Will It Run AI

Can HelpingAI2.5 10B i1 run on MacBook Pro M4 Max 128GB?

YES — Runs Great

C45Usable
Estimated from fit model

HelpingAI2.5 10B i1 needs ~22.0 GB VRAM. MacBook Pro M4 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~62 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
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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, 61.5 tok/s, Runs well
22.0 GB required92.2 GB available
24% VRAM used

Fit status

Runs well

Decode

61.5 tok/s

TTFT

3150 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 MacBook Pro M4 Max 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: 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well61.5 tok/s1718 ms974K
CodingCRuns well61.5 tok/s3150 ms974K
Agentic CodingCRuns well61.5 tok/s4581 ms974K
ReasoningCRuns well61.5 tok/s3722 ms974K
RAGCRuns well61.5 tok/s5727 ms974K

Quantization options

How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on MacBook Pro M4 Max 128GB (92.2 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.9 GB
LowD39
Q3_K_S
3
4.9 GB
LowD39
NVFP4
4
5.6 GB
MediumD39
Q4_K_M
4
6.1 GB
MediumD39
Q5_K_M
5
7.2 GB
HighD39
Q6_K
6
8.2 GB
HighD39
Q8_0
8
10.7 GB
Very HighD39
F16Best for your GPU
16
20.5 GB
MaximumC40

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

Opciones de mejora

Hardware que ejecuta bien HelpingAI2.5 10B i1

Mac Studio M3 Ultra 256GBOpción económica
256 GB Unified (+128)819 GB/s (+273)
C
Sube la velocidad estimada de decodificación alrededor de un 48%.91.3 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 48%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$6,999 MSRP

NVIDIARTX PRO 6000 Blackwell Workstation Edition 96GBMejor relación calidad-precio
1792 GB/s (+1246)
C
Sube la velocidad estimada de decodificación alrededor de un 128%.140 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 128%.

~$9,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 128GB run HelpingAI2.5 10B i1?

Yes, MacBook Pro M4 Max 128GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 61.5 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 MacBook Pro M4 Max 128GB?

On MacBook Pro M4 Max 128GB, HelpingAI2.5 10B i1 achieves approximately 61.5 tokens per second decode speed with a time-to-first-token of 3150ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 128GB run HelpingAI2.5 10B i1 for coding?

For coding workloads, HelpingAI2.5 10B i1 on MacBook Pro M4 Max 128GB receives a C grade with 61.5 tok/s and 974K context.

What context window can HelpingAI2.5 10B i1 use on MacBook Pro M4 Max 128GB?

On MacBook Pro M4 Max 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.

Is unified memory on MacBook Pro M4 Max 128GB as fast as VRAM for HelpingAI2.5 10B i1?

Not always. MacBook Pro M4 Max 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.

See all results for MacBook Pro M4 Max 128GBSee all hardware for HelpingAI2.5 10B i1
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