Can LLaVA 1.6 13B run on NVIDIA T4 16GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

LLaVA 1.6 13B needs ~22.9 GB but NVIDIA T4 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 22.9 GB, exceeds 16.0 GB available

22.9 GB required16.0 GB available

143% VRAM needed

6.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

8.7 tok/s

TTFT

22317 ms

Safe context

Memory

22.9 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLLaVA 1.6 13B on NVIDIA T4 16GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 8.7 tok/s decode · 22.3s TTFT (warm) · 22 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 22.9 GB, but this setup only exposes 16.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload (needs ~0.4 GB host RAM)	17.1 tok/s	6171 ms	4K
Coding	F	Too heavy	8.7 tok/s	22317 ms	4K
Agentic Coding	F	Too heavy	3.9 tok/s	71573 ms	4K
Reasoning	F	Too heavy	8.7 tok/s	26375 ms	4K
RAG	F	Too heavy	3.9 tok/s	89467 ms	4K

Quantization options

How LLaVA 1.6 13B (13B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A72
Q3_K_S	3	6.4 GB	Low	A73
NVFP4	4

Upgrade options

Hardware that runs LLaVA 1.6 13B well

RTX 4000 Ada 20GBBest value

20 GB VRAM (+4)360 GB/s (+40)

Makes the model fit on the accelerator instead of staying completely out of reach.19.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 121%.

~$1,250 MSRP

RTX 3090 24GBBudget pick

24 GB VRAM (+8)936 GB/s (+616)

Makes the model fit on the accelerator instead of staying completely out of reach.82.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,499 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+8)672 GB/s (+352)

Makes the model fit on the accelerator instead of staying completely out of reach.71.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

Frequently asked questions

Can NVIDIA T4 16GB run LLaVA 1.6 13B?

No, LLaVA 1.6 13B requires more memory than NVIDIA T4 16GB provides.

How much VRAM does LLaVA 1.6 13B need?

LLaVA 1.6 13B (13B parameters) requires approximately 22.9 GB of memory with Q4_K_M quantization.

What is the best quantization for LLaVA 1.6 13B?

The recommended quantization for LLaVA 1.6 13B is Q4_K_M, which balances quality and memory efficiency.

What speed will LLaVA 1.6 13B run at on NVIDIA T4 16GB?

On NVIDIA T4 16GB, LLaVA 1.6 13B achieves approximately 8.7 tokens per second decode speed with a time-to-first-token of 22317ms using Q4_K_M quantization.

Can NVIDIA T4 16GB run LLaVA 1.6 13B for coding?

For coding workloads, LLaVA 1.6 13B on NVIDIA T4 16GB receives a F grade with 8.7 tok/s and 4K context.

What context window can LLaVA 1.6 13B use on NVIDIA T4 16GB?

On NVIDIA T4 16GB, LLaVA 1.6 13B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

What should I upgrade first if LLaVA 1.6 13B feels slow on NVIDIA T4 16GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for NVIDIA T4 16GB See all hardware for LLaVA 1.6 13B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/llava-1.6-13b-on-t4-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: