Can Vicuna 7B run on Mac mini M4 64GB?

YES — Runs Great

C47Usable

Estimated — low-sample bucket· few comparable runs

Vicuna 7B needs ~19.9 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~19 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) — 19.9 GB, 18.6 tok/s, Runs well

19.9 GB required46.1 GB available

43% VRAM used

Fit status

Runs well

Decode

18.6 tok/s

TTFT

10400 ms

Safe context

Memory

19.9 GB / 46.1 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsVicuna 7B on Mac mini M4 64GB
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: 18.6 tok/s decode · 10.4s TTFT (warm) · 47 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	18.6 tok/s	5673 ms	4K
Coding	C	Runs well	18.6 tok/s	10400 ms	4K
Agentic Coding	C	Runs well	18.6 tok/s	15127 ms	4K
Reasoning	C	Runs well	18.6 tok/s	12291 ms	4K
RAG	C	Runs well	18.6 tok/s	18909 ms	4K

Quantization options

How Vicuna 7B (7B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C42
Q3_K_S	3	3.4 GB	Low	C42
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

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+32)546 GB/s (+426)

Raises estimated decode speed by about 372%.87.8 tok/s decode

Raises estimated decode speed by about 372%.

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

~$2,499 MSRP

MacBook Pro M3 Max 128GBBest value

128 GB Unified (+64)400 GB/s (+280)

Raises estimated decode speed by about 202%.56.2 tok/s decode

Raises estimated decode speed by about 202%.

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

~$2,499 MSRP

Mac Studio M3 Ultra 96GBApple upgrade

96 GB Unified (+32)819 GB/s (+699)

Raises estimated decode speed by about 427%.98 tok/s decode

Raises estimated decode speed by about 427%.

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

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run Vicuna 7B?

Yes, Mac mini M4 64GB can run Vicuna 7B with a C grade (Runs well). Expected decode speed: 18.6 tok/s.

How much VRAM does Vicuna 7B need?

Vicuna 7B (7B parameters) requires approximately 19.9 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 Mac mini M4 64GB?

On Mac mini M4 64GB, Vicuna 7B achieves approximately 18.6 tokens per second decode speed with a time-to-first-token of 10400ms using Q4_K_M quantization.

Can Mac mini M4 64GB run Vicuna 7B for coding?

For coding workloads, Vicuna 7B on Mac mini M4 64GB receives a C grade with 18.6 tok/s and 4K context.

What context window can Vicuna 7B use on Mac mini M4 64GB?

On Mac mini M4 64GB, 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 Mac mini M4 64GB as fast as VRAM for Vicuna 7B?

Not always. Mac mini M4 64GB 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.

See all results for Mac mini M4 64GB See all hardware for Vicuna 7B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/vicuna-7b-on-m4-mini-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: