Which local LLMs can the GeForce RTX 5060 Ti 16GB run?

Assumes 32 GB of DDR5-5600 system RAM, Windows with this GPU driving the display, 8k context and an f16 KV cache.

What to expect from the GeForce RTX 5060 Ti 16GB

With 16GB of VRAM, about 15.4 GB of it free for a model in CanRun's setup, the RTX 5060 Ti 16GB sits in the first VRAM tier where gpt-oss-20b fits completely. It runs great here, and our page for gpt-oss-20b on the GeForce RTX 5060 Ti 16GB has the commands. Qwen3 14B runs great as well and still fits fully at 32k.

The 24B–35B range is where the quant you download matters. Gemma 4 26B-A4B fits fully in its IQ4_XS file and runs great, while the Q4_K_M file puts part of its experts in system RAM and runs well, by a theoretical estimate (±30%). Mistral Small 3.2 24B needs 15.9 GB at Q4_K_M, just over what the card has free, so a little of it goes to system RAM; its IQ4_XS file stays fully on the GPU. Qwen3.8 27B spills further at Q4_K_M and runs slowly. Its IQ4_XS file still misses by a little, but with most of it on the GPU, CanRun expects it to run well. Qwen3.5 35B-A3B runs well with its experts in system RAM, a theoretical estimate (±30%).

There is also a GeForce RTX 5060 Ti 8GB with the same 448 GB/s of bandwidth but half the memory. On that card gpt-oss-20b no longer fits on the GPU, so check which version you have before reading this page.

Specs

VRAM: 16 GB
Memory bandwidth: 448.0 GB/s
FP16 compute: 23.7 TFLOPS
Launch year: 2025
Price: $569 street (as of 2026-08-09)
Also written as: 5060 Ti 16GB, 5060 Ti 16, 5060Ti

Verdicts at a glance

At Q4_K_M (or the closest available quant) with 8k context.

Runs great28 modelsRuns well9 modelsRuns slowly12 modelsWon't run10 models3 more models run at a lower quant.

How to read the verdicts

Runs great: Fully on the GPU at 20 tok/s or more
Runs well: Fully on the GPU at 8–20 tok/s; MoE experts in system RAM at 20 tok/s or more; or at least 90% on the GPU at 8 tok/s or more
Runs slowly: 2–8 tok/s; CPU-only; less than 90% on the GPU; or MoE experts in system RAM below 20 tok/s
Won't run: Does not fit, or under 2 tok/s

Every model on the GeForce RTX 5060 Ti 16GB

Dense models split between GPU and CPU slow down sharply — the CPU side sets the pace. MoE models that keep only their experts in system RAM degrade far more gently.

Scroll sideways to see every column.

