Can vntl llama3 8b v2 run on MacBook Pro M3 Pro 36GB?

Q: Is unified memory on MacBook Pro M3 Pro 36GB as fast as VRAM for vntl llama3 8b v2?

Not always. MacBook Pro M3 Pro 36GB 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

C46Usable

Estimated from fit model

vntl llama3 8b v2 needs ~10.6 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~22 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) — 10.6 GB, 22.4 tok/s, Runs well

10.6 GB required25.9 GB available

41% VRAM used

Fit status

Runs well

Decode

22.4 tok/s

TTFT

8628 ms

Safe context

277K

Memory

10.6 GB / 25.9 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom3.9 GB

See how fast it feels

See how fast it feelsvntl llama3 8b v2 on MacBook Pro M3 Pro 36GB
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: 22.4 tok/s decode · 8.6s TTFT (warm) · 56 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	22.4 tok/s	4706 ms	277K
Coding	C	Runs well	22.4 tok/s	8628 ms	277K
Agentic Coding	C	Runs well	22.4 tok/s	12550 ms	277K
Reasoning	C	Runs well	22.4 tok/s	10197 ms	277K
RAG	C	Runs well	22.4 tok/s	15687 ms	277K

Inference speed

vntl llama3 8b v2 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for vntl llama3 8b v2 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0

Quantization options

How vntl llama3 8b v2 (8B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C44
Q3_K_S	3	3.9 GB	Low	C45
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 M4 Max 48GBBudget pick

48 GB Unified (+12)546 GB/s (+396)

Raises estimated decode speed by about 243%.76.8 tok/s decode

Raises estimated decode speed by about 243%.

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

~$2,499 MSRP

MacBook Pro M3 Max 48GBBest value

48 GB Unified (+12)400 GB/s (+250)

Raises estimated decode speed by about 120%.49.2 tok/s decode

Raises estimated decode speed by about 120%.

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

~$2,499 MSRP

MacBook Pro M4 Max 96GBApple upgrade

96 GB Unified (+60)546 GB/s (+396)

Raises estimated decode speed by about 243%.76.8 tok/s decode

Raises estimated decode speed by about 243%.

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

~$2,499 MSRP

Frequently asked questions

Can MacBook Pro M3 Pro 36GB run vntl llama3 8b v2?

Yes, MacBook Pro M3 Pro 36GB can run vntl llama3 8b v2 with a C grade (Runs well). Expected decode speed: 22.4 tok/s.

How much VRAM does vntl llama3 8b v2 need?

vntl llama3 8b v2 (8B parameters) requires approximately 10.6 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 M3 Pro 36GB?

On MacBook Pro M3 Pro 36GB, vntl llama3 8b v2 achieves approximately 22.4 tokens per second decode speed with a time-to-first-token of 8628ms using Q4_K_M quantization.

Can MacBook Pro M3 Pro 36GB run vntl llama3 8b v2 for coding?

For coding workloads, vntl llama3 8b v2 on MacBook Pro M3 Pro 36GB receives a C grade with 22.4 tok/s and 277K context.

What context window can vntl llama3 8b v2 use on MacBook Pro M3 Pro 36GB?

On MacBook Pro M3 Pro 36GB, vntl llama3 8b v2 can safely use up to 277K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Pro 36GB as fast as VRAM for vntl llama3 8b v2?

See all results for MacBook Pro M3 Pro 36GB See all hardware for vntl llama3 8b v2

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