Would CUDA be a better path than Intel Arc Pro B60 24GB for stablelm 2 zephyr 1.6b?

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.

Can stablelm 2 zephyr 1.6b run on Intel Arc Pro B60 24GB?

Q: What should I upgrade first if stablelm 2 zephyr 1.6b 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.

YES — Runs Great

C41Usable

Estimated from fit model

stablelm 2 zephyr 1.6b needs ~4.5 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~22 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) — 4.5 GB, 22.4 tok/s, Runs well

4.5 GB required24.0 GB available

19% VRAM used

Fit status

Runs well

Decode

22.4 tok/s

TTFT

8643 ms

Safe context

1.7M

Memory

4.5 GB / 24.0 GB

Memory breakdown

Weights1.0 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsstablelm 2 zephyr 1.6b 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: 22.4 tok/s decode · 8.6s TTFT (warm) · 56 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	C	Runs well	22.4 tok/s	4714 ms	1.6M
Coding	C	Runs well	22.4 tok/s	8643 ms	1.7M
Agentic Coding	C	Runs well	22.4 tok/s	12571 ms	1.7M
Reasoning	C	Runs well	22.4 tok/s	10214 ms	1.7M
RAG	C	Runs well	22.4 tok/s	15714 ms	1.7M

Quantization options

How stablelm 2 zephyr 1.6b (1.600000023841858B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C43
Q3_K_S	3	0.8 GB	Low	C43
NVFP4	4

Get started

Copy-paste commands to run stablelm 2 zephyr 1.6b on your machine.

Run

lms load hf-second-state--stablelm-2-zephyr-1-6b-gguf && lms server start

Upgrade options

Hardware that runs stablelm 2 zephyr 1.6b well

Mac mini M4 64GBBudget pick

64 GB Unified (+40)

Adds memory headroom for longer context windows and future model growth.22.4 tok/s decode

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

~$1,099 MSRP

MacBook Pro M4 Pro 64GBBest value

64 GB Unified (+40)

Adds memory headroom for longer context windows and future model growth.22.4 tok/s decode

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

~$1,599 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run stablelm 2 zephyr 1.6b?

Yes, Intel Arc Pro B60 24GB can run stablelm 2 zephyr 1.6b with a C grade (Runs well). Expected decode speed: 22.4 tok/s.

How much VRAM does stablelm 2 zephyr 1.6b need?

stablelm 2 zephyr 1.6b (1.600000023841858B parameters) requires approximately 4.5 GB of memory with Q4_K_M quantization.

What is the best quantization for stablelm 2 zephyr 1.6b?

The recommended quantization for stablelm 2 zephyr 1.6b is Q4_K_M, which balances quality and memory efficiency.

What speed will stablelm 2 zephyr 1.6b run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, stablelm 2 zephyr 1.6b achieves approximately 22.4 tokens per second decode speed with a time-to-first-token of 8643ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run stablelm 2 zephyr 1.6b for coding?

For coding workloads, stablelm 2 zephyr 1.6b on Intel Arc Pro B60 24GB receives a C grade with 22.4 tok/s and 1.7M context.

What context window can stablelm 2 zephyr 1.6b use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, stablelm 2 zephyr 1.6b can safely use up to 1.7M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Intel Arc Pro B60 24GB See all hardware for stablelm 2 zephyr 1.6b

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