MacBook Pro M3 Pro 36GBOpção econômica
36 GB Unified (+4)150 GB/s (+30)
C
Raises estimated decode speed by about 37%.19.9 tok/s decodificação
Raises estimated decode speed by about 37%.
~$1,999 MSRP
Can Qwen3.5 9B Uncensored HauhauCS Aggressive run on Mac mini M4 32GB?
YES — Runs Great
C46Usable
Estimated — low-sample bucket· few comparable runs
Qwen3.5 9B Uncensored HauhauCS Aggressive needs ~10.9 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~15 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.
Select quantization to explore
Q4_K_M (Medium quality) — 10.9 GB, 14.5 tok/s, Runs well
10.9 GB required23.0 GB available
47% VRAM used
Fit status
Runs well
Decode
14.5 tok/s
TTFT
13371 ms
Safe context
200K
Memory
10.9 GB / 23.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsQwen3.5 9B Uncensored HauhauCS Aggressive on Mac mini M4 32GB
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: 14.5 tok/s decode · 13.4s TTFT (warm) · 36 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 | 14.5 tok/s | 7293 ms | 200K |
| Coding | C | Runs well | 14.5 tok/s | 13371 ms | 200K |
| Agentic Coding | C | Runs well | 14.5 tok/s | 19449 ms | 200K |
| Reasoning | C | Runs well | 14.5 tok/s | 15803 ms | 200K |
| RAG | C | Runs well | 14.5 tok/s | 24312 ms | 200K |
Quantization options
How Qwen3.5 9B Uncensored HauhauCS Aggressive (9B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C45 |
Q3_K_S | 3 | 4.4 GB | Low | C46 |
NVFP4 | 4 | 5.0 GB | Medium | C46 |
Q4_K_M | 4 | 5.5 GB | Medium | C46 |
Q5_K_M | 5 | 6.5 GB | High | C47 |
Q6_K | 6 | 7.4 GB | High | C48 |
Q8_0 | 8 | 9.6 GB | Very High | C49 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C50 |
Get started
Copy-paste commands to run Qwen3.5 9B Uncensored HauhauCS Aggressive on your machine.
Run
lms load hf-hauhaucs--qwen3-5-9b-uncensored-hauhaucs-aggressive && lms server startOpções de upgrade
Hardware que roda bem Qwen3.5 9B Uncensored HauhauCS Aggressive
MacBook Pro M4 Max 36GBMelhor custo-benefício
36 GB Unified (+4)410 GB/s (+290)
C
Raises estimated decode speed by about 254%.51.3 tok/s decodificação
Raises estimated decode speed by about 254%.
~$2,499 MSRP
Mac Studio M2 Ultra 64GBUpgrade Apple
64 GB Unified (+32)800 GB/s (+680)
C
Raises estimated decode speed by about 483%.84.5 tok/s decodificação
Raises estimated decode speed by about 483%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Frequently asked questions
Can Mac mini M4 32GB run Qwen3.5 9B Uncensored HauhauCS Aggressive?
Yes, Mac mini M4 32GB can run Qwen3.5 9B Uncensored HauhauCS Aggressive with a C grade (Runs well). Expected decode speed: 14.5 tok/s.
How much VRAM does Qwen3.5 9B Uncensored HauhauCS Aggressive need?
Qwen3.5 9B Uncensored HauhauCS Aggressive (9B parameters) requires approximately 10.9 GB of memory with Q4_K_M quantization.
What is the best quantization for Qwen3.5 9B Uncensored HauhauCS Aggressive?
The recommended quantization for Qwen3.5 9B Uncensored HauhauCS Aggressive is Q4_K_M, which balances quality and memory efficiency.
What speed will Qwen3.5 9B Uncensored HauhauCS Aggressive run at on Mac mini M4 32GB?
On Mac mini M4 32GB, Qwen3.5 9B Uncensored HauhauCS Aggressive achieves approximately 14.5 tokens per second decode speed with a time-to-first-token of 13371ms using Q4_K_M quantization.
Can Mac mini M4 32GB run Qwen3.5 9B Uncensored HauhauCS Aggressive for coding?
For coding workloads, Qwen3.5 9B Uncensored HauhauCS Aggressive on Mac mini M4 32GB receives a C grade with 14.5 tok/s and 200K context.
What context window can Qwen3.5 9B Uncensored HauhauCS Aggressive use on Mac mini M4 32GB?
On Mac mini M4 32GB, Qwen3.5 9B Uncensored HauhauCS Aggressive can safely use up to 200K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on Mac mini M4 32GB as fast as VRAM for Qwen3.5 9B Uncensored HauhauCS Aggressive?
Not always. Mac mini M4 32GB 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.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-hauhaucs--qwen3-5-9b-uncensored-hauhaucs-aggressive-on-m4-mini-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: