Can LLaVA 1.6 13B run on RX 9060 XT 16GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

LLaVA 1.6 13B needs ~22.6 GB but RX 9060 XT 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity

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) — 22.6 GB, exceeds 16.0 GB available

22.6 GB required16.0 GB available

141% VRAM needed

6.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.2 tok/s

TTFT

21079 ms

Safe context

Memory

22.6 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLLaVA 1.6 13B on RX 9060 XT 16GB
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: 9.2 tok/s decode · 21.1s TTFT (warm) · 23 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 22.6 GB, but this setup only exposes 16.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload (needs ~0.3 GB host RAM)	17.8 tok/s	5934 ms	4K
Coding	F	Too heavy	9.2 tok/s	21079 ms	4K
Agentic Coding	F	Too heavy	3.8 tok/s	73846 ms	4K
Reasoning	F	Too heavy	9.2 tok/s	24911 ms	4K
RAG	F	Too heavy	3.8 tok/s	92307 ms	4K

Inference speed

LLaVA 1.6 13B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for LLaVA 1.6 13B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~151 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	151.4	Fits
RTX 4090 24GB	24 GB	Q4_K_M	96.6	Offloads
RX 7900 XTX 24GB	24 GB	Q4_K_M	87.2	Offloads
RTX 3090 24GB	24 GB	Q4_K_M	82.6	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	70.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	58.5	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	55.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	38.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	38.2	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	30.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	27.7	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	27.1	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.3	Fits
RTX 4070 12GB	12 GB	Q4_K_M	9.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	6.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	3.8	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How LLaVA 1.6 13B (13B params) fits at each quantization level on RX 9060 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A72
Q3_K_S	3	6.4 GB	Low	A73
NVFP4	4	7.3 GB	Medium	A74
Q4_K_M	4	7.9 GB	Medium	A75
Q5_K_M	5	9.4 GB	High	A75
Q6_KBest for your GPU	6	10.7 GB	High	A74
Q8_0	8	13.9 GB	Very High	F0
F16	16	26.7 GB	Maximum	F0

Upgrade-Optionen

Hardware, die LLaVA 1.6 13B gut ausführt

RX 7900 XT 20GBBestes Preis-Leistungs-Verhältnis

20 GB VRAM (+4)800 GB/s (+480)

Makes the model fit on the accelerator instead of staying completely out of reach.33.7 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 266%.

ca. $899 MSRP

RX 7900 XTX 24GBGünstige Wahl

24 GB VRAM (+8)960 GB/s (+640)

Makes the model fit on the accelerator instead of staying completely out of reach.87.2 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

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

ca. $999 MSRP

Radeon AI PRO R9700 32GBAMD-Upgrade

32 GB VRAM (+16)640 GB/s (+320)

Makes the model fit on the accelerator instead of staying completely out of reach.47.6 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

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

ca. $1,899 MSRP

Frequently asked questions

Can RX 9060 XT 16GB run LLaVA 1.6 13B?

No, LLaVA 1.6 13B requires more memory than RX 9060 XT 16GB provides.

How much VRAM does LLaVA 1.6 13B need?

LLaVA 1.6 13B (13B parameters) requires approximately 22.6 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 9060 XT 16GB?

On RX 9060 XT 16GB, LLaVA 1.6 13B achieves approximately 9.2 tokens per second decode speed with a time-to-first-token of 21079ms using Q4_K_M quantization.

Can RX 9060 XT 16GB run LLaVA 1.6 13B for coding?

For coding workloads, LLaVA 1.6 13B on RX 9060 XT 16GB receives a F grade with 9.2 tok/s and 4K context.

What context window can LLaVA 1.6 13B use on RX 9060 XT 16GB?

On RX 9060 XT 16GB, 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 9060 XT 16GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RX 9060 XT 16GB See all hardware for LLaVA 1.6 13B

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