Can Qwen 3 0.6B run on Mac Studio M2 Ultra 64GB?

Q: Is unified memory on Mac Studio M2 Ultra 64GB as fast as VRAM for Qwen 3 0.6B?

Not always. Mac Studio M2 Ultra 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.

YES — Runs Great

C45Usable

Estimated from fit model

Qwen 3 0.6B needs ~8.9 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~8 tok/s.

Runtime: MLXCapacity: RoomyBandwidth: HighStack: OptimizedBottleneck: 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) — 8.9 GB, 8.4 tok/s, Runs well

8.9 GB required46.1 GB available

19% VRAM used

Fit status

Runs well

Decode

8.4 tok/s

TTFT

23048 ms

Safe context

33K

Memory

8.9 GB / 46.1 GB

Memory breakdown

Weights0.4 GB

KV Cache0.9 GB

Runtime0.8 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsQwen 3 0.6B on Mac Studio M2 Ultra 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: 8.4 tok/s decode · 23.0s TTFT (warm) · 21 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	8.4 tok/s	12571 ms	33K
Coding	C	Runs well	8.4 tok/s	23048 ms	33K
Agentic Coding	C	Runs well	8.4 tok/s	33524 ms	33K
Reasoning	C	Runs well	8.4 tok/s	27238 ms	33K
RAG	C	Runs well	8.4 tok/s	41905 ms	33K

Quantization options

How Qwen 3 0.6B (0.6000000238418579B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.2 GB	Low	C48
Q3_K_S	3	0.3 GB	Low	C48
NVFP4	4

Get started

Copy-paste commands to run Qwen 3 0.6B on your machine.

Run

ollama run qwen3:0.6b

Frequently asked questions

Can Mac Studio M2 Ultra 64GB run Qwen 3 0.6B?

Yes, Mac Studio M2 Ultra 64GB can run Qwen 3 0.6B with a C grade (Runs well). Expected decode speed: 8.4 tok/s.

How much VRAM does Qwen 3 0.6B need?

Qwen 3 0.6B (0.6000000238418579B parameters) requires approximately 8.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3 0.6B?

The recommended quantization for Qwen 3 0.6B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3 0.6B run at on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Qwen 3 0.6B achieves approximately 8.4 tokens per second decode speed with a time-to-first-token of 23048ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 64GB run Qwen 3 0.6B for coding?

For coding workloads, Qwen 3 0.6B on Mac Studio M2 Ultra 64GB receives a C grade with 8.4 tok/s and 33K context.

What context window can Qwen 3 0.6B use on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Qwen 3 0.6B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 64GB as fast as VRAM for Qwen 3 0.6B?

See all results for Mac Studio M2 Ultra 64GB See all hardware for Qwen 3 0.6B

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