Can Nous Dolphin 13B run on RTX 3090 24GB?

YES — With Offload

A72Great

Estimated from fit model

Nous Dolphin 13B needs ~25.2 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q5_K_M quantization, expect ~49 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: 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, 48.5 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

48.5 tok/s

TTFT

3995 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 RTX 3090 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: 48.5 tok/s decode · 4.0s TTFT (warm) · 121 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.

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	71.4 tok/s	1479 ms	14K
Coding	A	Runs with offload (needs ~0.4 GB host RAM)	48.5 tok/s	3995 ms	14K
Agentic Coding	F	Too heavy	21.1 tok/s	13359 ms	14K
Reasoning	A	Runs with offload (needs ~0.4 GB host RAM)	48.5 tok/s	4722 ms	14K
RAG	F	Too heavy	21.1 tok/s	16699 ms	14K

Inference speed

Nous Dolphin 13B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Nous Dolphin 13B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 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	Q5_K_M	130.8	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q5_K_M	60.7	Fits
RTX 4090 24GB	24 GB	Q5_K_M	56.7	Offloads
RX 7900 XTX 24GB	24 GB	Q5_K_M	51.1	Offloads
Mac Studio M2 Ultra 128GB	128 GB	Q5_K_M	50.6	Fits
RTX 3090 24GB	24 GB	Q5_K_M	48.5	Offloads
Mac Studio M1 Ultra 128GB	128 GB	Q5_K_M	47.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q5_K_M	33.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q5_K_M	33.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q5_K_M	26.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q5_K_M	24.0	Fits
RTX 4080 Super 16GB	16 GB	Q5_K_M	20.6	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q5_K_M	20.2	Fits
RTX 4070 12GB	12 GB	Q5_K_M	7.2	Too big
RTX 3060 12GB	12 GB	Q5_K_M	4.5	Too big
RTX 4060 8GB	8 GB	Q5_K_M	3.2	Too big

Estimates for single-stream decoding at Q5_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 Nous Dolphin 13B (13B params) fits at each quantization level on RTX 3090 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 RTX 3090 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	99.1 tok/s
Qwen 3.5 27B	27B	S	43 tok/s
Qwen 3.6 27B	27B	S	43.1 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	102.5 tok/s
Qwen 3.5 35B A3B	35B	A	55.5 tok/s

Frequently asked questions

Can RTX 3090 24GB run Nous Dolphin 13B?

Yes, RTX 3090 24GB can run Nous Dolphin 13B with a A grade (Runs with offload (needs ~0.4 GB host RAM)). Expected decode speed: 48.5 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 RTX 3090 24GB?

On RTX 3090 24GB, Nous Dolphin 13B achieves approximately 48.5 tokens per second decode speed with a time-to-first-token of 3995ms using Q5_K_M quantization.

Can RTX 3090 24GB run Nous Dolphin 13B for coding?

For coding workloads, Nous Dolphin 13B on RTX 3090 24GB receives a A grade with 48.5 tok/s and 14K context.

What context window can Nous Dolphin 13B use on RTX 3090 24GB?

On RTX 3090 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 RTX 3090 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 RTX 3090 24GB See all hardware for Nous Dolphin 13B

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