Can LLaVA 1.5 7B run on RTX 4000 Ada Laptop 12GB?

Q: What should I upgrade first if LLaVA 1.5 7B feels slow on RTX 4000 Ada Laptop 12GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

C53Usable

Estimated from fit model

LLaVA 1.5 7B needs ~14.2 GB VRAM. RTX 4000 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~36 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 14.2 GB, 35.8 tok/s, Very compromised (needs ~0.7 GB host RAM)

14.2 GB required12.0 GB available

118% VRAM needed

2.2 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.7 GB host RAM)

Decode

35.8 tok/s

TTFT

5401 ms

Safe context

Memory

14.2 GB / 12.0 GB

Offload

20%

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsLLaVA 1.5 7B on RTX 4000 Ada Laptop 12GB
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: 35.8 tok/s decode · 5.4s TTFT (warm) · 90 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	67.9 tok/s	1554 ms	4K
Coding	C	Very compromised (needs ~0.7 GB host RAM)	35.8 tok/s	5401 ms	4K
Agentic Coding	F	Too heavy	14.2 tok/s	19784 ms	4K
Reasoning	C	Very compromised (needs ~0.7 GB host RAM)	35.8 tok/s	6383 ms	4K
RAG	F	Too heavy	14.2 tok/s	24730 ms

Quantization options

How LLaVA 1.5 7B (7B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B67
Q3_K_S	3	3.4 GB	Low	B68
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 5060 Ti 16GBBudget pick

16 GB VRAM (+4)448 GB/s (+16)

Removes host-memory offload, which is usually the single biggest latency and throughput win.65 tok/s decode

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

Raises estimated decode speed by about 82%.

~$449 MSRP

RTX 4060 Ti 16GBBest value

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.45.3 tok/s decode

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

Raises estimated decode speed by about 27%.

~$499 MSRP

RTX 5070 Ti 16GBNVIDIA upgrade

16 GB VRAM (+4)896 GB/s (+464)

Removes host-memory offload, which is usually the single biggest latency and throughput win.133 tok/s decode

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

Raises estimated decode speed by about 272%.

~$749 MSRP

Frequently asked questions

Can RTX 4000 Ada Laptop 12GB run LLaVA 1.5 7B?

Yes, RTX 4000 Ada Laptop 12GB can run LLaVA 1.5 7B with a C grade (Very compromised (needs ~0.7 GB host RAM)). Expected decode speed: 35.8 tok/s.

How much VRAM does LLaVA 1.5 7B need?

LLaVA 1.5 7B (7B parameters) requires approximately 14.2 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 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, LLaVA 1.5 7B achieves approximately 35.8 tokens per second decode speed with a time-to-first-token of 5401ms using Q4_K_M quantization.

Can RTX 4000 Ada Laptop 12GB run LLaVA 1.5 7B for coding?

For coding workloads, LLaVA 1.5 7B on RTX 4000 Ada Laptop 12GB receives a C grade with 35.8 tok/s and 4K context.

What context window can LLaVA 1.5 7B use on RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, 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 4000 Ada Laptop 12GB?

See all results for RTX 4000 Ada Laptop 12GB See all hardware for LLaVA 1.5 7B

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