59 models sorted by verdict and speed
Model	Verdict	Speed	Quant	Memory	Runs as	Context	Notes
MiniCPM5 1B	Runs great	est. 351.8 tok/s309.6–394.1calibrated estimate ±12%	Q4_K_M	1.9 / 16.0 GB	Full GPU	up to 128k	thinking mode
Llama 3.2 1B	Runs great	est. 290.8 tok/s255.9–325.7calibrated estimate ±12%	Q4_K_M	2.2 / 16.0 GB	Full GPU	up to 128k	—
Ling-3.0 Tiny	Runs great	est. 250.3 tok/s175.2–325.4theoretical estimate ±30%	Q4_K_M	5.9 / 16.0 GB	Full GPU	up to 128k	thinking mode
EXAONE 4.0 1.2B	Runs great	est. 238.8 tok/s210.1–267.4calibrated estimate ±12%	Q4_K_M	2.5 / 16.0 GB	Full GPU	up to 64k	thinking mode
LFM2.5 2.6B	Runs great	est. 173.8 tok/s121.7–226.0theoretical estimate ±30%	Q4_K_M	3.0 / 16.0 GB	Full GPU	up to 128k	thinking mode
HyperCLOVA X SEED 1.5B	Runs great	est. 172.8 tok/s152.0–193.5calibrated estimate ±12%	Q4_K_M	3.0 / 16.0 GB	Full GPU	up to 16k	—
MiniCPM5 2B	Runs great	est. 164.0 tok/s144.3–183.7calibrated estimate ±12%	Q4_K_M	2.9 / 16.0 GB	Full GPU	up to 128k	thinking mode
Gemma 4 E2B	Runs great	est. 158.9 tok/s111.2–206.6theoretical estimate ±30%	Q4_K_M	2.7 / 16.0 GB	Full GPU	up to 128k	thinking mode
Mellum2.1 12B-A2.5B Thinking	Runs great	est. 147.7 tok/s118.2–177.2calibrated estimate ±20%	Q4_K_M	9.2 / 16.0 GB	Full GPU	up to 128k	thinking mode
gpt-oss-20b	Runs greatgpt-oss-20b verdict on the GeForce RTX 5060 Ti 16GB	111.6 tok/s8k estimate 104.3 tok/s (83.5–125.2, calibrated estimate ±20%)measured (1 run, 2k context)	MXFP4	12.9 / 16.0 GB	Full GPU	up to 64k	thinking mode
Spark-X2.5 4B	Runs great	est. 108.1 tok/s95.1–121.0calibrated estimate ±12%	Q4_K_M	4.1 / 16.0 GB	Full GPU	up to 128k	thinking mode
Llama 3.2 3B	Runs great	est. 106.0 tok/s93.2–118.7calibrated estimate ±12%	Q4_K_M	4.1 / 16.0 GB	Full GPU	up to 64k	—
Qwen3.5 4B	Runs great	est. 101.2 tok/s89.1–113.4calibrated estimate ±12%	Q4_K_M	4.2 / 16.0 GB	Full GPU	up to 128k	thinking mode
Kanana 1.5 15.7B-A3B	Runs great	est. 89.1 tok/s71.3–106.9calibrated estimate ±20%	Q4_K_M	12.5 / 16.0 GB	Full GPU	up to 32k	—
Gemma 4 E4B	Runs great	est. 87.2 tok/s61.0–113.3theoretical estimate ±30%	Q4_K_M	4.3 / 16.0 GB	Full GPU	up to 128k	thinking mode
Llama 3.1 8B	Runs greatLlama 3.1 8B verdict on the GeForce RTX 5060 Ti 16GB	81.4 tok/s8k estimate 52.3 tok/s (46.0–58.6, calibrated estimate ±12%)measured (1 run, 512 context)	Q4_K_M	6.9 / 16.0 GB	Full GPU	up to 64k	—
DeepSeek R1 Distill Llama 8B	Runs great	est. 52.3 tok/s46.0–58.6calibrated estimate ±12%	Q4_K_M	6.9 / 16.0 GB	Full GPU	up to 32k	thinking mode
Kanana 1.5 8B	Runs great	est. 52.3 tok/s46.0–58.6calibrated estimate ±12%	Q4_K_M	6.9 / 16.0 GB	Full GPU	up to 32k	—
Nanbeige4.2 3B	Runs great	est. 51.2 tok/s35.8–66.5theoretical estimate ±30%	Q4_K_M	5.0 / 16.0 GB	Full GPU	up to 16k	thinking mode
Qwen3.5 9B	Runs greatQwen3.5 9B verdict on the GeForce RTX 5060 Ti 16GB	est. 51.1 tok/s45.0–57.2calibrated estimate ±12%	Q4_K_M	6.6 / 16.0 GB	Full GPU	up to 128k	thinking mode
Gemma 3 12B	Runs greatGemma 3 12B verdict on the GeForce RTX 5060 Ti 16GB	50.3 tok/s8k estimate 40.0 tok/s (35.2–44.8, calibrated estimate ±12%)measured (1 run, 512 context)	Q4_K_M	9.0 / 16.0 GB	Full GPU	up to 64k	—
DeepSeek R1 0528 Qwen3 8B	Runs great	est. 50.3 tok/s44.2–56.3calibrated estimate ±12%	Q4_K_M	7.1 / 16.0 GB	Full GPU	up to 32k	thinking mode
Qwen3 8B	Runs greatQwen3 8B verdict on the GeForce RTX 5060 Ti 16GB	est. 50.3 tok/s44.2–56.3calibrated estimate ±12%	Q4_K_M	7.1 / 16.0 GB	Full GPU	up to 32k	thinking mode
K2-Horizon 7B	Runs great	est. 46.1 tok/s40.6–51.7calibrated estimate ±12%	Q4_K_M	7.4 / 16.0 GB	Full GPU	up to 32k	thinking mode
