Can vntl llama3 8b v2 run on Mac mini M2 24GB?

Q: Is unified memory on Mac mini M2 24GB as fast as VRAM for vntl llama3 8b v2?

Not always. Mac mini M2 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.

YES — Runs Great

C47Usable

Estimated from fit model

vntl llama3 8b v2 needs ~9.3 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~13 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) — 9.3 GB, 13.3 tok/s, Runs well

9.3 GB required17.3 GB available

54% VRAM used

Fit status

Runs well

Decode

13.3 tok/s

TTFT

14535 ms

Safe context

152K

Memory

9.3 GB / 17.3 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsvntl llama3 8b v2 on Mac mini M2 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: 13.3 tok/s decode · 14.5s TTFT (warm) · 33 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	13.3 tok/s	7928 ms	152K
Coding	C	Runs well	13.3 tok/s	14535 ms	152K
Agentic Coding	C	Runs well	13.3 tok/s	21142 ms	152K
Reasoning	C	Runs well	13.3 tok/s	17178 ms	152K
RAG	C	Runs well	13.3 tok/s	26427 ms	152K

Quantization options

How vntl llama3 8b v2 (8B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C47
Q3_K_S	3	3.9 GB	Low	C47
NVFP4	4

Get started

Copy-paste commands to run vntl llama3 8b v2 on your machine.

Run

lms load hf-lmg-anon--vntl-llama3-8b-v2-gguf && lms server start

Upgrade options

Hardware that runs vntl llama3 8b v2 well

MacBook Pro M2 Max 32GBBudget pick

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

Raises estimated decode speed by about 257%.47.5 tok/s decode

Raises estimated decode speed by about 257%.

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

~$1,999 MSRP

MacBook Pro M2 Pro 32GBBest value

32 GB Unified (+8)200 GB/s (+100)

Raises estimated decode speed by about 116%.28.7 tok/s decode

Raises estimated decode speed by about 116%.

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

~$1,999 MSRP

Mac Studio M2 Ultra 64GBApple upgrade

64 GB Unified (+40)800 GB/s (+700)

Raises estimated decode speed by about 615%.95.1 tok/s decode

Raises estimated decode speed by about 615%.

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

~$3,999 MSRP

Frequently asked questions

Can Mac mini M2 24GB run vntl llama3 8b v2?

Yes, Mac mini M2 24GB can run vntl llama3 8b v2 with a C grade (Runs well). Expected decode speed: 13.3 tok/s.

How much VRAM does vntl llama3 8b v2 need?

vntl llama3 8b v2 (8B parameters) requires approximately 9.3 GB of memory with Q4_K_M quantization.

What is the best quantization for vntl llama3 8b v2?

The recommended quantization for vntl llama3 8b v2 is Q4_K_M, which balances quality and memory efficiency.

What speed will vntl llama3 8b v2 run at on Mac mini M2 24GB?

On Mac mini M2 24GB, vntl llama3 8b v2 achieves approximately 13.3 tokens per second decode speed with a time-to-first-token of 14535ms using Q4_K_M quantization.

Can Mac mini M2 24GB run vntl llama3 8b v2 for coding?

For coding workloads, vntl llama3 8b v2 on Mac mini M2 24GB receives a C grade with 13.3 tok/s and 152K context.

What context window can vntl llama3 8b v2 use on Mac mini M2 24GB?

On Mac mini M2 24GB, vntl llama3 8b v2 can safely use up to 152K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac mini M2 24GB as fast as VRAM for vntl llama3 8b v2?

See all results for Mac mini M2 24GB See all hardware for vntl llama3 8b v2

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