Can HelpingAI 15B i1 run on MacBook Pro M1 Max 64GB?

Q: Is unified memory on MacBook Pro M1 Max 64GB as fast as VRAM for HelpingAI 15B i1?

Not always. MacBook Pro M1 Max 64GB 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.

YES — Runs Great

C46Usable

Estimated from fit model

HelpingAI 15B i1 needs ~18.7 GB VRAM. MacBook Pro M1 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~24 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) — 18.7 GB, 24.0 tok/s, Runs well

18.7 GB required46.1 GB available

41% VRAM used

Fit status

Runs well

Decode

24.0 tok/s

TTFT

8052 ms

Safe context

265K

Memory

18.7 GB / 46.1 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsHelpingAI 15B i1 on MacBook Pro M1 Max 64GB
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: 24.0 tok/s decode · 8.1s TTFT (warm) · 60 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	24.0 tok/s	4392 ms	265K
Coding	C	Runs well	24.0 tok/s	8052 ms	265K
Agentic Coding	C	Runs well	24.0 tok/s	11712 ms	265K
Reasoning	C	Runs well	24.0 tok/s	9516 ms	265K
RAG	C	Runs well	24.0 tok/s	14640 ms	265K

Quantization options

How HelpingAI 15B i1 (15B params) fits at each quantization level on MacBook Pro M1 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C42
Q3_K_S	3	7.4 GB	Low	C42
NVFP4	4

Get started

Copy-paste commands to run HelpingAI 15B i1 on your machine.

Run

lms load hf-mradermacher--helpingai-15b-i1-gguf && lms server start

Upgrade options

Hardware that runs HelpingAI 15B i1 well

Mac Studio M3 Ultra 96GBBudget pick

96 GB Unified (+32)819 GB/s (+419)

Raises estimated decode speed by about 154%.60.9 tok/s decode

Raises estimated decode speed by about 154%.

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

~$3,999 MSRP

Radeon Pro W7900 48GBBest value

864 GB/s (+464)

Raises estimated decode speed by about 132%.55.7 tok/s decode

Raises estimated decode speed by about 132%.

~$3,999 MSRP

Frequently asked questions

Can MacBook Pro M1 Max 64GB run HelpingAI 15B i1?

Yes, MacBook Pro M1 Max 64GB can run HelpingAI 15B i1 with a C grade (Runs well). Expected decode speed: 24.0 tok/s.

How much VRAM does HelpingAI 15B i1 need?

HelpingAI 15B i1 (15B parameters) requires approximately 18.7 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI 15B i1?

The recommended quantization for HelpingAI 15B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI 15B i1 run at on MacBook Pro M1 Max 64GB?

On MacBook Pro M1 Max 64GB, HelpingAI 15B i1 achieves approximately 24.0 tokens per second decode speed with a time-to-first-token of 8052ms using Q4_K_M quantization.

Can MacBook Pro M1 Max 64GB run HelpingAI 15B i1 for coding?

For coding workloads, HelpingAI 15B i1 on MacBook Pro M1 Max 64GB receives a C grade with 24.0 tok/s and 265K context.

What context window can HelpingAI 15B i1 use on MacBook Pro M1 Max 64GB?

On MacBook Pro M1 Max 64GB, HelpingAI 15B i1 can safely use up to 265K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M1 Max 64GB as fast as VRAM for HelpingAI 15B i1?

See all results for MacBook Pro M1 Max 64GB See all hardware for HelpingAI 15B i1

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