Gemma 4 12B	Runs greatGemma 4 12B verdict on the GeForce RTX 5060 Ti 16GB	est. 43.2 tok/s38.0–48.4calibrated estimate ±12%	Q4_K_M	8.4 / 16.0 GB	Full GPU	up to 128k	thinking mode
HyperCLOVA X SEED Think 14B	Runs great	est. 30.8 tok/s21.5–40.0theoretical estimate ±30%	Q4_K_M	11.0 / 16.0 GB	Full GPU	up to 8k	thinking mode
Qwen3 14B	Runs greatQwen3 14B verdict on the GeForce RTX 5060 Ti 16GB	est. 30.3 tok/s26.7–34.0calibrated estimate ±12%	Q4_K_M	11.1 / 16.0 GB	Full GPU	up to 8k	thinking mode
Phi-4	Runs great	est. 29.2 tok/s25.7–32.7calibrated estimate ±12%	Q4_K_M	11.6 / 16.0 GB	Full GPU	up to 16k	—
MiniCPM-V 4.7 35B-A3B	Runs well	est. 74.7 tok/s52.3–97.1theoretical estimate ±30%	Q4_K_M	15.4 / 16.0 GB + 7.1 GB RAM	MoE experts in RAM	up to 128k	Not fully on the GPU — Runs great needs the whole model in GPU memory thinking mode
Qwen3.6 35B-A3B	Runs wellQwen3.6 35B-A3B verdict on the GeForce RTX 5060 Ti 16GB	est. 69.5 tok/s48.7–90.4theoretical estimate ±30%	Q4_K_M	15.1 / 16.0 GB + 8.5 GB RAM	MoE experts in RAM	up to 128k	Not fully on the GPU — Runs great needs the whole model in GPU memory Q2_K (heavy quality loss): Runs great thinking mode
Qwen3.5 35B-A3B	Runs wellQwen3.5 35B-A3B verdict on the GeForce RTX 5060 Ti 16GB	est. 62.4 tok/s43.7–81.1theoretical estimate ±30%	Q4_K_M	15.1 / 16.0 GB + 8.9 GB RAM	MoE experts in RAM	up to 128k	Not fully on the GPU — Runs great needs the whole model in GPU memory UD-Q2_K_XL (heavy quality loss): Runs great thinking mode
Gemma 4 26B-A4B	Runs wellGemma 4 26B-A4B verdict on the GeForce RTX 5060 Ti 16GB	est. 59.2 tok/s41.4–76.9theoretical estimate ±30%	Q4_K_M	15.4 / 16.0 GB + 2.9 GB RAM	MoE experts in RAM	up to 128k	Not fully on the GPU — Runs great needs the whole model in GPU memory Try IQ4_XS: Runs great thinking mode
Qwen3 30B-A3B (2507)	Runs wellQwen3 30B-A3B (2507) verdict on the GeForce RTX 5060 Ti 16GB	est. 57.7 tok/s40.4–75.1theoretical estimate ±30%	Q4_K_M	15.6 / 16.0 GB + 4.9 GB RAM	MoE experts in RAM	up to 64k	Not fully on the GPU — Runs great needs the whole model in GPU memory Q2_K (heavy quality loss): Runs great
Nemotron 3.5 Lightning 30B-A3B	Runs well	est. 50.8 tok/s35.6–66.0theoretical estimate ±30%	Q4_K_M	14.8 / 16.0 GB + 11.9 GB RAM	MoE experts in RAM	up to 256k	Not fully on the GPU — Runs great needs the whole model in GPU memory thinking mode
K2-Horizon MoVA 36B-A4B	Runs well	est. 30.7 tok/s21.5–39.9theoretical estimate ±30%	Q4_K_M	15.5 / 16.0 GB + 9.6 GB RAM	MoE experts in RAM	up to 8k	Not fully on the GPU — Runs great needs the whole model in GPU memory thinking mode
DeepSeek R1 Distill Qwen 14B	Runs well	est. 29.6 tok/s26.0–33.1calibrated estimate ±12%	Q4_K_M	11.3 / 16.0 GB	Full GPU	up to 16k	Try IQ4_XS: Runs great thinking mode
Mistral Small 3.2 24B	Runs well	est. 14.8 tok/s11.8–17.7calibrated estimate ±20%	Q4_K_M	16.0 / 16.0 GB + 0.8 GB RAM	Partial offload	up to 8k	Try IQ4_XS: Runs great
Gemma 3 27B	Runs slowly	est. 9.7 tok/s7.8–11.7calibrated estimate ±20%	Q4_K_M	16.0 / 16.0 GB + 2.4 GB RAM	Partial offload	up to 64k	Less than 90% on the GPU — a GPU/CPU split is capped at Runs slowly Try IQ4_XS: Runs well Q2_K (heavy quality loss): Runs great
Qwen3.6 27B	Runs slowly	est. 9.5 tok/s7.6–11.4calibrated estimate ±20%	Q4_K_M	16.0 / 16.0 GB + 2.5 GB RAM	Partial offload	up to 64k	Try IQ4_XS: Runs well thinking mode
Muse Glimmer 30B	Runs slowly	est. 9.5 tok/s7.6–11.4calibrated estimate ±20%	Q4_K_M	16.0 / 16.0 GB + 2.6 GB RAM	Partial offload	up to 128k	Try IQ4_XS: Runs well thinking mode
Qwen3.5 27B	Runs slowlyQwen3.5 27B verdict on the GeForce RTX 5060 Ti 16GB	est. 8.9 tok/s7.1–10.7calibrated estimate ±20%	Q4_K_M	16.0 / 16.0 GB + 2.8 GB RAM	Partial offload	up to 64k	Try IQ4_XS: Runs well thinking mode
Qwen3.8 27B	Runs slowlyQwen3.8 27B verdict on the GeForce RTX 5060 Ti 16GB	est. 8.4 tok/s6.7–10.0calibrated estimate ±20%	Q4_K_M	16.0 / 16.0 GB + 3.2 GB RAM	Partial offload	up to 64k	Try IQ4_XS: Runs well thinking mode
Gemma 4 31B	Runs slowlyGemma 4 31B verdict on the GeForce RTX 5060 Ti 16GB	est. 7.0 tok/s5.6–8.4calibrated estimate ±20%	Q4_K_M	16.0 / 16.0 GB + 4.2 GB RAM	Partial offload	up to 64k	UD-Q2_K_XL (heavy quality loss): Runs well thinking mode
EXAONE 4.0 32B	Runs slowly	est. 6.1 tok/s4.9–7.4calibrated estimate ±20%	Q4_K_M	16.0 / 16.0 GB + 5.1 GB RAM	Partial offload	up to 64k	thinking mode
Granite 4.2 30B	Runs slowly	est. 5.8 tok/s4.7–7.0calibrated estimate ±20%	Q4_K_M	16.0 / 16.0 GB + 5.0 GB RAM	Partial offload	up to 16k	Q2_K (heavy quality loss): Runs well thinking mode
EXAONE 4.5 33B	Runs slowly	est. 5.6 tok/s4.5–6.7calibrated estimate ±20%	Q4_K_M	16.0 / 16.0 GB + 5.8 GB RAM	Partial offload	up to 64k	thinking mode
Qwen3 32B	Runs slowlyQwen3 32B verdict on the GeForce RTX 5060 Ti 16GB	est. 4.5 tok/s3.6–5.5calibrated estimate ±20%	Q4_K_M	16.0 / 16.0 GB + 7.1 GB RAM	Partial offload	up to 16k	Q2_K (heavy quality loss): Runs well thinking mode
DeepSeek R1 Distill Qwen 32B	Runs slowly	est. 4.5 tok/s3.6–5.4calibrated estimate ±20%	Q4_K_M	16.0 / 16.0 GB + 7.2 GB RAM	Partial offload	up to 16k	Q2_K (heavy quality loss): Runs well thinking mode
Nanbeige4.3 33B	Runs slowly	est. 4.3 tok/s3.5–5.2calibrated estimate ±20%	Q4_K_M	16.0 / 16.0 GB + 7.7 GB RAM	Partial offload	up to 16k	Q2_K (heavy quality loss): Runs well thinking mode
Kolibri 1	Won't runTry Q2_K (heavy quality loss): Runs well	est. 63.3 tok/s44.3–82.3theoretical estimate ±30%	Q2_K	15.0 / 16.0 GB + 16.3 GB RAM	MoE experts in RAM	up to 128k	Not fully on the GPU — Runs great needs the whole model in GPU memory With 64 GB RAM: Runs well thinking mode
Solar Open 100B	Won't runTry Q2_K (heavy quality loss): Runs slowly	est. 18.7 tok/s13.1–24.4theoretical estimate ±30%	Q2_K	14.7 / 16.0 GB + 25.8 GB RAM	MoE experts in RAM	up to 16k	Experts run from system RAM — Runs well needs 30 tok/s or more on this path With 64 GB RAM: Runs slowly thinking mode
Llama 3.3 70B	Won't runTry Q2_K (heavy quality loss): Runs slowlyLlama 3.3 70B verdict on the GeForce RTX 5060 Ti 16GB	est. 2.5 tok/s2.0–3.1calibrated estimate ±20%	Q2_K	16.0 / 16.0 GB + 14.2 GB RAM	Partial offload	up to 16k	—
DeepSeek V4 Flash 0731	Won't run	—	MXFP4	needs 155.1 GB	—	—	Needs about 155.1 GB; 15.4 GB of VRAM and 28.0 GB of RAM are free thinking mode
GLM-5.3 Flash	Won't run	—	Q4_K_M	needs 181.4 GB	—	—	Needs about 181.4 GB; 15.4 GB of VRAM and 28.0 GB of RAM are free thinking mode
gpt-oss-120b	Won't rungpt-oss-120b verdict on the GeForce RTX 5060 Ti 16GB	—	MXFP4	needs 63.6 GB	—	—	Needs about 63.6 GB; 15.4 GB of VRAM and 28.0 GB of RAM are free With 64 GB RAM: Runs slowly thinking mode
Laguna S 2.1	Won't run	—	Q4_K_M	needs 68.5 GB	—	—	Needs about 68.5 GB; 15.4 GB of VRAM and 28.0 GB of RAM are free With 64 GB RAM: Runs slowly thinking mode
Ling-3.0 Flash	Won't run	—	Q4_K_M	needs 75.4 GB	—	—	Needs about 75.4 GB; 15.4 GB of VRAM and 28.0 GB of RAM are free With 96 GB RAM: Runs well thinking mode
Qwen3.5 122B-A10B	Won't runQwen3.5 122B-A10B verdict on the GeForce RTX 5060 Ti 16GB	—	Q4_K_M	needs 76.6 GB	—	—	Needs about 76.6 GB; 15.4 GB of VRAM and 28.0 GB of RAM are free With 96 GB RAM: Runs slowly thinking mode
Solar Open 2 250B	Won't run	—	IQ4_XS	needs 136.5 GB	—	—	Needs about 136.5 GB; 15.4 GB of VRAM and 28.0 GB of RAM are free thinking mode

