Mac mini M4 64GBコスパ重視
64 GB Unified (+16)
C
Adds memory headroom for longer context windows and future model growth.7.3 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$1,099 MSRP
Can Qwen3.5 35B A3B run on MacBook Pro M4 Pro 48GB?
YES — Tight Fit
C49Usable
Estimated — low-sample bucket· few comparable runs
Qwen3.5 35B A3B needs ~31.5 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~18 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 31.5 GB, 17.7 tok/s, Tight fit
31.5 GB required34.6 GB available
91% VRAM used
Fit status
Tight fit
Decode
17.7 tok/s
TTFT
10924 ms
Safe context
28K
Memory
31.5 GB / 34.6 GB
Memory breakdown
Weights21.3 GB
KV Cache4.1 GB
Runtime0.9 GB
Headroom5.2 GB
See how fast it feels
See how fast it feelsQwen3.5 35B A3B on MacBook Pro M4 Pro 48GB
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 | Tight fit | 17.7 tok/s | 5958 ms | 28K |
| Coding | C | Tight fit | 17.7 tok/s | 10924 ms | 28K |
| Agentic Coding | C | Runs with offload (needs ~0.6 GB host RAM) | 16.6 tok/s | 16919 ms | 28K |
| Reasoning | C | Tight fit | 17.7 tok/s | 12910 ms | 28K |
| RAG | C | Runs with offload (needs ~0.6 GB host RAM) | 16.6 tok/s | 21149 ms | 28K |
Quantization options
How Qwen3.5 35B A3B (35B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.7 GB | Low | C48 |
Q3_K_S | 3 | 17.2 GB | Low | C49 |
NVFP4 | 4 | 19.6 GB | Medium | C49 |
Q4_K_M | 4 | 21.3 GB | Medium | C49 |
Q5_K_MBest for your GPU | 5 | 25.2 GB | High | C49 |
Q6_K | 6 | 28.7 GB | High | F0 |
Q8_0 | 8 | 37.5 GB | Very High | F0 |
F16 | 16 | 71.8 GB | Maximum | F0 |
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アップグレードオプション
Qwen3.5 35B A3Bを快適に動かすハードウェア
MacBook Pro M4 Pro 64GBコスパ最良
64 GB Unified (+16)
C
Adds memory headroom for longer context windows and future model growth.17.7 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
MacBook Pro M3 Max 64GBAppleアップグレード
64 GB Unified (+16)400 GB/s (+127)
C
Adds memory headroom for longer context windows and future model growth.11.2 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
Frequently asked questions
Can MacBook Pro M4 Pro 48GB run Qwen3.5 35B A3B?
Yes, MacBook Pro M4 Pro 48GB can run Qwen3.5 35B A3B with a C grade (Tight fit). Expected decode speed: 17.7 tok/s.
How much VRAM does Qwen3.5 35B A3B need?
Qwen3.5 35B A3B (35B parameters) requires approximately 31.5 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 48GB?
On MacBook Pro M4 Pro 48GB, 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 48GB run Qwen3.5 35B A3B for coding?
For coding workloads, Qwen3.5 35B A3B on MacBook Pro M4 Pro 48GB receives a C grade with 17.7 tok/s and 28K context.
What context window can Qwen3.5 35B A3B use on MacBook Pro M4 Pro 48GB?
On MacBook Pro M4 Pro 48GB, Qwen3.5 35B A3B can safely use up to 28K 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 48GB as fast as VRAM for Qwen3.5 35B A3B?
Not always. MacBook Pro M4 Pro 48GB 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.
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<iframe src="https://willitrunai.com/embed/hf-unsloth--qwen3-5-35b-a3b-gguf-on-m4-pro-48gb" 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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