ti has 16 GB of VRAM. Here are the popular AI models it can run locally (4,096-token context, ~32.0 GB system RAM assumed), ranked by popularity.
See also: Best GPU for running local LLMs.
The NVIDIA RTX 5070 Ti is built on NVIDIA's Blackwell architecture featuring GDDR7 256-bit delivering 896 GB/s of raw memory bandwidth. Efficient 16 GB performer: ideal for running quantized 14B Q8 and 32B models locally.
Equipped with 16 GB of dedicated VRAM, the NVIDIA RTX 5070 Ti can run 34 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, LFM2.5-8B-A1B-GGUF. For a comprehensive breakdown of compatible model weights, see our guide to the best LLMs for 16 GB VRAM.
With a memory bandwidth of 896 GB/s, this card can generate tokens at an estimated peak rate of ~149.3 tokens/second on an 8B parameter model (Q4_K_M). Its rated power draw is 300W TDP, so ensure your system's power supply and case ventilation are adequate for sustained local inferencing.
34 fit fully in VRAM · 3 run with offload
| Model | Size | Quant. | Quality | Memory | Speed~ | Verdict |
|---|---|---|---|---|---|---|
| unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF | 30.53B | Q3_K_M | Fair |
14.88 GB
|
29.2 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/LFM2.5-8B-A1B-GGUF | 8.47B | Q8_0 | Excellent |
9.24 GB
|
47.7 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/gpt-oss-20b-GGUF | 20.91B | F16 | Very good |
13.74 GB
|
31.1 t/s | Fits in VRAM |
| Qwen/Qwen3-8B-GGUF | 8.19B | Q8_0 | Excellent |
9.47 GB
|
49.3 t/s | Fits in VRAM |
| unsloth/Ornith-1.0-9B-GGUF | — | Q8_K_XL | Excellent |
13.02 GB
|
33.1 t/s | Fits in VRAM |
| bartowski/DeepSeek-Coder-V2-Lite-Instruct-GGUF | 15.71B | Q6_K_L | Excellent |
15.03 GB
|
29.5 t/s | Fits in VRAM |
| unsloth/Qwen3-4B-GGUF | 4.02B | BF16 | Excellent |
8.86 GB
|
53.3 t/s | Fits in VRAM |
| unsloth/Qwen-AgentWorld-35B-A3B-GGUF | 34.66B | IQ3_S | Fair |
14.83 GB
|
28.7 t/s | Fits in VRAM |
| bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF | 34.66B | Q3_K_M | Fair |
15.99 GB
|
26.5 t/s | Fits in VRAM |
| Qwen/Qwen3-0.6B-GGUF | 0.75B | Q8_0 | Excellent |
1.83 GB
|
671.7 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 |
| LiquidAI/LFM2.5-1.2B-Instruct-GGUF | 1.17B | BF16 | Excellent |
3.03 GB
|
183.3 t/s | Fits in VRAM |
| Qwen/Qwen2.5-Coder-7B-Instruct-GGUF | 7.62B | GGUF | Excellent |
15.21 GB
|
28.2 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 | Q6_K | Excellent |
12.71 GB
|
35.4 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 | Q3_K_L | Fair |
15.98 GB
|
27.0 t/s | Fits in VRAM |
| MaziyarPanahi/Qwen3-32B-GGUF | 32.76B | Q2_K | Low |
13.3 GB
|
34.8 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 |
| MaziyarPanahi/Yi-Coder-9B-Chat-GGUF | 8.83B | Q5_K_M | Very good |
7.0 GB
|
68.6 t/s | Fits in VRAM |
| bartowski/Qwen2.5-32B-Instruct-GGUF | 32.76B | IQ3_M | Fair |
15.59 GB
|
29.0 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 |
| Qwen/Qwen2.5-0.5B-Instruct-GGUF | 0.49B | GGUF | Excellent |
2.03 GB
|
339.1 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/Qwen2.5-7B-Instruct-GGUF | 7.62B | F16 | Excellent |
15.21 GB
|
28.2 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/Meta-Llama-3-8B-Instruct-GGUF | 8.03B | Q8_0 | Excellent |
9.23 GB
|
50.3 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/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 | GGUF | Excellent |
14.8 GB
|
29.6 t/s | Fits in VRAM |
| MaziyarPanahi/gemma-3-4b-it-GGUF | 4.3B | GGUF | Excellent |
8.38 GB
|
55.3 t/s | Fits in VRAM |
| MaziyarPanahi/Llama-3.2-1B-Instruct-GGUF | 1.24B | GGUF | Excellent |
3.3 GB
|
173.2 t/s | Fits in VRAM |
| ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF | 31.58B | Q8_0 | Excellent |
32.1 GB
|
1.6 t/s | Offload |
| unsloth/Qwen3-Coder-Next-GGUF | 79.67B | Q4_1 | Very good |
47.79 GB
|
1.1 t/s | Offload |
| MaziyarPanahi/Mixtral-8x22B-v0.1-GGUF | 140.62B | IQ1_M | Very low |
32.16 GB
|
1.6 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?
The NVIDIA RTX 5070 Ti features 16 GB of VRAM and a memory bandwidth of 896 GB/s (GDDR7 256-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?
The best overall model for the NVIDIA RTX 5070 Ti is unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF using the recommended Q3_K_M quantization (14.88 GB total memory). With 16 GB of VRAM, this GPU typically runs a 14B at high quality, or a 24–27B at 4-bit at full GPU speed. Check our guide to the best LLMs for 16 GB VRAM for details.
Can the NVIDIA RTX 5070 Ti run 14B and 32B models?
Yes. A 16 GB VRAM buffer allows you to run 14B models (like Qwen 2.5 14B or DeepSeek-R1 14B) in high-fidelity Q8_0 quantizations, or 32B models (like Qwen 2.5 32B) in Q3_K_M or Q4_K_M quantizations fully on the GPU.
What power supply (PSU) do you need for the NVIDIA RTX 5070 Ti when running local AI?
The NVIDIA RTX 5070 Ti has a rated TDP of 300W. While local inference typically draws less power than full 3D rasterization or gaming, continuous token generation can sustain high loads. A quality power supply of at least 600W is strongly recommended.
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