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URL: https://willitrunai.com/can-run/llava-1.6-13b-on-rtx-4000-ada-20gb

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Can LLaVA 1.6 13B run on RTX 4000 Ada 20GB?

BARELY — Tight on Memory

B62Good
○Estimated from fit model

LLaVA 1.6 13B needs ~23.0 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~20 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload
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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) — 23.0 GB, 19.7 tok/s, Very compromised (needs ~1 GB host RAM)
23.0 GB required20.0 GB available
115% VRAM needed

3.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1 GB host RAM)

Decode

19.7 tok/s

TTFT

9817 ms

Safe context

4K

Memory

23.0 GB / 20.0 GB

Offload

10%

Memory breakdown

Weights7.9 GB
KV Cache12.2 GB
Runtime0.9 GB
Headroom2.0 GB

See how fast it feels

See how fast it feelsLLaVA 1.6 13B on RTX 4000 Ada 20GB
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: 19.7 tok/s decode · 9.8s TTFT (warm) · 49 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 10% 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 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatATight fit35.4 tok/s2982 ms4K
CodingBVery compromised (needs ~1 GB host RAM)19.7 tok/s9817 ms4K
Agentic CodingFToo heavy8.1 tok/s34946 ms4K
ReasoningBVery compromised (needs ~1 GB host RAM)19.7 tok/s11601 ms4K
RAGFToo heavy8.1 tok/s43682 ms4K

Quantization options

How LLaVA 1.6 13B (13B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.1 GB
LowA70
Q3_K_S
3
6.4 GB
LowA71
NVFP4
4

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

Upgrade options

Hardware that runs LLaVA 1.6 13B well

👁 NVIDIA
RTX 3090 24GBBudget pick
24 GB VRAM (+4)936 GB/s (+576)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.81 tok/s decode

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

Raises estimated decode speed by about 311%.

~$1,499 MSRP

👁 NVIDIA
RTX 4090 24GBBest value
24 GB VRAM (+4)1008 GB/s (+648)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.101.4 tok/s decode

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

Raises estimated decode speed by about 415%.

~$1,599 MSRP

👁 NVIDIA
RTX PRO 4000 Blackwell 24GBNVIDIA upgrade
24 GB VRAM (+4)672 GB/s (+312)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.71.2 tok/s decode

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

Raises estimated decode speed by about 261%.

~$1,599 MSRP

Frequently asked questions

See all results for RTX 4000 Ada 20GBSee all hardware for LLaVA 1.6 13B
7.3 GB
Medium
A72
Q4_K_M
4
7.9 GB
MediumA72
Q5_K_M
5
9.4 GB
HighA74
Q6_K
6
10.7 GB
HighA74
Q8_0Best for your GPU
8
13.9 GB
Very HighA74
F16
16
26.7 GB
MaximumF0

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.