NVIDIA · Ada Lovelace · 16 GB VRAM tier
Best AI Models for NVIDIA GeForce RTX 4060 Ti 16GB
Runs 14B models well; 27–31B barely fits and is slow (13 tok/s).
| # | Model | Speed | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 29.4 t/s | 29.4 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 57.4 t/s | 57.4 tok/s | Q4_K_M · 3.4 GB | |
| 3 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 66.0 t/s | 66.0 tok/s | Q4_K_M · 13.1 GB | |
| 4 | Phi 4Microsoft Q4_K_M · 12.5 GB · 19.7 t/s | 19.7 tok/s | Q4_K_M · 12.5 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 22.7 t/s | 22.7 tok/s | Q4_K_M · 9.0 GB |
| # | Model | Speed | Coding Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | GPT OSS 20BOpenAIHow to run → Q4_K_M · 13.1 GB · 66.0 t/s | 66.0 tok/s | Q4_K_M · 13.1 GB | |
| 2 | Q4_K_M · 7.9 GB · 35.3 t/s | 35.3 tok/s | Q4_K_M · 7.9 GB | |
| 3 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 29.4 t/s | 29.4 tok/s | overall | Q4_K_M · 6.6 GB |
| 4 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 22.7 t/s | 22.7 tok/s | Q4_K_M · 9.0 GB | |
| 5 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 158 t/s | 158 tok/s | Q4_K_M · 5.5 GB |
| # | Model | Speed | Reasoning Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 29.4 t/s | 29.4 tok/s | Q4_K_M · 6.6 GB | |
| 2 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 66.0 t/s | 66.0 tok/s | Q4_K_M · 13.1 GB | |
| 3 | Qwen3 8BAlibaba Q4_K_M · 7.6 GB · 33.9 t/s | 33.9 tok/s | Q4_K_M · 7.6 GB | |
| 4 | Q4_K_M · 7.9 GB · 35.3 t/s | 35.3 tok/s | Q4_K_M · 7.9 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 22.7 t/s | 22.7 tok/s | Q4_K_M · 9.0 GB |
| # | Model | Speed | Long context Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 29.4 t/s | 29.4 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 57.4 t/s | 57.4 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Mistral Nemo Instruct 2407Mistral AI Q4_K_M · 11.0 GB · 23.2 t/s | 23.2 tok/s | Q4_K_M · 11.0 GB | |
| 4 | Q4_K_M · 7.9 GB · 35.3 t/s | 35.3 tok/s | Q4_K_M · 7.9 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 22.7 t/s | 22.7 tok/s | Q4_K_M · 9.0 GB |
| # | Model | Speed ↓ | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | GPT OSS 20BOpenAIHow to run → Q4_K_M · 13.1 GB · 66.0 t/s | 66.0 tok/s | Q4_K_M · 13.1 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 57.4 t/s | 57.4 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 29.4 t/s | 29.4 tok/s | Q4_K_M · 6.6 GB | |
| 4 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 22.7 t/s | 22.7 tok/s | Q4_K_M · 9.0 GB | |
| 5 | Phi 4Microsoft Q4_K_M · 12.5 GB · 19.7 t/s | 19.7 tok/s | Q4_K_M · 12.5 GB |
| # | Model | Speed | Vision Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 29.4 t/s | 29.4 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 57.4 t/s | 57.4 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Gemma 3 12B ITGoogle Q4_K_M · 10.9 GB · 23.3 t/s | 23.3 tok/s | Q4_K_M · 10.9 GB | |
| 4 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 65.9 t/s | 65.9 tok/s | Q4_K_M · 5.0 GB | |
| 5 | Q4_K_M · 6.5 GB · 30.1 t/s | 30.1 tok/s | Q4_K_M · 6.5 GB |
The RTX 4060 Ti 16GB is an entry card in the 16 GB class: it runs 14B models well. It is power-hungry for its speed, and at its $499 MSRP it cost more per token than most of the cards compared. Ada Lovelace generation, released 2023.
