What LLMs can a RTX 4060 Ti 16GB run?
At the recommended quantization with 16K context, the largest model a RTX 4060 Ti 16GB can run is gpt-oss-20B (21B, about 12.6 GB); 3 models on the list run comfortably. With 288GB/s of memory bandwidth, the decode ceiling is roughly bandwidth ÷ weight size; measured speeds usually land at 70-85% of that. We have not benchmarked this machine ourselves yet — submissions welcome. Data verified 2026-10-03.
Specs: 16GB · 288GB/s · 128-bit bus: lower bandwidth than a 3060 but 4GB more VRAM
Model fit list (recommended quant, 16K context)
Comfortable = ≤ 75% of memory; Tight = ≤ 92%; Lower quant = the recommended quant doesn't fit but a smaller one does. Ceiling = 288GB/s ÷ weight size; single-request decoding cannot exceed it.
| Model | Quant | Needs | Verdict | Decode ceiling tok/s |
|---|---|---|---|---|
| Qwen3-4B | Q4_K_M (GGUF) | 6.0 GB | Comfortable | ≤ 127 |
| Qwen3.5-9B | Q4_K_M (GGUF) | 7.5 GB | Comfortable | ≤ 52 |
| Llama 3.1 8B | Q4_K_M (GGUF) | 8.0 GB | Comfortable | ≤ 63 |
| Qwen3-14B | Q4_K_M (GGUF) | 12.4 GB | Tight | ≤ 34 |
| gpt-oss-20B | MXFP4 | 12.6 GB | Tight | ≤ 28 |
| Nemotron 3.5 Lightning (30B-A3B) | Q2_K (GGUF) | 11.4 GB | Lower quant | ≤ 29 |
| Gemma 4 26B-A4B | Q3_K_M (GGUF) | 13.1 GB | Lower quant | ≤ 26 |
| Qwen3.8-27B | Q3_K_M (GGUF) | 13.8 GB | Lower quant | ≤ 24 |
| Mistral Small 3.x 24B | Q3_K_M (GGUF) | 14.5 GB | Lower quant | ≤ 27 |
| GLM-4.7-Flash (30B-A3B) | Q2_K (GGUF) | 12.5 GB | Lower quant | ≤ 28 |
| Qwen3-Coder-30B-A3B | Q2_K (GGUF) | 12.9 GB | Lower quant | ≤ 29 |
| Gemma 4 31B | Q2_K (GGUF) | 13.0 GB | Lower quant | ≤ 28 |
| Qwen3.6-35B-A3B | Q2_K (GGUF) | 13.5 GB | Lower quant | ≤ 25 |
| Qwen3-32B | Q4_K_M (GGUF) | 23.7 GB | Won't fit | — |
| Gemma 3 27B | Q4_K_M (GGUF) | 24.6 GB | Won't fit | — |
| Llama 3.3 70B | Q4_K_M (GGUF) | 47.9 GB | Won't fit | — |
| gpt-oss-120B | MXFP4 | 63.5 GB | Won't fit | — |
| Mistral Leanstral 1.5 (119B-A6B MoE) | Q4_K_M (GGUF) | 73.4 GB | Won't fit | — |
| Qwen3.8-Flash-Next (180B MoE) | Q4_K_M (GGUF) | 111 GB | Won't fit | — |
| Qwen3-235B-A22B | Q4_K_M (GGUF) | 147 GB | Won't fit | — |
| DeepSeek V4 Flash (304B MoE) | Q4_K_M (GGUF) | 187 GB | Won't fit | — |
| GLM-4.5 (355B MoE) | Q4_K_M (GGUF) | 224 GB | Won't fit | — |
| DeepSeek V3 / R1 (671B MoE) | Q4_K_M (GGUF) | 413 GB | Won't fit | — |
| GLM-5.3 (753B MoE) | Q4_K_M (GGUF) | 463 GB | Won't fit | — |
| Kimi K2 (1T MoE) | Q4_K_M (GGUF) | 631 GB | Won't fit | — |
| DeepSeek V4 Pro (1.6T MoE) | Q4_K_M (GGUF) | 983 GB | Won't fit | — |
| Kimi K3 (2.8T MoE) | MXFP4 | 1.46 TB | Won't fit | — |
Benchmarked a RTX 4060 Ti 16GB yourself?
Send us the model, quant, runtime version, tok/s and peak memory. Verified readings are added to this page with credit. First-hand numbers with a command or log only.
Submit a benchmarkFAQ
What is the largest LLM a RTX 4060 Ti 16GB can run?
At the recommended quantization with 16K context, the ceiling is gpt-oss-20B (about 12.6 GB, 79% of 16GB). The OS and display reserve 0.5-1GB of VRAM, and llama.cpp / vLLM need headroom for CUDA graphs and activation buffers.
Which models run best on a RTX 4060 Ti 16GB?
Models under 12GB leave room for long context without closing other apps, e.g. Qwen3.5-9B, Llama 3.1 8B, Qwen3-4B.
How many tokens per second does a RTX 4060 Ti 16GB get?
Single-request decoding is bandwidth-bound: ceiling ≈ 288GB/s ÷ weight size. A 15GB 27B 4-bit model tops out around 19 tok/s; measured speeds are usually 70-85% of that, and speculative decoding (a draft model) adds another 1.5-2x.
Are these numbers measured or estimated?
The fit table is a planning estimate (same formula as the calculator). The first-hand benchmark table contains readings we took on this exact machine with real model files; every row links to the source article.