Can Nous Dolphin 13B run on Quadro RTX 6000 24GB?

YES — With Offload

A71Great

Estimated from fit model

Nous Dolphin 13B needs ~25.2 GB VRAM. Quadro RTX 6000 24GB has 24.0 GB. With Q5_K_M quantization, expect ~33 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: 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

Q5_K_M (High quality) — 25.2 GB, 33.2 tok/s, Runs with offload (needs ~0.4 GB host RAM)

25.2 GB required24.0 GB available

105% VRAM needed

1.2 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.4 GB host RAM)

Decode

33.2 tok/s

TTFT

5830 ms

Safe context

14K

Memory

25.2 GB / 24.0 GB

Memory breakdown

Weights9.4 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsNous Dolphin 13B on Quadro RTX 6000 24GB
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: 33.2 tok/s decode · 5.8s TTFT (warm) · 83 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	50.5 tok/s	2090 ms	14K
Coding	A	Runs with offload (needs ~0.4 GB host RAM)	33.2 tok/s	5830 ms	14K
Agentic Coding	F	Too heavy	13.9 tok/s	20209 ms	14K
Reasoning	A	Runs with offload (needs ~0.4 GB host RAM)	33.2 tok/s	6890 ms	14K
RAG	F	Too heavy	13.9 tok/s	25261 ms	14K

Quantization options

How Nous Dolphin 13B (13B params) fits at each quantization level on Quadro RTX 6000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B66
Q3_K_S	3	6.4 GB	Low	B67
NVFP4	4	7.3 GB	Medium	B67
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	B70
Q8_0Best for your GPU	8	13.9 GB	Very High	A71
F16	16	26.7 GB	Maximum	F0

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

Your hardware

More models your Quadro RTX 6000 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	70.1 tok/s
Qwen 3.5 27B	27B	S	30.4 tok/s
Qwen 3.6 27B	27B	S	30.5 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	72.5 tok/s
Qwen 3.5 35B A3B	35B	A	37.9 tok/s

Frequently asked questions

Can Quadro RTX 6000 24GB run Nous Dolphin 13B?

Yes, Quadro RTX 6000 24GB can run Nous Dolphin 13B with a A grade (Runs with offload (needs ~0.4 GB host RAM)). Expected decode speed: 33.2 tok/s.

How much VRAM does Nous Dolphin 13B need?

Nous Dolphin 13B (13B parameters) requires approximately 25.2 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 Quadro RTX 6000 24GB?

On Quadro RTX 6000 24GB, Nous Dolphin 13B achieves approximately 33.2 tokens per second decode speed with a time-to-first-token of 5830ms using Q5_K_M quantization.

Can Quadro RTX 6000 24GB run Nous Dolphin 13B for coding?

For coding workloads, Nous Dolphin 13B on Quadro RTX 6000 24GB receives a A grade with 33.2 tok/s and 14K context.

What context window can Nous Dolphin 13B use on Quadro RTX 6000 24GB?

On Quadro RTX 6000 24GB, Nous Dolphin 13B can safely use up to 14K 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 Quadro RTX 6000 24GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for Quadro RTX 6000 24GB See all hardware for Nous Dolphin 13B

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