RTX 4060 Ti 16GB strengths and weaknesses
Strengths
- Runs 35B MoE models: Qwen3.6 35B A3B at 75.5 tok/s in 15.7 GB
Weaknesses
- 70B models don't fit: Llama 3.3 70B Instruct needs 51.3 GB at Q4 vs 16 GB here
- Slow on large dense models: Qwen3.8 27B at 12.5 tok/s
Speed per dollar and per watt
On Qwen3.5 9B (9B params) at Q4_K_M. New prices are compared with new, used with used. The last two columns are the speed you get per $100 of price and per 100 W of board power; higher is better. The RTX 4060 Ti 16GB has no current price, so it is shown at its MSRP.
| Card | New price | tok/s | tok/s per $100 | tok/s per 100 W |
|---|---|---|---|---|
RTX 507012 GB | about $840 | 68.7 | 8.2 | 27.5 |
RX 7900 XTX24 GB | about $1,130 | 90.6 | 8.0 | 25.5 |
RTX 5070 Ti16 GBBest new value | about $1,170 | 91.6 | 7.8 | 30.5 |
RX 907016 GB | about $800 | 60.4 | 7.6 | 27.5 |
RX 9070 XT16 GB | about $815 | 60.4 | 7.4 | 19.9 |
RTX 4060 Ti 16GB16 GB | about $499 | 29.4 | 5.9 | 17.8 |
RTX 508016 GB | about $1,690 | 98.1 | 5.8 | 27.3 |
RX 9060 XT 16GB16 GB | about $590 | 30.2 | 5.1 | 18.9 |
| Card | Used price | tok/s | tok/s per $100 | tok/s per 100 W |
|---|---|---|---|---|
RTX 3060 12GB12 GBBest used value | about $329 | 36.8 | 11.2 | 21.6 |
RX 907016 GB | about $650 | 60.4 | 9.3 | 27.5 |
RX 7900 XTX24 GB | about $1,000 | 90.6 | 9.1 | 25.5 |
RTX 407012 GB | about $575 | 51.5 | 9.0 | 25.8 |
RTX 5070 Ti16 GB | about $1,100 | 91.6 | 8.3 | 30.5 |
RTX 4070 SUPER12 GB | about $675 | 51.5 | 7.6 | 23.4 |
RTX 4070 Ti12 GB | about $722 | 51.5 | 7.1 | 18.1 |
RX 9060 XT 16GB16 GB | about $480 | 30.2 | 6.3 | 18.9 |
Cost per token: the RTX 4060 Ti 16GB against 12–32 GB cards
On Qwen3.5 9B (9B params, Q4_K_M, 4K context). Speed, cost per token and power per token depend on the model.
- Card, MSRP
- $1.08
- Power
- $0.23
Renting costs as much as buying after ≈4,100 hours (≈2.8 years at 4 h a day).
Each dot is a card; hover or tap it for its numbers, click to open it. Cards without a price for the chosen basis are left out. How we calculate cost per token
Cards people weigh against the RTX 4060 Ti 16GB, each with the one reason it is worth a look. Speeds on Qwen3.5 9B.
Output speed depends on memory bandwidth (288 GB/s). Grey rows need more than 16 GB.
