Can Qwen3.5 9B Uncensored HauhauCS Aggressive run on RTX 2070 8GB?

Q: What should I upgrade first if Qwen3.5 9B Uncensored HauhauCS Aggressive feels slow on RTX 2070 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

C51Usable

Estimated from fit model

Qwen3.5 9B Uncensored HauhauCS Aggressive needs ~8.2 GB VRAM. RTX 2070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~33 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 8.2 GB, 33.4 tok/s, Runs with offload (needs ~0.2 GB host RAM)

8.2 GB required8.0 GB available

102% VRAM needed

0.2 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

33.4 tok/s

TTFT

5792 ms

Safe context

12K

Memory

8.2 GB / 8.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsQwen3.5 9B Uncensored HauhauCS Aggressive on RTX 2070 8GB
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: 33.4 tok/s decode · 5.8s TTFT (warm) · 84 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload	49.0 tok/s	2157 ms	12K
Coding	C	Runs with offload	33.4 tok/s	5792 ms	12K
Agentic Coding	C	Very compromised	25.7 tok/s	10973 ms	12K
Reasoning	C	Runs with offload	33.4 tok/s	6845 ms	12K
RAG	C	Very compromised	25.7 tok/s	13717 ms	12K

Quantization options

How Qwen3.5 9B Uncensored HauhauCS Aggressive (9B params) fits at each quantization level on RTX 2070 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C54
Q3_K_S	3	4.4 GB	Low	C53
NVFP4Best for your GPU

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 3060 12GBBudget pick

12 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.36.4 tok/s decode

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

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+8)

Raises estimated decode speed by about 51%.50.6 tok/s decode

Raises estimated decode speed by about 51%.

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

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)

Adds memory headroom for longer context windows and future model growth.35.2 tok/s decode

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

~$499 MSRP

Frequently asked questions

Can RTX 2070 8GB run Qwen3.5 9B Uncensored HauhauCS Aggressive?

Yes, RTX 2070 8GB can run Qwen3.5 9B Uncensored HauhauCS Aggressive with a C grade (Runs with offload). Expected decode speed: 33.4 tok/s.

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

Qwen3.5 9B Uncensored HauhauCS Aggressive (9B parameters) requires approximately 8.2 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 2070 8GB?

On RTX 2070 8GB, Qwen3.5 9B Uncensored HauhauCS Aggressive achieves approximately 33.4 tokens per second decode speed with a time-to-first-token of 5792ms using Q4_K_M quantization.

Can RTX 2070 8GB run Qwen3.5 9B Uncensored HauhauCS Aggressive for coding?

For coding workloads, Qwen3.5 9B Uncensored HauhauCS Aggressive on RTX 2070 8GB receives a C grade with 33.4 tok/s and 12K context.

What context window can Qwen3.5 9B Uncensored HauhauCS Aggressive use on RTX 2070 8GB?

On RTX 2070 8GB, Qwen3.5 9B Uncensored HauhauCS Aggressive can safely use up to 12K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 2070 8GB See all hardware for Qwen3.5 9B Uncensored HauhauCS Aggressive

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