Is unified memory on MacBook Air M3 24GB as fast as VRAM for Nous Hermes 2 SOLAR 10.7B?

Not always. MacBook Air M3 24GB 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 Nous Hermes 2 SOLAR 10.7B run on MacBook Air M3 24GB?

YES — Runs Great

C49Usable

Estimated from fit model

Nous Hermes 2 SOLAR 10.7B needs ~11.3 GB VRAM. MacBook Air M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~10 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 11.3 GB, 10.4 tok/s, Runs well

11.3 GB required17.3 GB available

65% VRAM used

Fit status

Runs well

Decode

10.4 tok/s

TTFT

18583 ms

Safe context

93K

Memory

11.3 GB / 17.3 GB

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsNous Hermes 2 SOLAR 10.7B on MacBook Air M3 24GB
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: 10.4 tok/s decode · 18.6s TTFT (warm) · 26 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	10.4 tok/s	10136 ms	93K
Coding	C	Runs well	10.4 tok/s	18583 ms	93K
Agentic Coding	C	Runs well	10.4 tok/s	27029 ms	93K
Reasoning	C	Runs well	10.4 tok/s	21961 ms	93K
RAG	C	Runs well	10.4 tok/s	33787 ms	93K

Quantization options

How Nous Hermes 2 SOLAR 10.7B (10.699999809265137B params) fits at each quantization level on MacBook Air M3 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	C47
Q3_K_S	3	5.2 GB	Low	C48
NVFP4	4

Get started

Copy-paste commands to run Nous Hermes 2 SOLAR 10.7B on your machine.

Run

lms load hf-thebloke--nous-hermes-2-solar-10-7b-gguf && lms server start

Upgrade options

Hardware that runs Nous Hermes 2 SOLAR 10.7B well

MacBook Pro M2 Max 32GBBudget pick

32 GB Unified (+8)400 GB/s (+300)

Raises estimated decode speed by about 241%.35.5 tok/s decode

Raises estimated decode speed by about 241%.

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

~$1,999 MSRP

MacBook Pro M1 Max 32GBBest value

32 GB Unified (+8)400 GB/s (+300)

Raises estimated decode speed by about 224%.33.7 tok/s decode

Raises estimated decode speed by about 224%.

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

~$2,499 MSRP

MacBook Pro M4 Max 36GBApple upgrade

36 GB Unified (+12)410 GB/s (+310)

Raises estimated decode speed by about 281%.39.6 tok/s decode

Raises estimated decode speed by about 281%.

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

~$2,499 MSRP

Frequently asked questions

Can MacBook Air M3 24GB run Nous Hermes 2 SOLAR 10.7B?

Yes, MacBook Air M3 24GB can run Nous Hermes 2 SOLAR 10.7B with a C grade (Runs well). Expected decode speed: 10.4 tok/s.

How much VRAM does Nous Hermes 2 SOLAR 10.7B need?

Nous Hermes 2 SOLAR 10.7B (10.699999809265137B parameters) requires approximately 11.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Nous Hermes 2 SOLAR 10.7B?

The recommended quantization for Nous Hermes 2 SOLAR 10.7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nous Hermes 2 SOLAR 10.7B run at on MacBook Air M3 24GB?

On MacBook Air M3 24GB, Nous Hermes 2 SOLAR 10.7B achieves approximately 10.4 tokens per second decode speed with a time-to-first-token of 18583ms using Q4_K_M quantization.

Can MacBook Air M3 24GB run Nous Hermes 2 SOLAR 10.7B for coding?

For coding workloads, Nous Hermes 2 SOLAR 10.7B on MacBook Air M3 24GB receives a C grade with 10.4 tok/s and 93K context.

What context window can Nous Hermes 2 SOLAR 10.7B use on MacBook Air M3 24GB?

On MacBook Air M3 24GB, Nous Hermes 2 SOLAR 10.7B can safely use up to 93K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Air M3 24GB See all hardware for Nous Hermes 2 SOLAR 10.7B

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