Is unified memory on MacBook Pro M3 Max 64GB as fast as VRAM for NousResearch Hermes 4 14B?

Not always. MacBook Pro M3 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.

Can NousResearch Hermes 4 14B run on MacBook Pro M3 Max 64GB?

YES — Runs Great

C46Usable

Estimated from fit model

NousResearch Hermes 4 14B needs ~18.0 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~28 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) — 18.0 GB, 28.1 tok/s, Runs well

18.0 GB required46.1 GB available

39% VRAM used

Fit status

Runs well

Decode

28.1 tok/s

TTFT

6889 ms

Safe context

290K

Memory

18.0 GB / 46.1 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsNousResearch Hermes 4 14B on MacBook Pro M3 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: 28.1 tok/s decode · 6.9s TTFT (warm) · 70 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	28.1 tok/s	3758 ms	290K
Coding	C	Runs well	28.1 tok/s	6889 ms	290K
Agentic Coding	C	Runs well	28.1 tok/s	10020 ms	290K
Reasoning	C	Runs well	28.1 tok/s	8141 ms	290K
RAG	C	Runs well	28.1 tok/s	12525 ms	290K

Quantization options

How NousResearch Hermes 4 14B (14B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C42
Q3_K_S	3	6.9 GB	Low	C42
NVFP4	4

Get started

Copy-paste commands to run NousResearch Hermes 4 14B on your machine.

Run

lms load hf-bartowski--nousresearch-hermes-4-14b-gguf && lms server start

Upgrade options

Hardware that runs NousResearch Hermes 4 14B well

Mac Studio M3 Ultra 96GBBudget pick

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

Raises estimated decode speed by about 132%.65.2 tok/s decode

Raises estimated decode speed by about 132%.

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 112%.59.7 tok/s decode

Raises estimated decode speed by about 112%.

~$3,999 MSRP

Frequently asked questions

Can MacBook Pro M3 Max 64GB run NousResearch Hermes 4 14B?

Yes, MacBook Pro M3 Max 64GB can run NousResearch Hermes 4 14B with a C grade (Runs well). Expected decode speed: 28.1 tok/s.

How much VRAM does NousResearch Hermes 4 14B need?

NousResearch Hermes 4 14B (14B parameters) requires approximately 18.0 GB of memory with Q4_K_M quantization.

What is the best quantization for NousResearch Hermes 4 14B?

The recommended quantization for NousResearch Hermes 4 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will NousResearch Hermes 4 14B run at on MacBook Pro M3 Max 64GB?

On MacBook Pro M3 Max 64GB, NousResearch Hermes 4 14B achieves approximately 28.1 tokens per second decode speed with a time-to-first-token of 6889ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 64GB run NousResearch Hermes 4 14B for coding?

For coding workloads, NousResearch Hermes 4 14B on MacBook Pro M3 Max 64GB receives a C grade with 28.1 tok/s and 290K context.

What context window can NousResearch Hermes 4 14B use on MacBook Pro M3 Max 64GB?

On MacBook Pro M3 Max 64GB, NousResearch Hermes 4 14B can safely use up to 290K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M3 Max 64GB See all hardware for NousResearch Hermes 4 14B

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