Can Nous Dolphin 13B run on Intel Arc Pro B60 24GB?

YES — With Offload

B69Good

Estimated from fit model

Nous Dolphin 13B needs ~25.2 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q5_K_M quantization, expect ~19 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, 18.6 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

18.6 tok/s

TTFT

10394 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 Intel Arc Pro B60 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: 18.6 tok/s decode · 10.4s TTFT (warm) · 47 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade 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	26.8 tok/s	3935 ms	14K
Coding	B	Runs with offload (needs ~0.4 GB host RAM)	18.6 tok/s	10394 ms	14K
Agentic Coding	F	Too heavy	8.3 tok/s	33874 ms	14K
Reasoning	B	Runs with offload (needs ~0.4 GB host RAM)	18.6 tok/s	12284 ms	14K
RAG	F	Too heavy	8.3 tok/s	42343 ms	14K

Quantization options

How Nous Dolphin 13B (13B params) fits at each quantization level on Intel Arc Pro B60 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-Optionen

Hardware, die Nous Dolphin 13B gut ausführt

MacBook Pro M4 Pro 64GBGünstige Wahl

64 GB Unified (+40)

Adds memory headroom for longer context windows and future model growth.20.2 tok/s Dekodierung

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

ca. $1,599 MSRP

Radeon AI PRO R9700 32GBBestes Preis-Leistungs-Verhältnis

32 GB VRAM (+8)640 GB/s (+184)

Raises estimated decode speed by about 121%.41.1 tok/s Dekodierung

Raises estimated decode speed by about 121%.

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

ca. $1,899 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run Nous Dolphin 13B?

Yes, Intel Arc Pro B60 24GB can run Nous Dolphin 13B with a B grade (Runs with offload (needs ~0.4 GB host RAM)). Expected decode speed: 18.6 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 Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Nous Dolphin 13B achieves approximately 18.6 tokens per second decode speed with a time-to-first-token of 10394ms using Q5_K_M quantization.

Can Intel Arc Pro B60 24GB run Nous Dolphin 13B for coding?

For coding workloads, Nous Dolphin 13B on Intel Arc Pro B60 24GB receives a B grade with 18.6 tok/s and 14K context.

What context window can Nous Dolphin 13B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 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 Intel Arc Pro B60 24GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Would CUDA be a better path than Intel Arc Pro B60 24GB for Nous Dolphin 13B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Arc Pro B60 24GB See all hardware for Nous Dolphin 13B

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