Can Nous Dolphin 13B run on NVIDIA L4 24GB?

YES — With Offload

B69Good

Estimated from fit model

Nous Dolphin 13B needs ~25.2 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q5_K_M quantization, expect ~14 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: 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, 14.4 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

14.4 tok/s

TTFT

13424 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 NVIDIA L4 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: 14.4 tok/s decode · 13.4s TTFT (warm) · 36 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	21.3 tok/s	4969 ms	14K
Coding	B	Runs with offload (needs ~0.4 GB host RAM)	14.4 tok/s	13424 ms	14K
Agentic Coding	F	Too heavy	6.3 tok/s	44888 ms	14K
Reasoning	B	Runs with offload (needs ~0.4 GB host RAM)	14.4 tok/s	15865 ms	14K
RAG	F	Too heavy	6.3 tok/s	56110 ms	14K

Quantization options

How Nous Dolphin 13B (13B params) fits at each quantization level on NVIDIA L4 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

Upgrade options

Hardware that runs Nous Dolphin 13B well

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1492)

Raises estimated decode speed by about 808%.130.8 tok/s decode

Raises estimated decode speed by about 808%.

Adds memory headroom for longer context windows and future model growth.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+596)

Raises estimated decode speed by about 469%.82 tok/s decode

Raises estimated decode speed by about 469%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)576 GB/s (+276)

Raises estimated decode speed by about 249%.50.2 tok/s decode

Raises estimated decode speed by about 249%.

Adds memory headroom for longer context windows and future model growth.

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Nous Dolphin 13B?

Yes, NVIDIA L4 24GB can run Nous Dolphin 13B with a B grade (Runs with offload (needs ~0.4 GB host RAM)). Expected decode speed: 14.4 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, Nous Dolphin 13B achieves approximately 14.4 tokens per second decode speed with a time-to-first-token of 13424ms using Q5_K_M quantization.

Can NVIDIA L4 24GB run Nous Dolphin 13B for coding?

For coding workloads, Nous Dolphin 13B on NVIDIA L4 24GB receives a B grade with 14.4 tok/s and 14K context.

What context window can Nous Dolphin 13B use on NVIDIA L4 24GB?

On NVIDIA L4 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 NVIDIA L4 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 NVIDIA L4 24GB See all hardware for Nous Dolphin 13B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

21.3 tok/s

4969 ms

14K

Coding

Runs with offload (needs ~0.4 GB host RAM)

14.4 tok/s

13424 ms

14K

Agentic Coding

Too heavy

6.3 tok/s

44888 ms

14K

Reasoning

Runs with offload (needs ~0.4 GB host RAM)

14.4 tok/s

15865 ms

14K

RAG

Too heavy

6.3 tok/s

56110 ms

14K

Quantization options

How Nous Dolphin 13B (13B params) fits at each quantization level on NVIDIA L4 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

Frequently asked questions

Can NVIDIA L4 24GB run Nous Dolphin 13B?

Yes, NVIDIA L4 24GB can run Nous Dolphin 13B with a B grade (Runs with offload (needs ~0.4 GB host RAM)). Expected decode speed: 14.4 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, Nous Dolphin 13B achieves approximately 14.4 tokens per second decode speed with a time-to-first-token of 13424ms using Q5_K_M quantization.

Can NVIDIA L4 24GB run Nous Dolphin 13B for coding?

For coding workloads, Nous Dolphin 13B on NVIDIA L4 24GB receives a B grade with 14.4 tok/s and 14K context.

What context window can Nous Dolphin 13B use on NVIDIA L4 24GB?

On NVIDIA L4 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 NVIDIA L4 24GB?

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