Models with a thinking mode spend extra tokens thinking, so their speed thresholds are 1.5× stricter (30 / 12 / 3 tok/s). With thinking turned off, a model may do one grade better.

Measured results on the GeForce RTX 5060 Ti 16GB

Public benchmarks we calibrate against. Their conditions (context, backend, flags) can differ from the estimates above.

Measured results on the GeForce RTX 5060 Ti 16GB
Model	Quant	Backend	Context	Prompt (tok/s)	Generation (tok/s)	Flags	Source	Measured
llama-2-7b	Q4_0	llama.cpp	512	3,737.0	90.9	—	github.com	2025-08-02
gpt-oss-20b	MXFP4	llama.cpp	2k	3,821.0	111.6	—	github.com	2025-08-20
Qwen3.8 27B	UD-IQ4_XS	llama.cpp	8k	—	27.6	KV q8_0	github.com	2026-10-11
Qwen3 8B	Q4_K_XL	llama.cpp	4k	2,965.0	69.2	CUDA	hardware-corner.net	2025-12-09
Llama 3.1 8B	Q4_K_M	ollama	512	3,666.0	81.4	CUDA, Windows 11, num_ctx 8192	github.com	2026-10-25
Gemma 3 12B	Q4_K_M	ollama	512	1,692.0	50.3	CUDA, Windows 11, num_ctx 8192	github.com	2026-10-25

Frequently asked questions

What is the largest model that runs entirely on the GeForce RTX 5060 Ti 16GB?

Muse Glimmer 30B at IQ4_XS (a 15.44 GB file) fits entirely in 16 GB with 8k context, at est. 20.2 tok/s (17.7–22.6, calibrated estimate ±12%).

How many local LLMs run well on the GeForce RTX 5060 Ti 16GB?

At Q4_K_M with 8k context, out of 59 tracked models: 28 run great, 9 run well, 12 run slowly and 10 won't run.

Can the GeForce RTX 5060 Ti 16GB run a 70B model like Llama 3.3 70B?

Runs slowly — Partial offload, Q2_K (heavy quality loss): est. 2.5 tok/s (2.0–3.1, calibrated estimate ±20%).

Speeds are estimates from memory bandwidth, calibrated against public benchmarks, and each one comes with an error band and a confidence label. Real results vary with drivers, backend, context length and thermals.