What LLMs Can the RTX 4060 Ti 16GB Run?
The RTX 4060 Ti 16GB has 16 GB of VRAM and 288 GB/s of bandwidth. It runs 28 of 58 models fully at Q4_K_M with 8K context, up to Gemma 4 26B-A4B, and generates about 34 tokens/s on an 8B model.
28 of 58 models fit
RTX 4060 Ti 16GB (16.0 GB) at Q4_K_M with 8K context. Largest that fits fully: Gemma 4 26B-A4B.
| Model | Needs | Status | Speed | Best quant that fits |
|---|---|---|---|---|
| gpt-oss-20b | 12.9 GB | Fits | ~68 tok/s | MXFP4 (official) |
| Qwen 2.5 14B | 10.7 GB | Fits | ~19 tok/s | Q6_K |
| DeepSeek R1 Distill Qwen 14B | 10.7 GB | Fits | ~19 tok/s | Q6_K |
| Phi-4 14B | 10.6 GB | Fits | ~19 tok/s | Q6_K |
| Qwen 3 14B | 10.5 GB | Fits | ~19 tok/s | Q6_K |
| Ministral 3 14B | 9.7 GB | Fits | ~21 tok/s | Q6_K |
| Mistral Nemo 12B | 9.0 GB | Fits | ~23 tok/s | Q8_0 |
| Gemma 3 12B | 8.8 GB | Fits | ~23 tok/s | Q8_0 |
| Ministral 3 8B | 6.7 GB | Fits | ~32 tok/s | Q8_0 |
| Qwen 3 8B | 6.6 GB | Fits | ~33 tok/s | Q8_0 |
| Qwen 3.5 9B | 6.4 GB | Fits | ~33 tok/s | Q8_0 |
| Llama 3.1 8B | 6.3 GB | Fits | ~34 tok/s | Q8_0 |
| DeepSeek R1 Distill Llama 8B | 6.3 GB | Fits | ~34 tok/s | Q8_0 |
| Mistral 7B v0.3 | 5.7 GB | Fits | ~37 tok/s | Q8_0 |
| Gemma 4 E4B | 5.7 GB | Fits | ~37 tok/s | Q8_0 |
| Qwen 2.5 7B | 5.6 GB | Fits | ~38 tok/s | Q8_0 |
| DeepSeek R1 Distill Qwen 7B | 5.6 GB | Fits | ~38 tok/s | Q8_0 |
| Qwen 3 4B | 4.2 GB | Fits | ~60 tok/s | FP16 / BF16 |
| Phi-4 Mini | 4.1 GB | Fits | ~64 tok/s | FP16 / BF16 |
| Gemma 4 E2B | 4.0 GB | Fits | ~59 tok/s | FP16 / BF16 |
| Gemma 3 4B | 3.7 GB | Fits | ~65 tok/s | FP16 / BF16 |
| Qwen 3.5 4B | 3.7 GB | Fits | ~67 tok/s | FP16 / BF16 |
| Ministral 3 3B | 3.5 GB | Fits | ~73 tok/s | FP16 / BF16 |
| Llama 3.2 3B | 3.5 GB | Fits | ~76 tok/s | FP16 / BF16 |
| Llama 3.2 1B | 1.7 GB | Fits | ~204 tok/s | FP16 / BF16 |
| Gemma 3 1B | 1.6 GB | Fits | ~274 tok/s | FP16 / BF16 |
| Mistral Small 3.1 24B | 15.6 GB | Tight fit | ~12 tok/s | IQ4_XS |
| Gemma 4 26B-A4B | 15.6 GB | Tight fit | ~59 tok/s | IQ4_XS |
| Qwen3-Next 80B-A3B | 46.3 GB | 33% on GPU | ~25 tok/s | – |
| Qwen 2.5 72B | 44.5 GB | 34% on GPU | ~1.3 tok/s | – |
| Llama 3.3 70B | 43.2 GB | 35% on GPU | ~1.3 tok/s | – |
| DeepSeek R1 Distill Llama 70B | 43.2 GB | 35% on GPU | ~1.3 tok/s | – |
