Is unified memory on Mac Studio M3 Ultra 96GB as fast as VRAM for vntl llama3 8b v2?

Not always. Mac Studio M3 Ultra 96GB 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 vntl llama3 8b v2 run on Mac Studio M3 Ultra 96GB?

YES — Runs Great

C47Usable

Estimated from fit model

vntl llama3 8b v2 needs ~17.1 GB VRAM. Mac Studio M3 Ultra 96GB has 69.1 GB. With Q4_K_M quantization, expect ~112 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) — 17.1 GB, 112.0 tok/s, Runs well

17.1 GB required69.1 GB available

25% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

904K

Memory

17.1 GB / 69.1 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsvntl llama3 8b v2 on Mac Studio M3 Ultra 96GB
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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 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	112.0 tok/s	943 ms	904K
Coding	C	Runs well	112.0 tok/s	1729 ms	904K
Agentic Coding	C	Runs well	112.0 tok/s	2514 ms	904K
Reasoning	C	Runs well	112.0 tok/s	2043 ms	904K
RAG	C	Runs well	112.0 tok/s	3143 ms	904K

Quantization options

How vntl llama3 8b v2 (8B params) fits at each quantization level on Mac Studio M3 Ultra 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C40
Q3_K_S	3	3.9 GB	Low	C40
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

Frequently asked questions

Can Mac Studio M3 Ultra 96GB run vntl llama3 8b v2?

Yes, Mac Studio M3 Ultra 96GB can run vntl llama3 8b v2 with a C grade (Runs well). Expected decode speed: 112.0 tok/s.

How much VRAM does vntl llama3 8b v2 need?

vntl llama3 8b v2 (8B parameters) requires approximately 17.1 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 Studio M3 Ultra 96GB?

On Mac Studio M3 Ultra 96GB, vntl llama3 8b v2 achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 96GB run vntl llama3 8b v2 for coding?

For coding workloads, vntl llama3 8b v2 on Mac Studio M3 Ultra 96GB receives a C grade with 112.0 tok/s and 904K context.

What context window can vntl llama3 8b v2 use on Mac Studio M3 Ultra 96GB?

On Mac Studio M3 Ultra 96GB, vntl llama3 8b v2 can safely use up to 904K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Mac Studio M3 Ultra 96GB See all hardware for vntl llama3 8b v2

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/hf-lmg-anon--vntl-llama3-8b-v2-gguf-on-m3-ultra-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: