Can LLaVA 1.5 7B run on RTX 2000 Ada 16GB?

Q: What should I upgrade first if LLaVA 1.5 7B feels slow on RTX 2000 Ada 16GB?

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

YES — Tight Fit

B70Good

Estimated from fit model

LLaVA 1.5 7B needs ~14.9 GB VRAM. RTX 2000 Ada 16GB has 16.0 GB. With Q4_K_M quantization, expect ~51 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced

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, 51.3 tok/s, Tight fit

14.9 GB required16.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

51.3 tok/s

TTFT

3777 ms

Safe context

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 2000 Ada 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: 51.3 tok/s decode · 3.8s TTFT (warm) · 128 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	51.3 tok/s	2060 ms	4K
Coding	B	Tight fit	51.3 tok/s	3777 ms	4K
Agentic Coding	F	Too heavy	18.4 tok/s	15288 ms	4K
Reasoning	B	Tight fit	51.3 tok/s	4464 ms	4K
RAG	F	Too heavy	18.4 tok/s	19110 ms	4K

Quantization options

How LLaVA 1.5 7B (7B params) fits at each quantization level on RTX 2000 Ada 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B65
Q3_K_S	3	3.4 GB	Low	B65
NVFP4	4

Get started

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

Run

ollama run llava

Upgrade options

Hardware that runs LLaVA 1.5 7B well

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+4)360 GB/s (+72)

Raises estimated decode speed by about 28%.65.8 tok/s decode

Raises estimated decode speed by about 28%.

Adds memory headroom for longer context windows and future model growth.

~$1,250 MSRP

RTX 3090 24GBBest value

24 GB VRAM (+8)936 GB/s (+648)

Raises estimated decode speed by about 91%.98 tok/s decode

Raises estimated decode speed by about 91%.

Adds memory headroom for longer context windows and future model growth.

~$1,499 MSRP

RTX 4090 24GBNVIDIA upgrade

24 GB VRAM (+8)1008 GB/s (+720)

Raises estimated decode speed by about 91%.98 tok/s decode

Raises estimated decode speed by about 91%.

Adds memory headroom for longer context windows and future model growth.

~$1,599 MSRP

Frequently asked questions

Can RTX 2000 Ada 16GB run LLaVA 1.5 7B?

Yes, RTX 2000 Ada 16GB can run LLaVA 1.5 7B with a B grade (Tight fit). Expected decode speed: 51.3 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 2000 Ada 16GB?

On RTX 2000 Ada 16GB, LLaVA 1.5 7B achieves approximately 51.3 tokens per second decode speed with a time-to-first-token of 3777ms using Q4_K_M quantization.

Can RTX 2000 Ada 16GB run LLaVA 1.5 7B for coding?

For coding workloads, LLaVA 1.5 7B on RTX 2000 Ada 16GB receives a B grade with 51.3 tok/s and 4K context.

What context window can LLaVA 1.5 7B use on RTX 2000 Ada 16GB?

On RTX 2000 Ada 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 2000 Ada 16GB?

See all results for RTX 2000 Ada 16GB See all hardware for LLaVA 1.5 7B

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