Can vntl llama3 8b v2 run on MacBook Pro M1 Pro 16GB?

Q: Is unified memory on MacBook Pro M1 Pro 16GB as fast as VRAM for vntl llama3 8b v2?

Not always. MacBook Pro M1 Pro 16GB 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

C53Usable

Estimated from fit model

vntl llama3 8b v2 needs ~8.4 GB VRAM. MacBook Pro M1 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~27 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) — 8.4 GB, 26.6 tok/s, Runs well

8.4 GB required11.5 GB available

73% VRAM used

Fit status

Runs well

Decode

26.6 tok/s

TTFT

7267 ms

Safe context

68K

Memory

8.4 GB / 11.5 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsvntl llama3 8b v2 on MacBook Pro M1 Pro 16GB
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: 26.6 tok/s decode · 7.3s TTFT (warm) · 67 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	26.6 tok/s	3964 ms	68K
Coding	C	Runs well	26.6 tok/s	7267 ms	68K
Agentic Coding	C	Runs well	26.6 tok/s	10571 ms	68K
Reasoning	C	Runs well	26.6 tok/s	8589 ms	68K
RAG	C	Runs well	26.6 tok/s	13214 ms	68K

Quantization options

How vntl llama3 8b v2 (8B params) fits at each quantization level on MacBook Pro M1 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C50
Q3_K_S	3	3.9 GB	Low	C51
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

Intel Arc B580 12GBBudget pick

456 GB/s (+256)

Raises estimated decode speed by about 69%.44.9 tok/s decode

Raises estimated decode speed by about 69%.

~$249 MSRP

RTX 3060 12GBBest value

360 GB/s (+160)

Raises estimated decode speed by about 54%.40.9 tok/s decode

Raises estimated decode speed by about 54%.

~$329 MSRP

Frequently asked questions

Can MacBook Pro M1 Pro 16GB run vntl llama3 8b v2?

Yes, MacBook Pro M1 Pro 16GB can run vntl llama3 8b v2 with a C grade (Runs well). Expected decode speed: 26.6 tok/s.

How much VRAM does vntl llama3 8b v2 need?

vntl llama3 8b v2 (8B parameters) requires approximately 8.4 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 MacBook Pro M1 Pro 16GB?

On MacBook Pro M1 Pro 16GB, vntl llama3 8b v2 achieves approximately 26.6 tokens per second decode speed with a time-to-first-token of 7267ms using Q4_K_M quantization.

Can MacBook Pro M1 Pro 16GB run vntl llama3 8b v2 for coding?

For coding workloads, vntl llama3 8b v2 on MacBook Pro M1 Pro 16GB receives a C grade with 26.6 tok/s and 68K context.

What context window can vntl llama3 8b v2 use on MacBook Pro M1 Pro 16GB?

On MacBook Pro M1 Pro 16GB, vntl llama3 8b v2 can safely use up to 68K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M1 Pro 16GB as fast as VRAM for vntl llama3 8b v2?

See all results for MacBook Pro M1 Pro 16GB See all hardware for vntl llama3 8b v2

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