NVIDIA · Blackwell · 16 GB VRAM tier
Best AI Models for NVIDIA GeForce RTX 5060 Ti 16GB
Runs 14B models well; 27–31B only just fits.
| # | Model | Speed | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 45.8 t/s | 45.8 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 89.3 t/s | 89.3 tok/s | Q4_K_M · 3.4 GB | |
| 3 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 97.9 t/s | 97.9 tok/s | Q4_K_M · 13.1 GB | |
| 4 | Phi 4Microsoft Q4_K_M · 12.5 GB · 30.6 t/s | 30.6 tok/s | Q4_K_M · 12.5 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 35.4 t/s | 35.4 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 · 97.9 t/s | 97.9 tok/s | Q4_K_M · 13.1 GB | |
| 2 | Q4_K_M · 7.9 GB · 54.9 t/s | 54.9 tok/s | Q4_K_M · 7.9 GB | |
| 3 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 45.8 t/s | 45.8 tok/s | overall | Q4_K_M · 6.6 GB |
| 4 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 35.4 t/s | 35.4 tok/s | Q4_K_M · 9.0 GB | |
| 5 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 221 t/s | 221 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 · 45.8 t/s | 45.8 tok/s | Q4_K_M · 6.6 GB | |
| 2 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 97.9 t/s | 97.9 tok/s | Q4_K_M · 13.1 GB | |
| 3 | Qwen3 8BAlibaba Q4_K_M · 7.6 GB · 52.8 t/s | 52.8 tok/s | Q4_K_M · 7.6 GB | |
| 4 | Q4_K_M · 7.9 GB · 54.9 t/s | 54.9 tok/s | Q4_K_M · 7.9 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 35.4 t/s | 35.4 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 · 45.8 t/s | 45.8 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 89.3 t/s | 89.3 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Mistral Nemo Instruct 2407Mistral AI Q4_K_M · 11.0 GB · 36.1 t/s | 36.1 tok/s | Q4_K_M · 11.0 GB | |
| 4 | Q4_K_M · 7.9 GB · 54.9 t/s | 54.9 tok/s | Q4_K_M · 7.9 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 35.4 t/s | 35.4 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 · 97.9 t/s | 97.9 tok/s | Q4_K_M · 13.1 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 89.3 t/s | 89.3 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 45.8 t/s | 45.8 tok/s | Q4_K_M · 6.6 GB | |
| 4 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 35.4 t/s | 35.4 tok/s | Q4_K_M · 9.0 GB | |
| 5 | Phi 4Microsoft Q4_K_M · 12.5 GB · 30.6 t/s | 30.6 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 · 45.8 t/s | 45.8 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 89.3 t/s | 89.3 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Gemma 3 12B ITGoogle Q4_K_M · 10.9 GB · 36.2 t/s | 36.2 tok/s | Q4_K_M · 10.9 GB | |
| 4 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 103 t/s | 103 tok/s | Q4_K_M · 5.0 GB | |
| 5 | Q4_K_M · 6.5 GB · 46.9 t/s | 46.9 tok/s | Q4_K_M · 6.5 GB |
The RTX 5060 Ti 16GB is an entry card in the 16 GB class: it runs 14B models well. It is more efficient per watt than most 16 GB cards, and at its $429 MSRP it gave the best speed per dollar of the cards compared. Blackwell generation, released 2025.
RTX 5060 Ti 16GB strengths and weaknesses
Strengths
- Runs 35B MoE models: Qwen3.6 35B A3B at 108 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
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 5060 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 5060 Ti 16GB16 GB | about $429 | 45.8 | 10.7 | 25.4 |
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 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 Ti SUPER16 GB | about $900 | 68.7 | 7.6 | 24.1 |
RTX 4070 SUPER12 GB | about $675 | 51.5 | 7.6 | 23.4 |
RTX 4070 Ti12 GB | about $722 | 51.5 | 7.1 | 18.1 |
Cost per token: the RTX 5060 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
- $0.59
- Power
- $0.16
Renting costs as much as buying after ≈3,500 hours (≈2.4 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 5060 Ti 16GB, each with the one reason it is worth a look. Speeds on Qwen3.5 9B.
