Can Vicuna 7B run on MacBook Pro M2 Max 96GB?

Q: Is unified memory on MacBook Pro M2 Max 96GB as fast as VRAM for Vicuna 7B?

Not always. MacBook Pro M2 Max 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.

YES — Runs Great

C48Usable

Estimated from fit model

Vicuna 7B needs ~23.4 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~54 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 23.4 GB, 54.3 tok/s, Runs well

23.4 GB required69.1 GB available

34% VRAM used

Fit status

Runs well

Decode

54.3 tok/s

TTFT

3563 ms

Safe context

Memory

23.4 GB / 69.1 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsVicuna 7B on MacBook Pro M2 Max 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: 54.3 tok/s decode · 3.6s TTFT (warm) · 136 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	54.3 tok/s	1944 ms	4K
Coding	C	Runs well	54.3 tok/s	3563 ms	4K
Agentic Coding	C	Runs well	54.3 tok/s	5183 ms	4K
Reasoning	C	Runs well	54.3 tok/s	4211 ms	4K
RAG	C	Runs well	54.3 tok/s	6479 ms	4K

Quantization options

How Vicuna 7B (7B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C41
Q3_K_S	3	3.4 GB	Low	C41
NVFP4	4

Get started

Copy-paste commands to run Vicuna 7B on your machine.

Run

ollama run vicuna

Upgrade options

Hardware that runs Vicuna 7B well

Mac Studio M2 Ultra 128GBBudget pick

128 GB Unified (+32)800 GB/s (+400)

Raises estimated decode speed by about 80%.98 tok/s decode

Raises estimated decode speed by about 80%.

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

~$3,999 MSRP

Mac Studio M1 Ultra 128GBBest value

128 GB Unified (+32)800 GB/s (+400)

Raises estimated decode speed by about 80%.98 tok/s decode

Raises estimated decode speed by about 80%.

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

~$3,999 MSRP

MacBook Pro M4 Max 128GBApple upgrade

128 GB Unified (+32)546 GB/s (+146)

Raises estimated decode speed by about 62%.87.8 tok/s decode

Raises estimated decode speed by about 62%.

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

~$4,999 MSRP

Frequently asked questions

Can MacBook Pro M2 Max 96GB run Vicuna 7B?

Yes, MacBook Pro M2 Max 96GB can run Vicuna 7B with a C grade (Runs well). Expected decode speed: 54.3 tok/s.

How much VRAM does Vicuna 7B need?

Vicuna 7B (7B parameters) requires approximately 23.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Vicuna 7B?

The recommended quantization for Vicuna 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Vicuna 7B run at on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Vicuna 7B achieves approximately 54.3 tokens per second decode speed with a time-to-first-token of 3563ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 96GB run Vicuna 7B for coding?

For coding workloads, Vicuna 7B on MacBook Pro M2 Max 96GB receives a C grade with 54.3 tok/s and 4K context.

What context window can Vicuna 7B use on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Vicuna 7B 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 M2 Max 96GB as fast as VRAM for Vicuna 7B?

See all results for MacBook Pro M2 Max 96GB See all hardware for Vicuna 7B

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