NVIDIA / discrete

NVIDIA GeForce RTX 5060 Ti 16GB

16 GB decides what fits. 448 GB/s decides how fast it runs once it does.

Open in the calculator →Best models for 16 GB, on every card that size →

Find one: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

Quick answer

The NVIDIA GeForce RTX 5060 Ti 16GB fits 153 of 320 sized models entirely in its 16 GB; the largest widely used one is gpt-oss-20b (13.8 GB at Q4_K_M). Memory decides what fits; its 448 GB/s of bandwidth decides how fast it answers.

Largest popular model that fitsgpt-oss-20b21B params · Q4_K_M · needs 13.8 GB~122 tok/s Faster than you readOpen in the calculator →
Best fast pickERNIE-4.5-21B-A3B-PT22B params · Q4_K_M · needs 14.7 GB~120 tok/s Faster than you readOpen in the calculator →
Most downloaded that fitsQwen3.5-9B9.7B params · Q4_K_M · needs 7.05 GB~63 tok/s Faster than you readOpen in the calculator →

At 8,192 tokens of context with the whole model in device memory. Speeds are estimates from memory bandwidth, not benchmarks run on this card.

NVIDIA GeForce RTX 5060 Ti 16GB · 16 GB · 448 GB/s · Q4_K_M where published · 8,192 tokensSpecificationpublished dataDecode speedestimate

153 of 320 fit entirely

153 of the 320 models the engine can size fit entirely in device memory at Q4_K_M where it is published, otherwise the nearest published format, and 8,192 tokens. 73 run with some layers on system memory (32 GB assumed), and 94 do not run at all.

48%of the models the engine can size fit entirely

16GB of device memory

448GB/s memory bandwidth

73run with system memory

94do not run at 8,192 tokens

Featured models · Q4_K_M at 8,192 tokens, including offload

Model	Needs	Verdict	Decode	Calculator
Qwen3.5-2B2.3B parameters	2.32 GB	fits	~247 tok/s	Open →
NVIDIA-Nemotron-3-Nano-4B-BF164.0B parameters	3.46 GB	fits	~114 tok/s	Open →
Qwen3-VL-8B-Instruct8.8B parameters	7.04 GB	fits	~52 tok/s	Open →
Qwen3.5-9B9.7B parameters	7.05 GB	fits	~63 tok/s	Open →
gemma-4-12B-it12B parameters	9.54 GB	fits	~37 tok/s	Open →
gpt-oss-20b21B parameters	13.8 GB	fits	~122 tok/s	Open →
Qwen3.8-27B28B parameters	18.6 GB	10 of 64 layers on system RAM (2.66 GB)	~13 tok/s with offload	Open →
Qwen3.6-27B28B parameters	18.6 GB	10 of 64 layers on system RAM (2.66 GB)	~13 tok/s with offload	Open →
Kimi-Linear-48B-A3B-Instruct49B parameters	30.4 GB	14 of 27 layers on system RAM (15.3 GB)	~55 tok/s with offload	Open →
Qwen3.8-Flash-Next180B parameters	111.6 GB	needs 96.7 GB of system RAM; 32 GB assumed	does not run	Open →
DeepSeek-V4-Flash-Vision-Exp305B parameters	188.1 GB	needs 174.2 GB of system RAM; 32 GB assumed	does not run	Open →
GLM-5.3-Flash321B parameters	198.3 GB	needs 184.1 GB of system RAM; 32 GB assumed	does not run	Open →

These examples are selected from prominent labs using the catalogue’s latest Hugging Face 30-day downloads and repository-creation freshness signal, with newer releases guaranteed a place. Offloaded rows assume 32 GB of system RAM, and a speed is only shown for a row that runs. Fitting in memory is not the same as loading: whether the runtime and version you have supports each architecture and format on this machine has not been tested here. Each “Open” link carries the same model, format, context and RAM into the calculator.

Fits entirely in NVIDIA GeForce RTX 5060 Ti 16GB memory — 153 of 320 sized models

Every model that fitswhole model resident · offload off

The most demanding model that fits is gpt-neox-20b at Q4_K_M: 15.7 GB of the 16 GB, leaving 0.25 GB spare.

Fully resident at 8,192 tokens (or the model’s own maximum, where that is shorter), offload off, at 448 GB/s. Each row is a run of the engine for this configuration; the rows start with current, prominent releases and “fits” is memory, not a tested runtime. Older or less prominent models remain available through this search and “Show all”.

Find a model in this list

Showing 40 of 153 models that fit.

