Can LLaVA 1.6 13B run on RTX 5000 Ada 32GB?

YES — Runs Great

A79Great

Estimated from fit model

LLaVA 1.6 13B needs ~24.5 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~58 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 24.5 GB, 58.1 tok/s, Runs well

24.5 GB required32.0 GB available

77% VRAM used

Fit status

Runs well

Decode

58.1 tok/s

TTFT

3332 ms

Safe context

Memory

24.5 GB / 32.0 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsLLaVA 1.6 13B on RTX 5000 Ada 32GB
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: 58.1 tok/s decode · 3.3s TTFT (warm) · 145 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	58.1 tok/s	1817 ms	4K
Coding	A	Runs well	58.1 tok/s	3332 ms	4K
Agentic Coding	B	Very compromised (needs ~1 GB host RAM)	32.6 tok/s	8644 ms	4K
Reasoning	A	Runs well	58.1 tok/s	3937 ms	4K
RAG	B	Very compromised (needs ~1 GB host RAM)	32.6 tok/s	10805 ms	4K

Quantization options

How LLaVA 1.6 13B (13B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B67
Q3_K_S	3	6.4 GB	Low	B68
NVFP4	4	7.3 GB	Medium	B68
Q4_K_M	4	7.9 GB	Medium	B68
Q5_K_M	5	9.4 GB	High	B69
Q6_K	6	10.7 GB	High	B69
Q8_0	8	13.9 GB	Very High	A71
F16Best for your GPU	16	26.7 GB	Maximum	A72

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 RTX 5000 Ada 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	69.7 tok/s
Qwen 3.5 27B	27B	S	30.2 tok/s
Qwen 3.6 27B	27B	S	30.3 tok/s
Qwen 3.6 35B A3B	35B	S	58.6 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	72.1 tok/s

Frequently asked questions

Can RTX 5000 Ada 32GB run LLaVA 1.6 13B?

Yes, RTX 5000 Ada 32GB can run LLaVA 1.6 13B with a A grade (Runs well). Expected decode speed: 58.1 tok/s.

How much VRAM does LLaVA 1.6 13B need?

LLaVA 1.6 13B (13B parameters) requires approximately 24.5 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 5000 Ada 32GB?

On RTX 5000 Ada 32GB, LLaVA 1.6 13B achieves approximately 58.1 tokens per second decode speed with a time-to-first-token of 3332ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run LLaVA 1.6 13B for coding?

For coding workloads, LLaVA 1.6 13B on RTX 5000 Ada 32GB receives a A grade with 58.1 tok/s and 4K context.

What context window can LLaVA 1.6 13B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, 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.

See all results for RTX 5000 Ada 32GB See all hardware for LLaVA 1.6 13B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/llava-1.6-13b-on-rtx-5000-ada-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: