MacBook Pro M4 Max 36GBOpción económica
36 GB Unified (+4)410 GB/s (+210)
C
Sube la velocidad estimada de decodificación alrededor de un 116%.51.3 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 116%.
~$2,499 MSRP
Can Qwen3.5 9B Uncensored HauhauCS Aggressive run on MacBook Pro M1 Pro 32GB?
YES — Runs Great
C48Usable
Estimated from fit model
Qwen3.5 9B Uncensored HauhauCS Aggressive needs ~10.9 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~24 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, 23.7 tok/s, Runs well
10.9 GB required23.0 GB available
47% VRAM used
Fit status
Runs well
Decode
23.7 tok/s
TTFT
8176 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 MacBook Pro M1 Pro 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: 23.7 tok/s decode · 8.2s TTFT (warm) · 59 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 | 23.7 tok/s | 4460 ms | 200K |
| Coding | C | Runs well | 23.7 tok/s | 8176 ms | 200K |
| Agentic Coding | C | Runs well | 23.7 tok/s | 11892 ms | 200K |
| Reasoning | C | Runs well | 23.7 tok/s | 9662 ms | 200K |
| RAG | C | Runs well | 23.7 tok/s | 14865 ms | 200K |
Quantization options
How Qwen3.5 9B Uncensored HauhauCS Aggressive (9B params) fits at each quantization level on MacBook Pro M1 Pro 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 startOpciones de mejora
Hardware que ejecuta bien Qwen3.5 9B Uncensored HauhauCS Aggressive
MacBook Pro M4 Max 48GBMejor relación calidad-precio
48 GB Unified (+16)546 GB/s (+346)
C
Sube la velocidad estimada de decodificación alrededor de un 188%.68.3 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 188%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Mac Studio M2 Ultra 64GBMejora Apple
64 GB Unified (+32)800 GB/s (+600)
C
Sube la velocidad estimada de decodificación alrededor de un 257%.84.5 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 257%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$3,999 MSRP
Frequently asked questions
Can MacBook Pro M1 Pro 32GB run Qwen3.5 9B Uncensored HauhauCS Aggressive?
Yes, MacBook Pro M1 Pro 32GB can run Qwen3.5 9B Uncensored HauhauCS Aggressive with a C grade (Runs well). Expected decode speed: 23.7 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 MacBook Pro M1 Pro 32GB?
On MacBook Pro M1 Pro 32GB, Qwen3.5 9B Uncensored HauhauCS Aggressive achieves approximately 23.7 tokens per second decode speed with a time-to-first-token of 8176ms using Q4_K_M quantization.
Can MacBook Pro M1 Pro 32GB run Qwen3.5 9B Uncensored HauhauCS Aggressive for coding?
For coding workloads, Qwen3.5 9B Uncensored HauhauCS Aggressive on MacBook Pro M1 Pro 32GB receives a C grade with 23.7 tok/s and 200K context.
What context window can Qwen3.5 9B Uncensored HauhauCS Aggressive use on MacBook Pro M1 Pro 32GB?
On MacBook Pro M1 Pro 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 MacBook Pro M1 Pro 32GB as fast as VRAM for Qwen3.5 9B Uncensored HauhauCS Aggressive?
Not always. MacBook Pro M1 Pro 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.
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<iframe src="https://willitrunai.com/embed/hf-hauhaucs--qwen3-5-9b-uncensored-hauhaucs-aggressive-on-m1-pro-32gb" 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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