Can Qwen3.5 27B run on MacBook Pro M4 Pro 64GB?

Q: Is unified memory on MacBook Pro M4 Pro 64GB as fast as VRAM for Qwen3.5 27B?

Not always. MacBook Pro M4 Pro 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

C49Usable

Estimated — low-sample bucket· few comparable runs

Qwen3.5 27B needs ~27.4 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q4_K_M quantization, expect ~13 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) — 27.4 GB, 21.1 tok/s, Runs well

27.4 GB required46.1 GB available

59% VRAM used

Fit status

Runs well

Decode

21.1 tok/s

TTFT

9193 ms

Safe context

110K

Memory

27.4 GB / 46.1 GB

Memory breakdown

Weights16.5 GB

KV Cache3.2 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsQwen3.5 27B on MacBook Pro M4 Pro 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: 21.1 tok/s decode · 9.2s TTFT (warm) · 53 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	21.1 tok/s	5014 ms	110K
Coding	C	Runs well	12.8 tok/s	15169 ms	110K
Agentic Coding	C	Runs well	21.1 tok/s	13372 ms	110K
Reasoning	C	Runs well	21.1 tok/s	10865 ms	110K
RAG	C	Runs well	21.1 tok/s	16715 ms	110K

Quantization options

How Qwen3.5 27B (27B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	C44
Q3_K_S	3	13.2 GB	Low	C45
NVFP4	4

Get started

Copy-paste commands to run Qwen3.5 27B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "unsloth/Qwen3.5-27B-GGUF" \
  --hf-file "Qwen3.5-27B-GGUF-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs Qwen3.5 27B well

RTX A6000 48GBBudget pick

768 GB/s (+495)

Raises estimated decode speed by about 68%.35.4 tok/s decode

Raises estimated decode speed by about 68%.

~$4,650 MSRP

RTX PRO 5000 Blackwell 48GBBest value

1344 GB/s (+1071)

Raises estimated decode speed by about 225%.68.5 tok/s decode

Raises estimated decode speed by about 225%.

~$4,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Pro 64GB run Qwen3.5 27B?

Yes, MacBook Pro M4 Pro 64GB can run Qwen3.5 27B with a C grade (Runs well). Expected decode speed: 12.8 tok/s.

How much VRAM does Qwen3.5 27B need?

Qwen3.5 27B (27B parameters) requires approximately 27.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3.5 27B?

The recommended quantization for Qwen3.5 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 27B run at on MacBook Pro M4 Pro 64GB?

On MacBook Pro M4 Pro 64GB, Qwen3.5 27B achieves approximately 12.8 tokens per second decode speed with a time-to-first-token of 15169ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 64GB run Qwen3.5 27B for coding?

For coding workloads, Qwen3.5 27B on MacBook Pro M4 Pro 64GB receives a C grade with 12.8 tok/s and 110K context.

What context window can Qwen3.5 27B use on MacBook Pro M4 Pro 64GB?

On MacBook Pro M4 Pro 64GB, Qwen3.5 27B can safely use up to 110K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Pro 64GB as fast as VRAM for Qwen3.5 27B?

See all results for MacBook Pro M4 Pro 64GB See all hardware for Qwen3.5 27B

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