Can OpenHermes 2.5 7B run on MacBook Pro M1 Max 64GB?

Q: Is unified memory on MacBook Pro M1 Max 64GB as fast as VRAM for OpenHermes 2.5 7B?

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

C48Usable

Estimated from fit model

OpenHermes 2.5 7B needs ~14.0 GB VRAM. MacBook Pro M1 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~55 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) — 14.0 GB, 55.4 tok/s, Runs well

14.0 GB required46.1 GB available

30% VRAM used

Fit status

Runs well

Decode

55.4 tok/s

TTFT

3495 ms

Safe context

Memory

14.0 GB / 46.1 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsOpenHermes 2.5 7B 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: 55.4 tok/s decode · 3.5s TTFT (warm) · 139 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	55.4 tok/s	1907 ms	8K
Coding	C	Runs well	55.4 tok/s	3495 ms	8K
Agentic Coding	C	Runs well	55.4 tok/s	5084 ms	8K
Reasoning	C	Runs well	55.4 tok/s	4131 ms	8K
RAG	C	Runs well	55.4 tok/s	6355 ms	8K

Quantization options

How OpenHermes 2.5 7B (7B params) fits at each quantization level on MacBook Pro M1 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C42
Q3_K_S	3	3.4 GB	Low	C42
NVFP4	4

Get started

Copy-paste commands to run OpenHermes 2.5 7B on your machine.

Run

ollama run openhermes

Upgrade options

Hardware that runs OpenHermes 2.5 7B well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+32)546 GB/s (+146)

Raises estimated decode speed by about 70%.94.4 tok/s decode

Raises estimated decode speed by about 70%.

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

~$2,499 MSRP

Mac Studio M3 Ultra 96GBBest value

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

Raises estimated decode speed by about 77%.98 tok/s decode

Raises estimated decode speed by about 77%.

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

~$3,999 MSRP

Frequently asked questions

Can MacBook Pro M1 Max 64GB run OpenHermes 2.5 7B?

Yes, MacBook Pro M1 Max 64GB can run OpenHermes 2.5 7B with a C grade (Runs well). Expected decode speed: 55.4 tok/s.

How much VRAM does OpenHermes 2.5 7B need?

OpenHermes 2.5 7B (7B parameters) requires approximately 14.0 GB of memory with Q4_K_M quantization.

What is the best quantization for OpenHermes 2.5 7B?

The recommended quantization for OpenHermes 2.5 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will OpenHermes 2.5 7B run at on MacBook Pro M1 Max 64GB?

On MacBook Pro M1 Max 64GB, OpenHermes 2.5 7B achieves approximately 55.4 tokens per second decode speed with a time-to-first-token of 3495ms using Q4_K_M quantization.

Can MacBook Pro M1 Max 64GB run OpenHermes 2.5 7B for coding?

For coding workloads, OpenHermes 2.5 7B on MacBook Pro M1 Max 64GB receives a C grade with 55.4 tok/s and 8K context.

What context window can OpenHermes 2.5 7B use on MacBook Pro M1 Max 64GB?

On MacBook Pro M1 Max 64GB, OpenHermes 2.5 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M1 Max 64GB as fast as VRAM for OpenHermes 2.5 7B?

See all results for MacBook Pro M1 Max 64GB See all hardware for OpenHermes 2.5 7B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/openhermes-2.5-7b-on-m1-max-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: