Can LLaVA 1.6 13B run on RX 7900 XT 20GB?

BARELY — Tight on Memory

B64Good

Estimated from fit model

LLaVA 1.6 13B needs ~23.0 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: 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, 33.7 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

33.7 tok/s

TTFT

5743 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 RX 7900 XT 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: 33.7 tok/s decode · 5.7s TTFT (warm) · 84 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	60.5 tok/s	1745 ms	4K
Coding	B	Very compromised (needs ~1 GB host RAM)	33.7 tok/s	5743 ms	4K
Agentic Coding	F	Too heavy	13.8 tok/s	20443 ms	4K
Reasoning	B	Very compromised (needs ~1 GB host RAM)	33.7 tok/s	6787 ms	4K
RAG	F	Too heavy	13.8 tok/s	25554 ms	4K

Quantization options

How LLaVA 1.6 13B (13B params) fits at each quantization level on RX 7900 XT 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

アップグレードオプション

LLaVA 1.6 13Bを快適に動かすハードウェア

RX 7900 XTX 24GBコスパ重視

24 GB VRAM (+4)960 GB/s (+160)

Removes host-memory offload, which is usually the single biggest latency and throughput win.87.2 tok/sデコード

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

Raises estimated decode speed by about 159%.

〜$999 MSRP

Radeon AI PRO R9700 32GBコスパ最良

32 GB VRAM (+12)

Removes host-memory offload, which is usually the single biggest latency and throughput win.47.6 tok/sデコード

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

Raises estimated decode speed by about 41%.

〜$1,899 MSRP

Radeon Pro W6800 32GBAMDアップグレード

32 GB VRAM (+12)

Removes host-memory offload, which is usually the single biggest latency and throughput win.36.2 tok/sデコード

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

Adds memory headroom for longer context windows and future model growth.

〜$2,249 MSRP

Frequently asked questions

Can RX 7900 XT 20GB run LLaVA 1.6 13B?

Yes, RX 7900 XT 20GB can run LLaVA 1.6 13B with a B grade (Very compromised (needs ~1 GB host RAM)). Expected decode speed: 33.7 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 RX 7900 XT 20GB?

On RX 7900 XT 20GB, LLaVA 1.6 13B achieves approximately 33.7 tokens per second decode speed with a time-to-first-token of 5743ms using Q4_K_M quantization.

Can RX 7900 XT 20GB run LLaVA 1.6 13B for coding?

For coding workloads, LLaVA 1.6 13B on RX 7900 XT 20GB receives a B grade with 33.7 tok/s and 4K context.

What context window can LLaVA 1.6 13B use on RX 7900 XT 20GB?

On RX 7900 XT 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 RX 7900 XT 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 RX 7900 XT 20GB See all hardware for LLaVA 1.6 13B

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