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

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

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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	35.4 tok/s	2982 ms	4K
Coding	B	Very compromised (needs ~1 GB host RAM)	19.7 tok/s	9817 ms	4K
Agentic Coding	F	Too heavy	8.1 tok/s	34946 ms	4K
Reasoning	B	Very compromised (needs ~1 GB host RAM)	19.7 tok/s	11601 ms	4K
RAG	F	Too heavy	8.1 tok/s	43682 ms	4K

Quantization options

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

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A70
Q3_K_S	3	6.4 GB	Low	A71
NVFP4	4	7.3 GB	Medium	A72
Q4_K_M	4	7.9 GB	Medium	A72
Q5_K_M	5	9.4 GB	High	A74
Q6_K	6	10.7 GB	High	A74
Q8_0Best for your GPU	8	13.9 GB	Very High	A74
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

Upgrade-Optionen

Hardware, die LLaVA 1.6 13B gut ausführt

RTX 3090 24GBGünstige Wahl

24 GB VRAM (+4)936 GB/s (+576)

Removes host-memory offload, which is usually the single biggest latency and throughput win.81 tok/s Dekodierung

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

Raises estimated decode speed by about 311%.

ca. $1,499 MSRP

RTX 4090 24GBBestes Preis-Leistungs-Verhältnis

24 GB VRAM (+4)1008 GB/s (+648)

Removes host-memory offload, which is usually the single biggest latency and throughput win.101.4 tok/s Dekodierung

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

Raises estimated decode speed by about 415%.

ca. $1,599 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA-Upgrade

24 GB VRAM (+4)672 GB/s (+312)

Removes host-memory offload, which is usually the single biggest latency and throughput win.71.2 tok/s Dekodierung

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

Raises estimated decode speed by about 261%.

ca. $1,599 MSRP

Frequently asked questions

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

Yes, RTX 4000 Ada 20GB can run LLaVA 1.6 13B with a B grade (Very compromised (needs ~1 GB host RAM)). Expected decode speed: 19.7 tok/s.

How much VRAM does LLaVA 1.6 13B need?

LLaVA 1.6 13B (13B parameters) requires approximately 23.0 GB of memory with Q4_K_M quantization.

What is the best quantization for LLaVA 1.6 13B?

The recommended quantization for LLaVA 1.6 13B is Q4_K_M, which balances quality and memory efficiency.

What speed will LLaVA 1.6 13B run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, LLaVA 1.6 13B achieves approximately 19.7 tokens per second decode speed with a time-to-first-token of 9817ms using Q4_K_M quantization.

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

For coding workloads, LLaVA 1.6 13B on RTX 4000 Ada 20GB receives a B grade with 19.7 tok/s and 4K context.

What context window can LLaVA 1.6 13B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, LLaVA 1.6 13B 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 1.6 13B feels slow on RTX 4000 Ada 20GB?

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.

See all results for RTX 4000 Ada 20GB See all hardware for LLaVA 1.6 13B

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