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB29.4tok/s
- 14BQwen3 14B · Q4_K_M · 11.9 GB19.7tok/s
- 12.5tok/s
- 75.5tok/s
- 70BLlama 3.3 70B Instruct · not at 4-bitneeds 51.3 GB
- 120B MoEGPT OSS 120B · not at 4-bitneeds 70.8 GB
Estimated speeds: Q4_K_M, 4K context, one user. How we estimate
All 1057 models on the RTX 4060 Ti 16GB
Featured onlyBest quant that fits, ordered by llmrun Score. Fit grades how well it runs here, S best. How ranking works
| Model | Quant | VRAM | Speed | Context | Score | Fit |
|---|---|---|---|---|---|---|
Gemma 3 12B ITVision | Q4_K_M | 10.9 GB | 23.3 t/s | 33K | B64, Decent | |
| Q4_K_M | 0.1 GB | 1440.0 t/s | 2K | S100, Runs great | ||
GPT Neo 125MChat | BF16 | 0.3 GB | 567.3 t/s | 2K | S100, Runs great | |
Bloom 560MChat | Q4_K_M | 1.6 GB | 505.9 t/s | — | S100, Runs great | |
LFM2 VL 1.6BVision | Q4_K_M | 1.5 GB | 141.8 t/s | 128K | S96, Runs great | |
| BF16 | 7.6 GB | 31.0 t/s | 29K | A71, Runs well | ||
| Q4_K_M | 5.8 GB | 37.5 t/s | 33K | A74, Runs well | ||
Falcon 11BChat | Q4_K_M | 8.5 GB | 25.5 t/s | 8K | A66, Runs well | |
| Q4_K_M | 0.6 GB | 435.3 t/s | 8K | S100, Runs great | ||
Nerdsking Python Coder 7B IChatCode | Q4_K_M | 7.1 GB | 40.5 t/s | — | A75, Runs well | |
Gemma 2 Mitra EChat | Q4_K_M | 7.7 GB | 29.0 t/s | 8K | A69, Runs well | |
NuExtract 1.5Chat | Q4_K_M | 9.0 GB | 55.1 t/s | 131K | A82, Runs well | |
LFM2 24B A2BChatFunctions | Q4_K_M | 14.9 GB | 101.0 t/s | 128K | B62, Decent | |
Xflux Text EncodersChatCode | BF16 | 12.7 GB | 17.9 t/s | — | B58, Decent | |
Mellum2 12B A2.5B ThinkingChatFunctions | Q4_K_M | 7.8 GB | 102.5 t/s | 131K | S92, Runs great |
Specs
- Architecture
- Ada Lovelace
- VRAM
- 16 GB GDDR6
- Memory bandwidth
- 288.0 GB/s
- FP16 (dense)
- 44.10 TFLOPS
- CUDA cores
- 4,352
- Tensor cores
- 136
- Compute capability
- 8.9
- TDP
- 165 W
- Released
- 2023-07-18
- MSRP
- $499
RTX 4060 Ti 16GB FAQ
Is the RTX 4060 Ti 16GB still worth it in 2026?
At its MSRP of $499 you get 5.9 tok/s per $100 on Qwen3.5 9B, which ranks 6th of the 8 cards compared here; it is also the 16th fastest out of 17 cards with 16 GB. It runs 14B models well; 27–31B barely fits and is slow (13 tok/s).
What is the largest model the RTX 4060 Ti 16GB can run?
Gemma 4 26B A4B IT (25.8B) is the largest at 4-bit or better: at Q4_K_S it uses 15.5 GB of 16 GB, and runs at about 64.8 tok/s. At 3-bit, a bigger model fits too: Gemma 2 27B IT (27.2B) at IQ3_M, 15.0 GB.
Can the RTX 4060 Ti 16GB run Qwen3.5 9B?
Yes. Qwen3.5 9B fits at Q4_K_M, using 6.6 GB of 16 GB, and it is the best model that runs well on this card.
Can the RTX 4060 Ti 16GB run 70B models?
No. Llama 3.3 70B Instruct needs 51.3 GB at Q4, more than the 16 GB on this card.
How many tokens per second does the RTX 4060 Ti 16GB give?
About 29.4 tok/s on Qwen3.5 9B at Q4_K_M, with the first token after 4.0 s on a 4K-token prompt.
What power supply does the RTX 4060 Ti 16GB need?
Plan a 350 W PSU (2× the 165 W TDP).
Is 16 GB enough for coding and agents?
Yes for coding: GPT OSS 20B, the best of the 2 coding models that fit, runs at about 66 tok/s. For agents, not yet: no agent-scored model fits in less than 96 GB.
Is renting cheaper than buying an RTX 4060 Ti 16GB?
Rent from $0.12/hr. Renting costs as much as buying after ≈4,100 hours (≈2.8 years at 4 h a day).