Will It Run AI

Can LLaVA 1.5 7B run on RTX 4060 Ti 16GB?

YES — Tight Fit

B70Good
Estimated from fit model

LLaVA 1.5 7B needs ~14.9 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced
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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) — 14.9 GB, 49.2 tok/s, Tight fit
14.9 GB required16.0 GB available
93% VRAM used

Fit status

Tight fit

Decode

49.2 tok/s

TTFT

3932 ms

Safe context

4K

Memory

14.9 GB / 16.0 GB

Memory breakdown

Weights4.3 GB
KV Cache7.8 GB
Runtime1.2 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsLLaVA 1.5 7B on RTX 4060 Ti 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: 49.2 tok/s decode · 3.9s TTFT (warm) · 123 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatARuns well49.2 tok/s2145 ms4K
CodingBTight fit49.2 tok/s3932 ms4K
Agentic CodingFToo heavy17.7 tok/s15916 ms4K
ReasoningBTight fit49.2 tok/s4647 ms4K
RAGFToo heavy17.7 tok/s19895 ms4K

Quantization options

How LLaVA 1.5 7B (7B params) fits at each quantization level on RTX 4060 Ti 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowB65
Q3_K_S
3
3.4 GB
LowB65
NVFP4
4
3.9 GB
MediumB66
Q4_K_M
4
4.3 GB
MediumB66
Q5_K_M
5
5.0 GB
HighB67
Q6_K
6
5.7 GB
HighB67
Q8_0Best for your GPU
8
7.5 GB
Very HighB69
F16
16
14.3 GB
MaximumF0

Get started

Copy-paste commands to run LLaVA 1.5 7B on your machine.

Run

ollama run llava

Opciones de mejora

Hardware que ejecuta bien LLaVA 1.5 7B

NVIDIARTX 4000 Ada 20GBOpción económica
20 GB VRAM (+4)360 GB/s (+72)
A
Sube la velocidad estimada de decodificación alrededor de un 34%.65.8 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 34%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,250 MSRP

NVIDIARTX 3090 24GBMejor relación calidad-precio
24 GB VRAM (+8)936 GB/s (+648)
A
Sube la velocidad estimada de decodificación alrededor de un 99%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 99%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,499 MSRP

NVIDIARTX 4090 24GBMejora NVIDIA
24 GB VRAM (+8)1008 GB/s (+720)
A
Sube la velocidad estimada de decodificación alrededor de un 99%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 99%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,599 MSRP

Frequently asked questions

Can RTX 4060 Ti 16GB run LLaVA 1.5 7B?

Yes, RTX 4060 Ti 16GB can run LLaVA 1.5 7B with a B grade (Tight fit). Expected decode speed: 49.2 tok/s.

How much VRAM does LLaVA 1.5 7B need?

LLaVA 1.5 7B (7B parameters) requires approximately 14.9 GB of memory with Q4_K_M quantization.

What is the best quantization for LLaVA 1.5 7B?

The recommended quantization for LLaVA 1.5 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will LLaVA 1.5 7B run at on RTX 4060 Ti 16GB?

On RTX 4060 Ti 16GB, LLaVA 1.5 7B achieves approximately 49.2 tokens per second decode speed with a time-to-first-token of 3932ms using Q4_K_M quantization.

Can RTX 4060 Ti 16GB run LLaVA 1.5 7B for coding?

For coding workloads, LLaVA 1.5 7B on RTX 4060 Ti 16GB receives a B grade with 49.2 tok/s and 4K context.

What context window can LLaVA 1.5 7B use on RTX 4060 Ti 16GB?

On RTX 4060 Ti 16GB, LLaVA 1.5 7B 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 1.5 7B feels slow on RTX 4060 Ti 16GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 4060 Ti 16GBSee all hardware for LLaVA 1.5 7B
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