Can Qwen3.5 9B run on RX 9070 16GB?

YES — Runs Great

C54Usable

Estimated from fit model

Qwen3.5 9B needs ~9.0 GB VRAM. RX 9070 16GB has 16.0 GB. With Q4_K_M quantization, expect ~72 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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, 72.3 tok/s, Runs well

9.0 GB required16.0 GB available

56% VRAM used

Fit status

Runs well

Decode

72.3 tok/s

TTFT

2679 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 9070 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: 72.3 tok/s decode · 2.7s TTFT (warm) · 181 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	72.3 tok/s	1461 ms	122K
Coding	C	Runs well	72.3 tok/s	2679 ms	122K
Agentic Coding	B	Runs well	72.3 tok/s	3896 ms	122K
Reasoning	C	Runs well	72.3 tok/s	3166 ms	122K
RAG	B	Runs well	72.3 tok/s	4870 ms	122K

Inference speed

Qwen3.5 9B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen3.5 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	125.9	Fits
RTX 3090 24GB	24 GB	Q4_K_M	119.3	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	111.3	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	101.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	84.5	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	80.1	Fits
RTX 4070 12GB	12 GB	Q4_K_M	68.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	68.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	68.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	43.7	Fits
RTX 3060 12GB	12 GB	Q4_K_M	43.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	40.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	35.2	Fits
RTX 4060 8GB	8 GB	Q4_K_M	23.4	Offloads

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Qwen3.5 9B (9B params) fits at each quantization level on RX 9070 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	C49
NVFP4	4	5.0 GB	Medium	C49
Q4_K_M	4	5.5 GB	Medium	C50
Q5_K_M	5	6.5 GB	High	C51
Q6_K	6	7.4 GB	High	C52
Q8_0Best for your GPU	8	9.6 GB	Very High	C52
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 "unsloth/Qwen3.5-9B-GGUF" \
  --hf-file "Qwen3.5-9B-GGUF-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can RX 9070 16GB run Qwen3.5 9B?

Yes, RX 9070 16GB can run Qwen3.5 9B with a C grade (Runs well). Expected decode speed: 72.3 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 9070 16GB?

On RX 9070 16GB, Qwen3.5 9B achieves approximately 72.3 tokens per second decode speed with a time-to-first-token of 2679ms using Q4_K_M quantization.

Can RX 9070 16GB run Qwen3.5 9B for coding?

For coding workloads, Qwen3.5 9B on RX 9070 16GB receives a C grade with 72.3 tok/s and 122K context.

What context window can Qwen3.5 9B use on RX 9070 16GB?

On RX 9070 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 9070 16GB See all hardware for Qwen3.5 9B

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