Can mxbai Embed Large run on Intel Arc Pro B60 24GB?

YES — Runs Great

A72Great

Estimated from fit model

mxbai Embed Large needs ~5.8 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With F16 quantization, expect ~5 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Memory bandwidth

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

F16 (Maximum quality) — 5.8 GB, 4.7 tok/s, Runs well

5.8 GB required24.0 GB available

24% VRAM used

Fit status

Runs well

Decode

4.7 tok/s

TTFT

41279 ms

Safe context

512

Memory

5.8 GB / 24.0 GB

Memory breakdown

Weights0.7 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsmxbai Embed Large 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: 4.7 tok/s decode · 41.3s TTFT (warm) · 12 tok/s prefill

What limits this setup

This model fits, but memory bandwidth is the part holding decode speed back.

Throughput will feel slow

Estimated decode speed is only 4.7 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

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.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	4.7 tok/s	22516 ms	512
Coding	A	Runs well	4.7 tok/s	41279 ms	512
Agentic Coding	A	Runs well	4.7 tok/s	60043 ms	512
Reasoning	A	Runs well	4.7 tok/s	48785 ms	512
RAG	A	Runs well	4.7 tok/s	75053 ms	512

Quantization options

How mxbai Embed Large (0.33500000834465027B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.1 GB	Low	A78
Q3_K_S	3	0.2 GB	Low	A78
NVFP4	4	0.2 GB	Medium	A78
Q4_K_M	4	0.2 GB	Medium	A78
Q5_K_M	5	0.2 GB	High	A78
Q6_K	6	0.3 GB	High	A78
Q8_0	8	0.4 GB	Very High	A78
F16Best for your GPU	16	0.7 GB	Maximum	A78

Get started

Copy-paste commands to run mxbai Embed Large on your machine.

Run

ollama run mxbai-embed-large

Your hardware

More models your Intel Arc Pro B60 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	37.2 tok/s
Qwen 3.5 27B	27B	S	16.1 tok/s
Qwen 3.6 27B	27B	S	16.2 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	38.5 tok/s
Qwen 3.5 9B	9B	S	48.2 tok/s

Frequently asked questions

Can Intel Arc Pro B60 24GB run mxbai Embed Large?

Yes, Intel Arc Pro B60 24GB can run mxbai Embed Large with a A grade (Runs well). Expected decode speed: 4.7 tok/s.

How much VRAM does mxbai Embed Large need?

mxbai Embed Large (0.33500000834465027B parameters) requires approximately 5.8 GB of memory with F16 quantization.

What is the best quantization for mxbai Embed Large?

The recommended quantization for mxbai Embed Large is F16, which balances quality and memory efficiency.

What speed will mxbai Embed Large run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, mxbai Embed Large achieves approximately 4.7 tokens per second decode speed with a time-to-first-token of 41279ms using F16 quantization.

Can Intel Arc Pro B60 24GB run mxbai Embed Large for coding?

For coding workloads, mxbai Embed Large on Intel Arc Pro B60 24GB receives a A grade with 4.7 tok/s and 512 context.

What context window can mxbai Embed Large use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, mxbai Embed Large can safely use up to 512 tokens of context. The model's official context limit is 512, but available memory constrains the safe maximum.

What should I upgrade first if mxbai Embed Large feels slow on Intel Arc Pro B60 24GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Would CUDA be a better path than Intel Arc Pro B60 24GB for mxbai Embed Large?

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 mxbai Embed Large

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Preview:

What limits this setup

This model fits, but memory bandwidth is the part holding decode speed back.

Throughput will feel slow

Estimated decode speed is only 4.7 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

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.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

4.7 tok/s

22516 ms

512

Coding

Runs well

4.7 tok/s

41279 ms

512

Agentic Coding

Runs well

4.7 tok/s

60043 ms

512

Reasoning

Runs well

4.7 tok/s

48785 ms

512

RAG

Runs well

4.7 tok/s

75053 ms

512

Quantization options

How mxbai Embed Large (0.33500000834465027B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.1 GB	Low	A78
Q3_K_S	3	0.2 GB	Low	A78
NVFP4	4	0.2 GB	Medium	A78
Q4_K_M	4	0.2 GB	Medium	A78
Q5_K_M	5	0.2 GB	High	A78
Q6_K	6	0.3 GB	High	A78
Q8_0	8	0.4 GB	Very High	A78
F16Best for your GPU	16	0.7 GB	Maximum	A78

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

37.2 tok/s

Qwen 3.5 27B

27B

16.1 tok/s

Qwen 3.6 27B

27B

16.2 tok/s

Qwen3-VL 30B A3B Instruct

30B

38.5 tok/s

Qwen 3.5 9B

48.2 tok/s