| Mixtral 8x7B | 28.0 GB | 54% on GPU | ~8.4 tok/s | – |
| Qwen 2.5 32B | 21.4 GB | 70% on GPU | ~4.4 tok/s | Q2_K |
| Qwen 2.5 Coder 32B | 21.4 GB | 70% on GPU | ~4.4 tok/s | Q2_K |
| DeepSeek R1 Distill Qwen 32B | 21.4 GB | 70% on GPU | ~4.4 tok/s | Q2_K |
| Qwen 3 32B | 21.4 GB | 70% on GPU | ~4.4 tok/s | Q2_K |
| Qwen 3.5 35B-A3B | 21.0 GB | 72% on GPU | ~40 tok/s | Q2_K |
| Qwen 3.6 35B-A3B | 21.0 GB | 72% on GPU | ~40 tok/s | Q2_K |
| Gemma 4 31B | 19.8 GB | 76% on GPU | ~5.1 tok/s | Q2_K |
| Qwen 3 30B-A3B | 18.8 GB | 80% on GPU | ~38 tok/s | Q2_K |
| Qwen3-Coder 30B-A3B | 18.8 GB | 80% on GPU | ~38 tok/s | Q2_K |
| Gemma 3 27B | 17.6 GB | 86% on GPU | ~7.1 tok/s | Q3_K_M |
| Qwen 3.5 27B | 16.9 GB | 89% on GPU | ~8.1 tok/s | IQ4_XS |
| Qwen 3.6 27B | 16.9 GB | 89% on GPU | ~8.1 tok/s | IQ4_XS |
| Qwen 3.8 27B | 16.9 GB | 89% on GPU | ~8.1 tok/s | IQ4_XS |
| Kimi K2.6 | 566 GB | Too small | – | – |
| DeepSeek R1 671B | 380 GB | Too small | – | – |
| Llama 3.1 405B | 234 GB | Too small | – | – |
| Qwen 3.5 397B-A17B | 226 GB | Too small | – | – |
| DeepSeek V4 Flash | 161 GB | Too small | – | – |
| Qwen 3 235B-A22B | 135 GB | Too small | – | – |
| MiniMax-M2 | 133 GB | Too small | – | – |
| Qwen 3.5 122B-A10B | 70.2 GB | Too small | – | – |
| Mistral Small 4 119B | 68.2 GB | Too small | – | – |
| gpt-oss-120b | 67.2 GB | Too small | – | – |
| Llama 4 Scout | 64.0 GB | Too small | – | – |
| GLM-4.5-Air | 62.1 GB | Too small | – | – |
RTX 4060 Ti 16GB for local AI
The cheapest new 16 GB NVIDIA card of its generation. 16 GB fits 14B models at Q6_K or 24B models at Q4_K_M, but the 288 GB/s bus makes it slower than a used RTX 3090 or 3080 12GB.
Setup tips: the narrow 128-bit bus makes this card slower than its price suggests, so favour mixture-of-experts models such as gpt-oss-20b, which read few parameters per token. It draws only about 165 W, making it a good choice for a quiet always-on home AI server.
Among the 14 devices we track with about 16 GB of usable memory, the RTX 4060 Ti 16GB ranks 13 of 14 for text-generation speed, because generation speed follows memory bandwidth. Others in the same class: RTX 5080 16GB, RTX 5070 Ti 16GB, RTX 4080 Super 16GB, RTX 4080 16GB. They all hold the same models; the faster ones simply answer sooner.
