Can Qwen3.5 9B Uncensored HauhauCS Aggressive run on RTX A2000 12GB?

YES — Runs Great

C55Usable

Estimated from fit model

Qwen3.5 9B Uncensored HauhauCS Aggressive needs ~8.9 GB VRAM. RTX A2000 12GB has 12.0 GB. With Q4_K_M quantization, expect ~41 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 8.9 GB, 40.9 tok/s, Runs well

8.9 GB required12.0 GB available

74% VRAM used

Fit status

Runs well

Decode

40.9 tok/s

TTFT

4731 ms

Safe context

62K

Memory

8.9 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsQwen3.5 9B Uncensored HauhauCS Aggressive on RTX A2000 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: 40.9 tok/s decode · 4.7s TTFT (warm) · 102 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	40.9 tok/s	2581 ms	62K
Coding	C	Runs well	40.9 tok/s	4731 ms	62K
Agentic Coding	C	Tight fit	40.9 tok/s	6882 ms	62K
Reasoning	C	Runs well	40.9 tok/s	5592 ms	62K
RAG	C	Tight fit	40.9 tok/s	8603 ms	62K

Quantization options

How Qwen3.5 9B Uncensored HauhauCS Aggressive (9B params) fits at each quantization level on RTX A2000 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

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

Upgrade options

Hardware that runs Qwen3.5 9B Uncensored HauhauCS Aggressive well

RTX 5070 Ti 16GBBudget pick

16 GB VRAM (+4)896 GB/s (+608)

Raises estimated decode speed by about 156%.104.5 tok/s decode

Raises estimated decode speed by about 156%.

Adds memory headroom for longer context windows and future model growth.

~$749 MSRP

RTX 4070 Ti Super 16GBBest value

16 GB VRAM (+4)672 GB/s (+384)

Raises estimated decode speed by about 139%.97.9 tok/s decode

Raises estimated decode speed by about 139%.

Adds memory headroom for longer context windows and future model growth.

~$799 MSRP

RTX 4080 Super 16GBNVIDIA upgrade

16 GB VRAM (+4)736 GB/s (+448)

Raises estimated decode speed by about 172%.111.3 tok/s decode

Raises estimated decode speed by about 172%.

Adds memory headroom for longer context windows and future model growth.

~$999 MSRP

Frequently asked questions

Can RTX A2000 12GB run Qwen3.5 9B Uncensored HauhauCS Aggressive?

Yes, RTX A2000 12GB can run Qwen3.5 9B Uncensored HauhauCS Aggressive with a C grade (Runs well). Expected decode speed: 40.9 tok/s.

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

Qwen3.5 9B Uncensored HauhauCS Aggressive (9B parameters) requires approximately 8.9 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 RTX A2000 12GB?

On RTX A2000 12GB, Qwen3.5 9B Uncensored HauhauCS Aggressive achieves approximately 40.9 tokens per second decode speed with a time-to-first-token of 4731ms using Q4_K_M quantization.

Can RTX A2000 12GB run Qwen3.5 9B Uncensored HauhauCS Aggressive for coding?

For coding workloads, Qwen3.5 9B Uncensored HauhauCS Aggressive on RTX A2000 12GB receives a C grade with 40.9 tok/s and 62K context.

What context window can Qwen3.5 9B Uncensored HauhauCS Aggressive use on RTX A2000 12GB?

On RTX A2000 12GB, Qwen3.5 9B Uncensored HauhauCS Aggressive can safely use up to 62K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX A2000 12GB See all hardware for Qwen3.5 9B Uncensored HauhauCS Aggressive

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