Can HelpingAI2 9B run on Mac Studio M2 Ultra 64GB?

Q: Is unified memory on Mac Studio M2 Ultra 64GB as fast as VRAM for HelpingAI2 9B?

Not always. Mac Studio M2 Ultra 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

C48Usable

Estimated from fit model

HelpingAI2 9B needs ~14.4 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~85 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) — 14.4 GB, 84.5 tok/s, Runs well

14.4 GB required46.1 GB available

31% VRAM used

Fit status

Runs well

Decode

84.5 tok/s

TTFT

2291 ms

Safe context

497K

Memory

14.4 GB / 46.1 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsHelpingAI2 9B on Mac Studio M2 Ultra 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: 84.5 tok/s decode · 2.3s TTFT (warm) · 211 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	84.5 tok/s	1249 ms	497K
Coding	C	Runs well	84.5 tok/s	2291 ms	497K
Agentic Coding	C	Runs well	84.5 tok/s	3332 ms	497K
Reasoning	C	Runs well	84.5 tok/s	2707 ms	497K
RAG	C	Runs well	84.5 tok/s	4165 ms	497K

Inference speed

HelpingAI2 9B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for HelpingAI2 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits

Quantization options

How HelpingAI2 9B (9B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C41
Q3_K_S	3	4.4 GB	Low	C41
NVFP4	4

Get started

Copy-paste commands to run HelpingAI2 9B on your machine.

Run

lms load hf-bartowski--helpingai2-9b-gguf && lms server start

Frequently asked questions

Can Mac Studio M2 Ultra 64GB run HelpingAI2 9B?

Yes, Mac Studio M2 Ultra 64GB can run HelpingAI2 9B with a C grade (Runs well). Expected decode speed: 84.5 tok/s.

How much VRAM does HelpingAI2 9B need?

HelpingAI2 9B (9B parameters) requires approximately 14.4 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI2 9B?

The recommended quantization for HelpingAI2 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI2 9B run at on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, HelpingAI2 9B achieves approximately 84.5 tokens per second decode speed with a time-to-first-token of 2291ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 64GB run HelpingAI2 9B for coding?

For coding workloads, HelpingAI2 9B on Mac Studio M2 Ultra 64GB receives a C grade with 84.5 tok/s and 497K context.

What context window can HelpingAI2 9B use on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, HelpingAI2 9B can safely use up to 497K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 64GB as fast as VRAM for HelpingAI2 9B?

See all results for Mac Studio M2 Ultra 64GB See all hardware for HelpingAI2 9B

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