Can LLaVA 1.6 13B run on MacBook Pro M4 Max 128GB?

Q: Is unified memory on MacBook Pro M4 Max 128GB as fast as VRAM for LLaVA 1.6 13B?

Not always. MacBook Pro M4 Max 128GB 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

A71Great

Estimated from fit model

LLaVA 1.6 13B needs ~34.9 GB VRAM. MacBook Pro M4 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~43 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 34.9 GB, 38.2 tok/s, Runs well

34.9 GB required92.2 GB available

38% VRAM used

Fit status

Runs well

Decode

38.2 tok/s

TTFT

5072 ms

Safe context

Memory

34.9 GB / 92.2 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsLLaVA 1.6 13B on MacBook Pro M4 Max 128GB
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: 38.2 tok/s decode · 5.1s TTFT (warm) · 95 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	B	Runs well	38.2 tok/s	2766 ms	4K
Coding	A	Runs well	43.4 tok/s	4463 ms	4K
Agentic Coding	A	Runs well	38.2 tok/s	7377 ms	4K
Reasoning	A	Runs well	38.2 tok/s	5994 ms	4K
RAG	A	Runs well	38.2 tok/s	9221 ms	4K

Inference speed

LLaVA 1.6 13B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for LLaVA 1.6 13B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~151 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	151.4	Fits
RTX 4090 24GB	24 GB	Q4_K_M	96.6	Offloads

Quantization options

How LLaVA 1.6 13B (13B params) fits at each quantization level on MacBook Pro M4 Max 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B63
Q3_K_S	3	6.4 GB	Low	B63
NVFP4	4

Get started

Copy-paste commands to run LLaVA 1.6 13B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "liuhaotian/llava-v1.6-mistral-7b" \
  --hf-file "llava-v1.6-mistral-7b-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your MacBook Pro M4 Max 128GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	8.2 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S

Frequently asked questions

Can MacBook Pro M4 Max 128GB run LLaVA 1.6 13B?

Yes, MacBook Pro M4 Max 128GB can run LLaVA 1.6 13B with a A grade (Runs well). Expected decode speed: 43.4 tok/s.

How much VRAM does LLaVA 1.6 13B need?

LLaVA 1.6 13B (13B parameters) requires approximately 34.9 GB of memory with Q4_K_M quantization.

What is the best quantization for LLaVA 1.6 13B?

The recommended quantization for LLaVA 1.6 13B is Q4_K_M, which balances quality and memory efficiency.

What speed will LLaVA 1.6 13B run at on MacBook Pro M4 Max 128GB?

On MacBook Pro M4 Max 128GB, LLaVA 1.6 13B achieves approximately 43.4 tokens per second decode speed with a time-to-first-token of 4463ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 128GB run LLaVA 1.6 13B for coding?

For coding workloads, LLaVA 1.6 13B on MacBook Pro M4 Max 128GB receives a A grade with 43.4 tok/s and 4K context.

What context window can LLaVA 1.6 13B use on MacBook Pro M4 Max 128GB?

On MacBook Pro M4 Max 128GB, LLaVA 1.6 13B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 128GB as fast as VRAM for LLaVA 1.6 13B?

See all results for MacBook Pro M4 Max 128GB See all hardware for LLaVA 1.6 13B

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