Can Vicuna 13B run on Mac mini M4 64GB?

YES — Runs Great

B68Good

Estimated — low-sample bucket· few comparable runs

Vicuna 13B needs ~27.9 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~10 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) — 27.9 GB, 9.6 tok/s, Runs well

27.9 GB required46.1 GB available

61% VRAM used

Fit status

Runs well

Decode

9.6 tok/s

TTFT

20192 ms

Safe context

Memory

27.9 GB / 46.1 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsVicuna 13B 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: 9.6 tok/s decode · 20.2s TTFT (warm) · 24 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	9.6 tok/s	11014 ms	4K
Coding	B	Runs well	9.6 tok/s	20192 ms	4K
Agentic Coding	B	Tight fit	9.6 tok/s	29370 ms	4K
Reasoning	B	Runs well	9.6 tok/s	23863 ms	4K
RAG	B	Tight fit	9.6 tok/s	36713 ms	4K

Inference speed

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

Estimated decode speed (tokens/sec) for Vicuna 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
RX 7900 XTX 24GB	24 GB	Q4_K_M	87.2	Offloads
RTX 3090 24GB	24 GB	Q4_K_M	82.6	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	70.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	58.5	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	55.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	38.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	38.2	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	30.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	27.7	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	27.1	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.3	Fits
RTX 4070 12GB	12 GB	Q4_K_M	9.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	6.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	3.8	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Vicuna 13B (13B params) fits at each quantization level on Mac mini M4 64GB (46.1 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	7.3 GB	Medium	B63
Q4_K_M	4	7.9 GB	Medium	B63
Q5_K_M	5	9.4 GB	High	B64
Q6_K	6	10.7 GB	High	B64
Q8_0	8	13.9 GB	Very High	B65
F16Best for your GPU	16	26.7 GB	Maximum	B69

Get started

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

Run

ollama run vicuna:13b

Opciones de mejora

Hardware que ejecuta bien Vicuna 13B

MacBook Pro M4 Max 96GBOpción económica

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

Sube la velocidad estimada de decodificación alrededor de un 298%.38.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 298%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

MacBook Pro M2 Max 96GBMejor relación calidad-precio

96 GB Unified (+32)400 GB/s (+280)

Sube la velocidad estimada de decodificación alrededor de un 205%.29.3 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 205%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,199 MSRP

Mac Studio M3 Ultra 96GBMejora Apple

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

Sube la velocidad estimada de decodificación alrededor de un 631%.70.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 631%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run Vicuna 13B?

Yes, Mac mini M4 64GB can run Vicuna 13B with a B grade (Runs well). Expected decode speed: 9.6 tok/s.

How much VRAM does Vicuna 13B need?

Vicuna 13B (13B parameters) requires approximately 27.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Vicuna 13B?

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

What speed will Vicuna 13B run at on Mac mini M4 64GB?

On Mac mini M4 64GB, Vicuna 13B achieves approximately 9.6 tokens per second decode speed with a time-to-first-token of 20192ms using Q4_K_M quantization.

Can Mac mini M4 64GB run Vicuna 13B for coding?

For coding workloads, Vicuna 13B on Mac mini M4 64GB receives a B grade with 9.6 tok/s and 4K context.

What context window can Vicuna 13B use on Mac mini M4 64GB?

On Mac mini M4 64GB, Vicuna 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 Mac mini M4 64GB as fast as VRAM for Vicuna 13B?

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 13B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/vicuna-13b-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: