Can Qwen3.5 35B A3B run on MacBook Pro M4 Pro 64GB?

Q: Is unified memory on MacBook Pro M4 Pro 64GB as fast as VRAM for Qwen3.5 35B A3B?

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

C52Usable

Estimated — low-sample bucket· few comparable runs

Qwen3.5 35B A3B needs ~33.3 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q4_K_M quantization, expect ~18 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) — 33.3 GB, 17.7 tok/s, Runs well

33.3 GB required46.1 GB available

72% VRAM used

Fit status

Runs well

Decode

17.7 tok/s

TTFT

10924 ms

Safe context

66K

Memory

33.3 GB / 46.1 GB

Memory breakdown

Weights21.3 GB

KV Cache4.1 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsQwen3.5 35B A3B 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: 17.7 tok/s decode · 10.9s TTFT (warm) · 44 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	17.7 tok/s	5958 ms	66K
Coding	C	Runs well	17.7 tok/s	10924 ms	66K
Agentic Coding	C	Runs well	17.7 tok/s	15889 ms	66K
Reasoning	C	Runs well	17.7 tok/s	12910 ms	66K
RAG	C	Runs well	17.7 tok/s	19862 ms	66K

Quantization options

How Qwen3.5 35B A3B (35B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	C45
Q3_K_S	3	17.2 GB	Low	C46
NVFP4	4

Get started

Copy-paste commands to run Qwen3.5 35B A3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "unsloth/Qwen3.5-35B-A3B-GGUF" \
  --hf-file "Qwen3.5-35B-A3B-GGUF-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs Qwen3.5 35B A3B well

RTX A6000 48GBBudget pick

768 GB/s (+495)

Raises estimated decode speed by about 54%.27.3 tok/s decode

Raises estimated decode speed by about 54%.

~$4,650 MSRP

RTX PRO 5000 Blackwell 48GBBest value

1344 GB/s (+1071)

Raises estimated decode speed by about 199%.52.9 tok/s decode

Raises estimated decode speed by about 199%.

~$4,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Pro 64GB run Qwen3.5 35B A3B?

Yes, MacBook Pro M4 Pro 64GB can run Qwen3.5 35B A3B with a C grade (Runs well). Expected decode speed: 17.7 tok/s.

How much VRAM does Qwen3.5 35B A3B need?

Qwen3.5 35B A3B (35B parameters) requires approximately 33.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3.5 35B A3B?

The recommended quantization for Qwen3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 35B A3B run at on MacBook Pro M4 Pro 64GB?

On MacBook Pro M4 Pro 64GB, Qwen3.5 35B A3B achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10924ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 64GB run Qwen3.5 35B A3B for coding?

For coding workloads, Qwen3.5 35B A3B on MacBook Pro M4 Pro 64GB receives a C grade with 17.7 tok/s and 66K context.

What context window can Qwen3.5 35B A3B use on MacBook Pro M4 Pro 64GB?

On MacBook Pro M4 Pro 64GB, Qwen3.5 35B A3B can safely use up to 66K 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 35B A3B?

See all results for MacBook Pro M4 Pro 64GB See all hardware for Qwen3.5 35B A3B

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<iframe src="https://willitrunai.com/embed/hf-unsloth--qwen3-5-35b-a3b-gguf-on-m4-pro-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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