Can Nous Dolphin 13B run on RX 6900 XT 16GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Nous Dolphin 13B needs ~24.4 GB but RX 6900 XT 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: 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

Q5_K_M (High quality) — 24.4 GB, exceeds 16.0 GB available

24.4 GB required16.0 GB available

153% VRAM needed

8.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.8 tok/s

TTFT

19675 ms

Safe context

Memory

24.4 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights9.4 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsNous Dolphin 13B on RX 6900 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: 9.8 tok/s decode · 19.7s TTFT (warm) · 25 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 24.4 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	B	Very compromised (needs ~1.2 GB host RAM)	18.1 tok/s	5849 ms	5K
Coding	F	Too heavy	9.8 tok/s	19675 ms	5K
Agentic Coding	F	Too heavy	4.8 tok/s	59030 ms	5K
Reasoning	F	Too heavy	9.8 tok/s	23253 ms	5K
RAG	F	Too heavy	4.8 tok/s	73787 ms	5K

Quantization options

How Nous Dolphin 13B (13B params) fits at each quantization level on RX 6900 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B70
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	A72
Q6_KBest for your GPU	6	10.7 GB	High	A72
Q8_0	8	13.9 GB	Very High	F0
F16	16	26.7 GB	Maximum	F0

Upgrade-Optionen

Hardware, die Nous Dolphin 13B gut ausführt

RX 7900 XTX 24GBGünstige Wahl

24 GB VRAM (+8)960 GB/s (+448)

Makes the model fit on the accelerator instead of staying completely out of reach.51.1 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 32GBBestes Preis-Leistungs-Verhältnis

32 GB VRAM (+16)640 GB/s (+128)

Makes the model fit on the accelerator instead of staying completely out of reach.41.1 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

Radeon Pro W6800 32GBAMD-Upgrade

32 GB VRAM (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.31.2 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. $2,249 MSRP

Frequently asked questions

Can RX 6900 XT 16GB run Nous Dolphin 13B?

No, Nous Dolphin 13B requires more memory than RX 6900 XT 16GB provides.

How much VRAM does Nous Dolphin 13B need?

Nous Dolphin 13B (13B parameters) requires approximately 24.4 GB of memory with Q5_K_M quantization.

What is the best quantization for Nous Dolphin 13B?

The recommended quantization for Nous Dolphin 13B is Q5_K_M, which balances quality and memory efficiency.

What speed will Nous Dolphin 13B run at on RX 6900 XT 16GB?

On RX 6900 XT 16GB, Nous Dolphin 13B achieves approximately 9.8 tokens per second decode speed with a time-to-first-token of 19675ms using Q5_K_M quantization.

Can RX 6900 XT 16GB run Nous Dolphin 13B for coding?

For coding workloads, Nous Dolphin 13B on RX 6900 XT 16GB receives a F grade with 9.8 tok/s and 5K context.

What context window can Nous Dolphin 13B use on RX 6900 XT 16GB?

On RX 6900 XT 16GB, Nous Dolphin 13B can safely use up to 5K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if Nous Dolphin 13B feels slow on RX 6900 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 6900 XT 16GB See all hardware for Nous Dolphin 13B

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