Can Qwen3.5 9B Uncensored HauhauCS Aggressive run on RX 7700 XT 12GB?

YES — Runs Great

B55Good

Estimated from fit model

Qwen3.5 9B Uncensored HauhauCS Aggressive needs ~8.6 GB VRAM. RX 7700 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~47 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) — 8.6 GB, 47.2 tok/s, Runs well

8.6 GB required12.0 GB available

72% VRAM used

Fit status

Runs well

Decode

47.2 tok/s

TTFT

4101 ms

Safe context

67K

Memory

8.6 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsQwen3.5 9B Uncensored HauhauCS Aggressive on RX 7700 XT 12GB
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: 47.2 tok/s decode · 4.1s TTFT (warm) · 118 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	47.2 tok/s	2237 ms	67K
Coding	B	Runs well	47.2 tok/s	4101 ms	67K
Agentic Coding	B	Runs well	47.2 tok/s	5964 ms	67K
Reasoning	B	Runs well	47.2 tok/s	4846 ms	67K
RAG	B	Runs well	47.2 tok/s	7456 ms	67K

Quantization options

How Qwen3.5 9B Uncensored HauhauCS Aggressive (9B params) fits at each quantization level on RX 7700 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C50
Q3_K_S	3	4.4 GB	Low	C51
NVFP4	4	5.0 GB	Medium	C52
Q4_K_M	4	5.5 GB	Medium	C53
Q5_K_M	5	6.5 GB	High	C52
Q6_KBest for your GPU	6	7.4 GB	High	C52
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

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 start

Frequently asked questions

Can RX 7700 XT 12GB run Qwen3.5 9B Uncensored HauhauCS Aggressive?

Yes, RX 7700 XT 12GB can run Qwen3.5 9B Uncensored HauhauCS Aggressive with a B grade (Runs well). Expected decode speed: 47.2 tok/s.

How much VRAM does Qwen3.5 9B Uncensored HauhauCS Aggressive need?

Qwen3.5 9B Uncensored HauhauCS Aggressive (9B parameters) requires approximately 8.6 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 RX 7700 XT 12GB?

On RX 7700 XT 12GB, Qwen3.5 9B Uncensored HauhauCS Aggressive achieves approximately 47.2 tokens per second decode speed with a time-to-first-token of 4101ms using Q4_K_M quantization.

Can RX 7700 XT 12GB run Qwen3.5 9B Uncensored HauhauCS Aggressive for coding?

For coding workloads, Qwen3.5 9B Uncensored HauhauCS Aggressive on RX 7700 XT 12GB receives a B grade with 47.2 tok/s and 67K context.

What context window can Qwen3.5 9B Uncensored HauhauCS Aggressive use on RX 7700 XT 12GB?

On RX 7700 XT 12GB, Qwen3.5 9B Uncensored HauhauCS Aggressive can safely use up to 67K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7700 XT 12GB See all hardware for Qwen3.5 9B Uncensored HauhauCS Aggressive

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<iframe src="https://willitrunai.com/embed/hf-hauhaucs--qwen3-5-9b-uncensored-hauhaucs-aggressive-on-rx-7700-xt-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

47.2 tok/s

2237 ms

67K

Coding

Runs well

47.2 tok/s

4101 ms

67K

Agentic Coding

Runs well

47.2 tok/s

5964 ms

67K

Reasoning

Runs well

47.2 tok/s

4846 ms

67K

RAG

Runs well

47.2 tok/s

7456 ms

67K

Quantization options

How Qwen3.5 9B Uncensored HauhauCS Aggressive (9B params) fits at each quantization level on RX 7700 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C50
Q3_K_S	3	4.4 GB	Low	C51
NVFP4	4	5.0 GB	Medium	C52
Q4_K_M	4	5.5 GB	Medium	C53
Q5_K_M	5	6.5 GB	High	C52
Q6_KBest for your GPU	6	7.4 GB	High	C52
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0