Our picks for the RTX 4060 Ti 16GB
The model we would run first for each job, chosen from our recommendations by what fits fully with an 8K context. "Best quant" is the highest quality that fits; "max context" is at Q4_K_M or the native format.
| Task | Model | Best quant | Max context | Speed |
|---|---|---|---|---|
| Best all-rounder | Qwen 3 14B | Q6_K | 37K | ~19 tok/s |
| Coding | gpt-oss-20b | MXFP4 (official) | 55K | ~64 tok/s |
| Fastest replies | gpt-oss-20b | MXFP4 (official) | 55K | ~64 tok/s |
| Long documents (64K+) | Qwen 3.5 9B | Q8_0 | 256K | ~33 tok/s |
| Images in, text out | Ministral 3 14B | Q6_K | 42K | ~21 tok/s |
| Image generation | Qwen-Image | GGUF Q5_K_S | – | – |
| Spec | Value |
|---|---|
| Memory | 16 GB |
| Memory bandwidth | 288 GB/s |
| Architecture | Ada Lovelace |
| Released | 2023-07 |
| Llama 3.1 8B Q4_K_M speed | ~34 tok/s |
| Largest model that fits | Gemma 4 26B-A4B |
Image and video models on the RTX 4060 Ti 16GB
Peak VRAM with model swapping (ComfyUI default on smaller cards).
| Model | FP8 | FP8 fit | GGUF Q4 | Q4 fit |
|---|---|---|---|---|
| Stable Diffusion 1.5 | 1.8 GB | Fits | 1.7 GB | Fits |
| SDXL 1.0 | 4.1 GB | Fits | 4.1 GB | Fits |
| Stable Diffusion 3.5 Medium | 6.4 GB | Fits | 6.4 GB | Fits |
| Stable Diffusion 3.5 Large | 8.6 GB | Fits | 6.4 GB | Fits |
| FLUX.1 [dev] | 12.1 GB | Fits | 7.3 GB | Fits |
| FLUX.1 [schnell] | 12.1 GB | Fits | 7.3 GB | Fits |
| Qwen-Image | 20.4 GB | 74% on GPU | 12.3 GB | Fits |
| Wan 2.2 A14B (T2V / I2V) | 18.8 GB | 80% on GPU | 13.2 GB | Fits |
| Wan 2.2 TI2V 5B | 8.0 GB | Fits | 6.5 GB | Fits |
| HunyuanVideo | 18.3 GB | 82% on GPU | 13.1 GB | Fits |
| FLUX.1 Kontext [dev] | 12.3 GB | Fits | 7.5 GB | Fits |
| FLUX.2 [dev] | 31.0 GB | 49% on GPU | 23.6 GB | 64% on GPU |
| Z-Image Turbo | 6.8 GB | Fits | 4.9 GB | Fits |
| Qwen-Image-Edit 2509 | 20.5 GB | 73% on GPU | 12.4 GB | Fits |
| LTX-2 | 19.2 GB | 78% on GPU | 12.6 GB | Fits |
Upgrade path
The next step up is the Radeon RX 7900 XT 20GB with 20 GB and 178% more bandwidth.
Comparisons
- RTX 3060 12GB vs RTX 4060 Ti 16GB
- RTX 4060 Ti 16GB vs RTX 5060 Ti 16GB
- RTX 4060 Ti 16GB vs RTX 4070 12GB
RTX 4060 Ti 16GB for LLMs: common questions
Can the RTX 4060 Ti 16GB run Llama 3.3 70B?
Only partly. It needs about 43.2 GB at Q4_K_M, so about 35% fits in VRAM and the rest runs from system RAM, which is slow.
Can the RTX 4060 Ti 16GB run Qwen 3 32B?
Only partly. It needs about 21.4 GB at Q4_K_M, so about 70% fits in VRAM and the rest runs from system RAM, which is slow. Q2_K fits fully.
Can the RTX 4060 Ti 16GB run Qwen 3 14B?
Yes. Qwen 3 14B needs about 10.5 GB at Q4_K_M with 8K context, and the best quality that fits is Q6_K.
How fast is the RTX 4060 Ti 16GB for LLMs?
About 34 tokens per second for an 8B model at Q4_K_M, based on its 288 GB/s memory bandwidth. Larger models scale down roughly in proportion to their size.