Can Helply 10.2b chat i1 run on Intel Arc Pro B60 24GB?

YES — Runs Great

C48Usable

Estimated from fit model

Helply 10.2b chat i1 needs ~10.7 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~40 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) — 10.7 GB, 39.6 tok/s, Runs well

10.7 GB required24.0 GB available

45% VRAM used

Fit status

Runs well

Decode

39.6 tok/s

TTFT

4892 ms

Safe context

194K

Memory

10.7 GB / 24.0 GB

Memory breakdown

Weights6.2 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsHelply 10.2b chat i1 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: 39.6 tok/s decode · 4.9s TTFT (warm) · 99 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	39.6 tok/s	2668 ms	194K
Coding	C	Runs well	39.6 tok/s	4892 ms	194K
Agentic Coding	C	Runs well	39.6 tok/s	7115 ms	194K
Reasoning	C	Runs well	39.6 tok/s	5781 ms	194K
RAG	C	Runs well	39.6 tok/s	8894 ms	194K

Quantization options

How Helply 10.2b chat i1 (10.199999809265137B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.0 GB	Low	C44
Q3_K_S	3	5.0 GB	Low	C45
NVFP4	4	5.7 GB	Medium	C45
Q4_K_M	4	6.2 GB	Medium	C46
Q5_K_M	5	7.3 GB	High	C46
Q6_K	6	8.4 GB	High	C47
Q8_0Best for your GPU	8	10.9 GB	Very High	C49
F16	16	20.9 GB	Maximum	F0

Get started

Copy-paste commands to run Helply 10.2b chat i1 on your machine.

Run

lms load hf-mradermacher--helply-10-2b-chat-i1-gguf && lms server start

アップグレードオプション

Helply 10.2b chat i1を快適に動かすハードウェア

RTX 5090 32GBコスパ重視

32 GB VRAM (+8)1792 GB/s (+1336)

Raises estimated decode speed by about 373%.187.2 tok/sデコード

Raises estimated decode speed by about 373%.

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

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

〜$1,999 MSRP

RTX PRO 4500 Blackwell 32GBコスパ最良

32 GB VRAM (+8)896 GB/s (+440)

Raises estimated decode speed by about 206%.121 tok/sデコード

Raises estimated decode speed by about 206%.

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

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

〜$2,499 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run Helply 10.2b chat i1?

Yes, Intel Arc Pro B60 24GB can run Helply 10.2b chat i1 with a C grade (Runs well). Expected decode speed: 39.6 tok/s.

How much VRAM does Helply 10.2b chat i1 need?

Helply 10.2b chat i1 (10.199999809265137B parameters) requires approximately 10.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Helply 10.2b chat i1?

The recommended quantization for Helply 10.2b chat i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Helply 10.2b chat i1 run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Helply 10.2b chat i1 achieves approximately 39.6 tokens per second decode speed with a time-to-first-token of 4892ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Helply 10.2b chat i1 for coding?

For coding workloads, Helply 10.2b chat i1 on Intel Arc Pro B60 24GB receives a C grade with 39.6 tok/s and 194K context.

What context window can Helply 10.2b chat i1 use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Helply 10.2b chat i1 can safely use up to 194K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Helply 10.2b chat i1 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 Helply 10.2b chat i1?

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 Helply 10.2b chat i1

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