Can Nous Dolphin 13B run on RTX 4000 Ada 20GB?

Q: What should I upgrade first if Nous Dolphin 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.

YES — With Q4_K_M

B59Good

Estimated from fit model

Nous Dolphin 13B needs ~23.3 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~19 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: 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.

Nous Dolphin 13B at Q5_K_M needs 24.8 GB — too much for RTX 4000 Ada 20GB (20.0 GB). Runs at Q4_K_M (23.3 GB) with medium quality. 4 quantization levels fit.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 24.8 GB, exceeds 20.0 GB available

24.8 GB required20.0 GB available

124% VRAM needed

4.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

14.6 tok/s

TTFT

13230 ms

Safe context

10K

Memory

24.8 GB / 20.0 GB

Offload

20%

Memory breakdown

Weights9.4 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsNous Dolphin 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: 14.6 tok/s decode · 13.2s TTFT (warm) · 37 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 1.1 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	30.6 tok/s	3451 ms	10K
Coding	F	Too heavy	14.6 tok/s	13230 ms	10K
Agentic Coding	F	Too heavy	6.3 tok/s	44731 ms	10K
Reasoning	F	Too heavy	14.6 tok/s	15635 ms	10K
RAG	F	Too heavy	6.3 tok/s	55913 ms	10K

Quantization options

How Nous Dolphin 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	B68
Q3_K_S	3	6.4 GB	Low	B68
NVFP4	4

Get started

Copy-paste commands to run Nous Dolphin 13B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "nousresearch/Nous-Dolphin-13B" \
  --hf-file "Nous-Dolphin-13B-Q5_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs Nous Dolphin 13B well

RTX 3090 24GBBudget pick

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

Makes the model fit on the accelerator instead of staying completely out of reach.48.5 tok/s decode

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.

~$1,499 MSRP

RTX 4090 24GBBest value

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

Makes the model fit on the accelerator instead of staying completely out of reach.56.7 tok/s decode

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.

~$1,599 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

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

Makes the model fit on the accelerator instead of staying completely out of reach.42.7 tok/s decode

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.

~$1,599 MSRP

Frequently asked questions

Can RTX 4000 Ada 20GB run Nous Dolphin 13B?

Yes, RTX 4000 Ada 20GB can run Nous Dolphin 13B at Q4_K_M quantization (Very compromised (needs ~1.1 GB host RAM)). The recommended Q5_K_M requires 24.8 GB which exceeds available memory, but at Q4_K_M it needs only 23.3 GB. Expected decode speed: 19.2 tok/s.

How much VRAM does Nous Dolphin 13B need?

Nous Dolphin 13B (13B parameters) requires approximately 24.8 GB at Q5_K_M quantization. On RTX 4000 Ada 20GB, it fits at Q4_K_M using 23.3 GB.

What is the best quantization for Nous Dolphin 13B?

The recommended quantization is Q5_K_M, but on RTX 4000 Ada 20GB the best fitting quantization is Q4_K_M, which uses 23.3 GB.

What speed will Nous Dolphin 13B run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Nous Dolphin 13B achieves approximately 19.2 tokens per second decode speed with a time-to-first-token of 10088ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Nous Dolphin 13B for coding?

For coding workloads, Nous Dolphin 13B on RTX 4000 Ada 20GB receives a F grade with 14.6 tok/s and 10K context.

What context window can Nous Dolphin 13B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Nous Dolphin 13B can safely use up to 12K tokens of context at Q4_K_M quantization. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada 20GB See all hardware for Nous Dolphin 13B

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