Can Qwen3.5 9B run on RX 7600 XT 16GB?

YES — Runs Great

C51Usable

Estimated from fit model

Qwen3.5 9B needs ~9.0 GB VRAM. RX 7600 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~30 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) — 9.0 GB, 30.4 tok/s, Runs well

9.0 GB required16.0 GB available

56% VRAM used

Fit status

Runs well

Decode

30.4 tok/s

TTFT

6363 ms

Safe context

122K

Memory

9.0 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen3.5 9B on RX 7600 XT 16GB
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: 30.4 tok/s decode · 6.4s TTFT (warm) · 76 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	30.4 tok/s	3471 ms	122K
Coding	C	Runs well	30.4 tok/s	6363 ms	122K
Agentic Coding	C	Runs well	30.4 tok/s	9255 ms	122K
Reasoning	C	Runs well	30.4 tok/s	7520 ms	122K
RAG	C	Runs well	30.4 tok/s	11569 ms	122K

Quantization options

How Qwen3.5 9B (9B params) fits at each quantization level on RX 7600 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C48
Q3_K_S	3	4.4 GB	Low	C48
NVFP4	4	5.0 GB	Medium	C49
Q4_K_M	4	5.5 GB	Medium	C49
Q5_K_M	5	6.5 GB	High	C50
Q6_K	6	7.4 GB	High	C51
Q8_0Best for your GPU	8	9.6 GB	Very High	C51
F16	16	18.5 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen3.5 9B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "lmstudio-community/Qwen3.5-9B-GGUF" \
  --hf-file "Qwen3.5-9B-GGUF-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Opciones de mejora

Hardware que ejecuta bien Qwen3.5 9B

RX 7900 XT 20GBOpción económica

20 GB VRAM (+4)800 GB/s (+512)

Sube la velocidad estimada de decodificación alrededor de un 188%.87.4 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.

~$899 MSRP

RX 7900 XTX 24GBMejor relación calidad-precio

24 GB VRAM (+8)960 GB/s (+672)

Sube la velocidad estimada de decodificación alrededor de un 314%.125.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 314%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$999 MSRP

Frequently asked questions

Can RX 7600 XT 16GB run Qwen3.5 9B?

Yes, RX 7600 XT 16GB can run Qwen3.5 9B with a C grade (Runs well). Expected decode speed: 30.4 tok/s.

How much VRAM does Qwen3.5 9B need?

Qwen3.5 9B (9B parameters) requires approximately 9.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3.5 9B?

The recommended quantization for Qwen3.5 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 9B run at on RX 7600 XT 16GB?

On RX 7600 XT 16GB, Qwen3.5 9B achieves approximately 30.4 tokens per second decode speed with a time-to-first-token of 6363ms using Q4_K_M quantization.

Can RX 7600 XT 16GB run Qwen3.5 9B for coding?

For coding workloads, Qwen3.5 9B on RX 7600 XT 16GB receives a C grade with 30.4 tok/s and 122K context.

What context window can Qwen3.5 9B use on RX 7600 XT 16GB?

On RX 7600 XT 16GB, Qwen3.5 9B can safely use up to 122K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7600 XT 16GB See all hardware for Qwen3.5 9B

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<iframe src="https://willitrunai.com/embed/hf-lmstudio-community--qwen3-5-9b-gguf-on-rx-7600-xt-16gb" 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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