Is unified memory on MacBook Pro M4 Max 96GB as fast as VRAM for HelpingAI2.5 10B i1?

Not always. MacBook Pro M4 Max 96GB 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.

Can HelpingAI2.5 10B i1 run on MacBook Pro M4 Max 96GB?

YES — Runs Great

C46Usable

Estimated from fit model

HelpingAI2.5 10B i1 needs ~18.5 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~56 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) — 18.5 GB, 61.5 tok/s, Runs well

18.5 GB required69.1 GB available

27% VRAM used

Fit status

Runs well

Decode

61.5 tok/s

TTFT

3150 ms

Safe context

707K

Memory

18.5 GB / 69.1 GB

Memory breakdown

Weights6.1 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsHelpingAI2.5 10B i1 on MacBook Pro M4 Max 96GB
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: 61.5 tok/s decode · 3.1s TTFT (warm) · 154 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	61.5 tok/s	1718 ms	707K
Coding	C	Runs well	56.4 tok/s	3433 ms	707K
Agentic Coding	C	Runs well	61.5 tok/s	4581 ms	707K
Reasoning	C	Runs well	61.5 tok/s	3722 ms	707K
RAG	C	Runs well	61.5 tok/s	5727 ms	707K

Quantization options

How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.9 GB	Low	D40
Q3_K_S	3	4.9 GB	Low	D40
NVFP4	4

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

Upgrade options

Hardware that runs HelpingAI2.5 10B i1 well

Mac Studio M2 Ultra 128GBBudget pick

128 GB Unified (+32)800 GB/s (+254)

Adds memory headroom for longer context windows and future model growth.76.1 tok/s decode

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

~$3,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 96GB run HelpingAI2.5 10B i1?

Yes, MacBook Pro M4 Max 96GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 56.4 tok/s.

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

HelpingAI2.5 10B i1 (10B parameters) requires approximately 18.5 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 Max 96GB?

On MacBook Pro M4 Max 96GB, HelpingAI2.5 10B i1 achieves approximately 56.4 tokens per second decode speed with a time-to-first-token of 3433ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 96GB run HelpingAI2.5 10B i1 for coding?

For coding workloads, HelpingAI2.5 10B i1 on MacBook Pro M4 Max 96GB receives a C grade with 56.4 tok/s and 707K context.

What context window can HelpingAI2.5 10B i1 use on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, HelpingAI2.5 10B i1 can safely use up to 707K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M4 Max 96GB See all hardware for HelpingAI2.5 10B i1

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