Can HelpingAI2.5 10B i1 run on MacBook Pro M4 Pro 24GB?

YES — Runs Great

C51Usable

Estimated — low-sample bucket· few comparable runs

HelpingAI2.5 10B i1 needs ~10.8 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~32 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 10.8 GB, 31.7 tok/s, Runs well

10.8 GB required17.3 GB available

62% VRAM used

Fit status

Runs well

Decode

31.7 tok/s

TTFT

6107 ms

Safe context

105K

Memory

10.8 GB / 17.3 GB

Memory breakdown

Weights6.1 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsHelpingAI2.5 10B i1 on MacBook Pro M4 Pro 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: 31.7 tok/s decode · 6.1s TTFT (warm) · 79 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	31.7 tok/s	3331 ms	105K
Coding	C	Runs well	31.7 tok/s	6107 ms	105K
Agentic Coding	C	Runs well	31.7 tok/s	8882 ms	105K
Reasoning	C	Runs well	31.7 tok/s	7217 ms	105K
RAG	C	Runs well	31.7 tok/s	11103 ms	105K

Quantization options

How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.9 GB	Low	C47
Q3_K_S	3	4.9 GB	Low	C47
NVFP4	4	5.6 GB	Medium	C48
Q4_K_M	4	6.1 GB	Medium	C49
Q5_K_M	5	7.2 GB	High	C50
Q6_K	6	8.2 GB	High	C50
Q8_0Best for your GPU	8	10.7 GB	Very High	C50
F16	16	20.5 GB	Maximum	F0

Get started

Copy-paste commands to run HelpingAI2.5 10B i1 on your machine.

Run

lms load hf-mradermacher--helpingai2-5-10b-i1-gguf && lms server start

升级选项

能流畅运行 HelpingAI2.5 10B i1 的硬件

RX 7900 XT 20GB经济之选

800 GB/s (+527)

Raises estimated decode speed by about 148%.78.7 tok/s 解码

Raises estimated decode speed by about 148%.

~$899 MSRP

RTX A4500 20GB最佳性价比

640 GB/s (+367)

Raises estimated decode speed by about 158%.81.8 tok/s 解码

Raises estimated decode speed by about 158%.

~$2,000 MSRP

Frequently asked questions

Can MacBook Pro M4 Pro 24GB run HelpingAI2.5 10B i1?

Yes, MacBook Pro M4 Pro 24GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 31.7 tok/s.

How much VRAM does HelpingAI2.5 10B i1 need?

HelpingAI2.5 10B i1 (10B parameters) requires approximately 10.8 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI2.5 10B i1?

The recommended quantization for HelpingAI2.5 10B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI2.5 10B i1 run at on MacBook Pro M4 Pro 24GB?

On MacBook Pro M4 Pro 24GB, HelpingAI2.5 10B i1 achieves approximately 31.7 tokens per second decode speed with a time-to-first-token of 6107ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 24GB run HelpingAI2.5 10B i1 for coding?

For coding workloads, HelpingAI2.5 10B i1 on MacBook Pro M4 Pro 24GB receives a C grade with 31.7 tok/s and 105K context.

What context window can HelpingAI2.5 10B i1 use on MacBook Pro M4 Pro 24GB?

On MacBook Pro M4 Pro 24GB, HelpingAI2.5 10B i1 can safely use up to 105K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Pro 24GB as fast as VRAM for HelpingAI2.5 10B i1?

Not always. MacBook Pro M4 Pro 24GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for MacBook Pro M4 Pro 24GB See all hardware for HelpingAI2.5 10B i1

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