Output speed depends on memory bandwidth (448 GB/s). Grey rows need more than 16 GB.
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB45.8tok/s
- 14BQwen3 14B · Q4_K_M · 11.9 GB30.7tok/s
- 19.4tok/s
- 108tok/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 5060 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 |
|---|---|---|---|---|---|---|
DeepSeek R1 Distill Qwen 14BChatReasoningFunctions | Q4_K_M | 12.4 GB | 30.5 t/s | 131K | A71, Runs well | |
Qwen3 14B PAROChat | Q4_K_M | 3.9 GB | 185.5 t/s | 41K | S99, Runs great | |
AFM 4.5BChat | Q4_K_M | 4.3 GB | 90.4 t/s | 66K | S90, Runs great | |
Qwen3.5 9B BaseVision | Q4_K_M | 6.6 GB | 45.8 t/s | 262K | A78, Runs well | |
Granite 4.0 350MChatFunctions | Q4_K_M | 1.0 GB | 510.9 t/s | 33K | S100, Runs great | |
| Q4_K_M | 10.7 GB | 51.6 t/s | 1073K | A80, Runs well | ||
Steelman 14B AdaChatCode | Q4_K_M | 12.2 GB | 31.5 t/s | — | A71, Runs well | |
NVIDIA Nemotron Nano 12B v2ChatFunctions | Q4_K_M | 11.0 GB | 35.9 t/s | — | A73, Runs well | |
Granite 4.1 8BChatFunctions | Q4_K_M | 8.3 GB | 49.3 t/s | 131K | A79, Runs well | |
| Q4_K_M | 0.5 GB | 606.7 t/s | 33K | S100, Runs great | ||
XiaoHong V1Chat | Q4_K_M | 7.6 GB | 52.8 t/s | 41K | A81, Runs well | |
Qwen3.5 9B Gemini 3.1 Pro Reasoning DistillChatReasoning | Q4_K_M | 6.6 GB | 45.8 t/s | 262K | A78, Runs well | |
Huihui Qwen3 8B Abliterated v2ChatFunctions | Q4_K_M | 7.6 GB | 52.8 t/s | 41K | A81, Runs well | |
Qwen3.5 4B MiniFantasy MTPChatRoleplay | FP16 | 10.0 GB | 29.7 t/s | 262K | A71, Runs well | |
DeepSeek R1 Distill Qwen 1.5BChatReasoning | Q4_K_M | 2.6 GB | 294.1 t/s | — | S100, Runs great |
Specs
- Architecture
- Blackwell
- VRAM
- 16 GB GDDR7
- Memory bandwidth
- 448.0 GB/s
- FP16 (dense)
- 47.40 TFLOPS
- CUDA cores
- 4,608
- Tensor cores
- 144
- Compute capability
- 12.0
- TDP
- 180 W
- Released
- 2025-04-16
- MSRP
- $429
RTX 5060 Ti 16GB FAQ
Is the RTX 5060 Ti 16GB still worth it in 2026?
At its MSRP of $429 you get 10.7 tok/s per $100 on Qwen3.5 9B, which ranks 1st of the 8 cards compared here; it is also the 13th fastest out of 17 cards with 16 GB. It runs 14B models well; 27–31B only just fits.
What is the largest model the RTX 5060 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 95.1 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 5060 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 5060 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 5060 Ti 16GB give?
About 45.8 tok/s on Qwen3.5 9B at Q4_K_M, with the first token after 3.7 s on a 4K-token prompt.
What power supply does the RTX 5060 Ti 16GB need?
Plan a 400 W PSU (2× the 180 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 98 tok/s. For agents, not yet: no agent-scored model fits in less than 96 GB.
Is renting cheaper than buying an RTX 5060 Ti 16GB?
Rent from $0.12/hr. Renting costs as much as buying after ≈3,500 hours (≈2.4 years at 4 h a day).