Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for HelpingAI2.5 10B i1?

Not always. Mac Studio M2 Ultra 128GB 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 Mac Studio M2 Ultra 128GB?

YES — Runs Great

C46Usable

Estimated from fit model

HelpingAI2.5 10B i1 needs ~22.0 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~76 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 22.0 GB, 76.1 tok/s, Runs well

22.0 GB required92.2 GB available

24% VRAM used

Fit status

Runs well

Decode

76.1 tok/s

TTFT

2545 ms

Safe context

974K

Memory

22.0 GB / 92.2 GB

Memory breakdown

Weights6.1 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsHelpingAI2.5 10B i1 on Mac Studio M2 Ultra 128GB
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: 76.1 tok/s decode · 2.5s TTFT (warm) · 190 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	76.1 tok/s	1388 ms	974K
Coding	C	Runs well	76.1 tok/s	2545 ms	974K
Agentic Coding	C	Runs well	76.1 tok/s	3702 ms	974K
Reasoning	C	Runs well	76.1 tok/s	3008 ms	974K
RAG	C	Runs well	76.1 tok/s	4628 ms	974K

Quantization options

How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.9 GB	Low	D39
Q3_K_S	3	4.9 GB	Low	D39
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

Frequently asked questions

Can Mac Studio M2 Ultra 128GB run HelpingAI2.5 10B i1?

Yes, Mac Studio M2 Ultra 128GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 76.1 tok/s.

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

HelpingAI2.5 10B i1 (10B parameters) requires approximately 22.0 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 Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, HelpingAI2.5 10B i1 achieves approximately 76.1 tokens per second decode speed with a time-to-first-token of 2545ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 128GB run HelpingAI2.5 10B i1 for coding?

For coding workloads, HelpingAI2.5 10B i1 on Mac Studio M2 Ultra 128GB receives a C grade with 76.1 tok/s and 974K context.

What context window can HelpingAI2.5 10B i1 use on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, HelpingAI2.5 10B i1 can safely use up to 974K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Mac Studio M2 Ultra 128GB See all hardware for HelpingAI2.5 10B i1

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