Can Phi 3 Mini 3.8B run on Intel Arc Pro B60 24GB?

YES — Runs Great

B67Good

Estimated from fit model

Phi 3 Mini 3.8B needs ~11.5 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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

Q4_K_M (Medium quality) — 11.5 GB, 53.2 tok/s, Runs well

11.5 GB required24.0 GB available

48% VRAM used

Fit status

Runs well

Decode

53.2 tok/s

TTFT

3639 ms

Safe context

50K

Memory

11.5 GB / 24.0 GB

Memory breakdown

Weights2.3 GB

KV Cache5.9 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsPhi 3 Mini 3.8B 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: 53.2 tok/s decode · 3.6s TTFT (warm) · 133 tok/s prefill

What limits this setup

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

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.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	53.2 tok/s	1985 ms	50K
Coding	B	Runs well	53.2 tok/s	3639 ms	50K
Agentic Coding	A	Runs well	53.2 tok/s	5293 ms	50K
Reasoning	B	Runs well	53.2 tok/s	4301 ms	50K
RAG	A	Runs well	53.2 tok/s	6617 ms	50K

Inference speed

Phi 3 Mini 3.8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Phi 3 Mini 3.8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~72 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	Q4_K_M	72.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	60.8	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	60.8	Fits
RTX 4070 12GB	12 GB	Q4_K_M	60.8	Tight
RTX 3090 24GB	24 GB	Q4_K_M	53.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	53.2	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	53.2	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	53.2	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	53.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	53.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	53.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	53.2	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	53.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	53.2	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	53.2	Fits
RTX 4060 8GB	8 GB	Q4_K_M	38.7	Too big

Estimates for single-stream decoding at Q4_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 Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.5 GB	Low	B61
Q3_K_S	3	1.9 GB	Low	B61
NVFP4	4	2.1 GB	Medium	B61
Q4_K_M	4	2.3 GB	Medium	B61
Q5_K_M	5	2.7 GB	High	B61
Q6_K	6	3.1 GB	High	B62
Q8_0	8	4.1 GB	Very High	B62
F16Best for your GPU	16	7.8 GB	Maximum	B64

Get started

Copy-paste commands to run Phi 3 Mini 3.8B on your machine.

Run

ollama run phi3:mini

Upgrade-Optionen

Hardware, die Phi 3 Mini 3.8B gut ausführt

MacBook Pro M3 Pro 36GBGünstige Wahl

36 GB Unified (+12)

This setup is broadly balanced for this model.47.2 tok/s Dekodierung

ca. $1,999 MSRP

MacBook Pro M4 Max 36GBBestes Preis-Leistungs-Verhältnis

36 GB Unified (+12)

This setup is broadly balanced for this model.53.2 tok/s Dekodierung

ca. $2,499 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run Phi 3 Mini 3.8B?

Yes, Intel Arc Pro B60 24GB can run Phi 3 Mini 3.8B with a B grade (Runs well). Expected decode speed: 53.2 tok/s.

How much VRAM does Phi 3 Mini 3.8B need?

Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 11.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi 3 Mini 3.8B?

The recommended quantization for Phi 3 Mini 3.8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 3 Mini 3.8B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Phi 3 Mini 3.8B achieves approximately 53.2 tokens per second decode speed with a time-to-first-token of 3639ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Phi 3 Mini 3.8B for coding?

For coding workloads, Phi 3 Mini 3.8B on Intel Arc Pro B60 24GB receives a B grade with 53.2 tok/s and 50K context.

What context window can Phi 3 Mini 3.8B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Phi 3 Mini 3.8B can safely use up to 50K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Phi 3 Mini 3.8B 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 Phi 3 Mini 3.8B?

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 Phi 3 Mini 3.8B

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