Model	Format	Needs	Spare	Decode	Downloads / 30d	Calculator
Qwen3.5-9BQwen · 9.7B params	Q4_K_M	7.05 GB	8.95 GB	~63 tok/s	9M	Open →
Qwen3.5-4BQwen · 4.7B params	Q4_K_M	3.98 GB	12.0 GB	~113 tok/s	7.8M	Open →
gemma-4-12B-itGoogle · 12B params	Q4_K_M	9.54 GB	6.46 GB	~37 tok/s	1.9M	Open →
Qwen3.5-2BQwen · 2.3B params	Q4_K_M	2.32 GB	13.7 GB	~247 tok/s	4.9M	Open →
gemma-4-E4B-itGoogle · 8.0B params	Q4_K_M	5.86 GB	10.1 GB	~101 tok/s	4.4M	Open →
NVIDIA-Nemotron-3-Nano-4B-BF16NVIDIA · 4.0B params	Q4_K_M	3.46 GB	12.5 GB	~114 tok/s	3.4M	Open →
gemma-4-E2B-itGoogle · 5.1B params	Q4_K_M	4.02 GB	12.0 GB	~266 tok/s	3M	Open →
Qwen3.5-0.8BQwen · 873M params	Q4_K_M	1.46 GB	14.5 GB	~541 tok/s	2.6M	Open →
Qwen3-VL-8B-InstructQwen · 8.8B params	Q4_K_M	7.04 GB	8.96 GB	~52 tok/s	14.6M	Open →
North-Micro-Vision-InstructCohereLabs · 2.5B params	Q4_K_M	2.99 GB	13.0 GB	~162 tok/s	180.7K	Open →
granite-4.2-8bIBM · 8.8B params	Q4_K_M	7.49 GB	8.51 GB	~47 tok/s	128.4K	Open →
granite-4.1-3bIBM · 3.4B params	Q4_K_M	3.72 GB	12.3 GB	~110 tok/s	521.8K	Open →
LFM2.5-2.6BLiquidAI · 2.7B params	Q4_K_M	2.61 GB	13.4 GB	~176 tok/s	108.1K	Open →
Qwen3-VL-4B-InstructQwen · 4.4B params	Q4_K_M	4.72 GB	11.3 GB	~82 tok/s	3.5M	Open →
Qwen3-VL-2B-InstructQwen · 2.1B params	Q4_K_M	3.05 GB	13.0 GB	~149 tok/s	2.8M	Open →
granite-4.2-3bIBM · 3.7B params	Q4_K_M	3.72 GB	12.3 GB	~110 tok/s	50.6K	Open →
LFM2.5-230MLiquidAI · 230M params	Q4_K_M	1.05 GB	14.9 GB	~1230 tok/s	88.6K	Open →
Qwen3-0.6BQwen · 752M params	Q4_K_M	2.22 GB	13.8 GB	~229 tok/s	29.7M	Open →
gpt-oss-20bOpenAI · 21B params	Q4_K_M	13.8 GB	2.24 GB	~122 tok/s	6.6M	Open →
granite-4.1-8bIBM · 8.8B params	Q4_K_M	7.49 GB	8.51 GB	~47 tok/s	179.3K	Open →
LFM2.5-VL-3BLiquidAI · 3.1B params	Q4_K_M	2.85 GB	13.1 GB	~176 tok/s	26.7K	Open →
MiMo-V2.6-Distill-Qwen-9BNEWXiaomiMiMo · 9.4B params	Q4_K_M	6.90 GB	9.10 GB	~63 tok/s	14.6K	Open →
Qwen3-4B-Instruct-2507Qwen · 4.0B params	Q4_K_M	4.72 GB	11.3 GB	~82 tok/s	3.7M	Open →
Ling-3.0-tinyinclusionAI · 7.9B params	Q4_K_M	5.85 GB	10.1 GB	~355 tok/s	17.7K	Open →
Qwen3-8BQwen · 8.2B params	Q4_K_M	7.04 GB	8.96 GB	~52 tok/s	10.7M	Open →
Olmo-3-7B-Instructallenai · 7.3B params	Q4_K_M	7.96 GB	8.04 GB	~44 tok/s	481.7K	Open →
Qwen3-4BQwen · 4.0B params	Q4_K_M	4.51 GB	11.5 GB	~82 tok/s	7.8M	Open →
LFM2.5-8B-A1BLiquidAI · 8.5B params	Q4_K_M	6.06 GB	9.94 GB	~275 tok/s	32.4K	Open →
LFM2.5-350MLiquidAI · 354M params	Q4_K_M	1.13 GB	14.9 GB	~794 tok/s	69.9K	Open →
Hy-MT2-1.8BTencent · 2.0B params	Q4_K_M	2.47 GB	13.5 GB	~183 tok/s	28.9K	Open →
LLaDA2.0-miniinclusionAI · 16B params	Q4_K_M	11.0 GB	5.00 GB	~285 tok/s	217.2K	Open →
LFM2.5-1.2B-InstructLiquidAI · 1.2B params	Q4_K_M	1.63 GB	14.4 GB	~293 tok/s	119.2K	Open →
Ministral-3-14B-Instruct-2512Mistral AI · 14B params	Q4_K_M	10.4 GB	5.62 GB	~33 tok/s	251K	Open →
Qwen3-1.7BQwen · 2.0B params	Q4_K_M	3.02 GB	13.0 GB	~149 tok/s	3.1M	Open →
Nemotron-3.5-Content-SafetyNVIDIA · 4.3B params	Q4_K_M	3.73 GB	12.3 GB	~110 tok/s	14.1K	Open →
Hy-MT2-7BTencent · 8.0B params	Q4_K_M	6.50 GB	9.50 GB	~53 tok/s	15.4K	Open →
Qwen3-14BQwen · 15B params	Q4_K_M	11.1 GB	4.86 GB	~31 tok/s	2.7M	Open →
GLM-4.6V-Flashzai-org · 10B params	Q4_K_M	7.45 GB	8.55 GB	~53 tok/s	103.7K	Open →
Qwen2.5-VL-7B-InstructQwen · 8.3B params	Q4_K_M	6.36 GB	9.64 GB	~63 tok/s	5.8M	Open →
SmolLM3-3BHuggingFaceTB · 3.1B params	Q4_K_M	3.46 GB	12.5 GB	~121 tok/s	617K	Open →

Sized at the format most people actually download, not at FP16. The catalogue ordered by downloads →

Rent it instead

Rent an RTX 5060 Ti 16GB by the hour

What the NVIDIA GeForce RTX 5060 Ti 16GB costs on Vast.ai and RunPod right now, per machine, from one card to eight. Every price is read from the provider's own API.

Prices · 18:30 UTC, 22 Sept

1× RTX 5060 Ti 16GB · 16 GB

$0.12per hour

Vast.ai · Marketplace · 99.1% reliableRent on Vast.ai

2× RTX 5060 Ti 16GB · 32 GB

$0.26per hour

Vast.ai · Marketplace · 99.5% reliableRent on Vast.ai

4× RTX 5060 Ti 16GB · 64 GB

$0.51per hour

Vast.ai · Marketplace · 99.5% reliableRent on Vast.ai

8× RTX 5060 Ti 16GB · 128 GB

$1.60per hour

Vast.ai · Marketplace · 99.7% reliableRent on Vast.ai

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Hours of use per day4 h

$ per kWh

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Watts start at the NVIDIA GeForce RTX 5060 Ti 16GB’s published board power, an upper bound: decoding rarely holds a card at its limit. Rent starts at the cheapest live price (Vast.ai). The electricity price is a placeholder — put yours in.

At 4 h a day, renting costs $175.20 a year.

That is $14.60 a month, and nothing when the machine is stopped.
Owning would cost $127.75 a year in electricity, on top of the price.
Every $1,000 of purchase price takes 21.1 years of this use to earn back.

Enter the price you would pay to see the exact break-even.

On-demand prices for the whole machine. Vast hosts below 98% measured reliability are left out; RunPod’s Community Cloud is vetted third-party hosts and its Secure Cloud is data-centre capacity. Every rentable card compared.

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Find the NVIDIA GeForce RTX 5060 Ti 16GB: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

Under the hood

The specification behind every figure

What the manufacturer publishes for this device, and the pages it was read from.

Manufacturer specification

Memory scope	dedicated
Capacity	16 GB
Published options	16 GB
Bandwidth	448 GB/s
Memory type	GDDR7
Bus width	128-bit
FP32 peak	Not published
Dense matrix peak	Not published without sparsity
Power	Not published

Source ledger

NVIDIA GeForce Graphics Cards — Compare Specs ↗

Retrieved 2026-08-31

Caveats

NVIDIA's comparison page does not publish a peak throughput figure for this card, so no compute roof is priced for it and time to first token is withheld.
No dense matrix throughput is catalogued for this device: The RTX Blackwell whitepaper's appendices cover GB202, GB203 and GB205 only; the GB206 part this card uses has no published tensor table. Time to first token is withheld rather than priced against a sparsity figure.

Published capacity is a hardware ceiling, not guaranteed free runtime memory. The calculator shows the runtime reserve separately rather than folding it into a single number.

Run the diagnostic on the NVIDIA GeForce RTX 5060 Ti 16GB →