Can LLaVA 1.6 13B run on NVIDIA L4 24GB?

YES — With Offload

A73Great

○Estimated from fit model

LLaVA 1.6 13B needs ~23.4 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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) — 23.4 GB, 25.8 tok/s, Runs with offload

23.4 GB required24.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

25.8 tok/s

TTFT

7498 ms

Safe context

Memory

23.4 GB / 24.0 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsLLaVA 1.6 13B on NVIDIA L4 24GB
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: 25.8 tok/s decode · 7.5s TTFT (warm) · 65 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	25.8 tok/s	4090 ms	4K
Coding	A	Runs with offload	25.8 tok/s	7498 ms	4K
Agentic Coding	F	Too heavy	8.4 tok/s	33436 ms	4K
Reasoning	A	Runs with offload	25.8 tok/s	8861 ms	4K
RAG	F	Too heavy	8.4 tok/s	41794 ms	4K

Quantization options

How LLaVA 1.6 13B (13B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B69
Q3_K_S	3	6.4 GB	Low	B70
NVFP4	4	7.3 GB	Medium	A70
Q4_K_M	4	7.9 GB	Medium	A71
Q5_K_M	5	9.4 GB	High	A71
Q6_K	6	10.7 GB	High	A72
Q8_0Best for your GPU	8	13.9 GB	Very High	A73
F16	16	26.7 GB	Maximum	F0

Get started

Copy-paste commands to run LLaVA 1.6 13B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "liuhaotian/llava-v1.6-mistral-7b" \
 --hf-file "llava-v1.6-mistral-7b-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Your hardware

More models your NVIDIA L4 24GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	21.2 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	8.9 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	6.2 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	A	13.6 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	30.5 tok/s

Frequently asked questions

See all results for NVIDIA L4 24GB See all hardware for LLaVA 1.6 13B

URL: https://willitrunai.com/can-run/llava-1.6-13b-on-l4-24gb