Can Qwen 3.5 9B run on Radeon RX 7600M 8GB?

BARELY — Tight on Memory

A80Great

Estimated from fit model

Qwen 3.5 9B needs ~9.4 GB VRAM. Radeon RX 7600M 8GB has 8.0 GB. With Q4_K_M quantization, expect ~18 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) — 9.4 GB, 17.8 tok/s, Very compromised (needs ~0.8 GB host RAM)

9.4 GB required8.0 GB available

118% VRAM needed

1.4 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.8 GB host RAM)

Decode

17.8 tok/s

TTFT

10863 ms

Safe context

Memory

9.4 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights5.5 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsQwen 3.5 9B on Radeon RX 7600M 8GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 17.8 tok/s decode · 10.9s TTFT (warm) · 45 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 0.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs with offload (needs ~0.2 GB host RAM)	23.2 tok/s	4560 ms	6K
Coding	A	Very compromised (needs ~0.8 GB host RAM)	17.8 tok/s	10863 ms	6K
Agentic Coding	F	Too heavy	11.4 tok/s	24601 ms	6K
Reasoning	A	Very compromised (needs ~0.8 GB host RAM)	17.8 tok/s	12838 ms	6K
RAG	F	Too heavy	11.4 tok/s	30751 ms	6K

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on Radeon RX 7600M 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	S95
Q3_K_S	3	4.4 GB	Low	S95
NVFP4Best for your GPU	4	5.0 GB	Medium	S94
Q4_K_M	4	5.5 GB	Medium	F0
Q5_K_M	5	6.5 GB	High	F0
Q6_K	6	7.4 GB	High	F0
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3.5 9B on your machine.

Run

ollama run qwen3.5:9b

Frequently asked questions

Can Radeon RX 7600M 8GB run Qwen 3.5 9B?

Yes, Radeon RX 7600M 8GB can run Qwen 3.5 9B with a A grade (Very compromised (needs ~0.8 GB host RAM)). Expected decode speed: 17.8 tok/s.

How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B (9B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 9B?

The recommended quantization for Qwen 3.5 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 9B run at on Radeon RX 7600M 8GB?

On Radeon RX 7600M 8GB, Qwen 3.5 9B achieves approximately 17.8 tokens per second decode speed with a time-to-first-token of 10863ms using Q4_K_M quantization.

Can Radeon RX 7600M 8GB run Qwen 3.5 9B for coding?

For coding workloads, Qwen 3.5 9B on Radeon RX 7600M 8GB receives a A grade with 17.8 tok/s and 6K context.

What context window can Qwen 3.5 9B use on Radeon RX 7600M 8GB?

On Radeon RX 7600M 8GB, Qwen 3.5 9B can safely use up to 6K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3.5 9B feels slow on Radeon RX 7600M 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for Radeon RX 7600M 8GB See all hardware for Qwen 3.5 9B

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Preview:

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 0.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs with offload (needs ~0.2 GB host RAM)	23.2 tok/s	4560 ms	6K
Coding	A	Very compromised (needs ~0.8 GB host RAM)	17.8 tok/s	10863 ms	6K
Agentic Coding	F	Too heavy	11.4 tok/s	24601 ms	6K
Reasoning	A	Very compromised (needs ~0.8 GB host RAM)	17.8 tok/s	12838 ms	6K
RAG	F	Too heavy	11.4 tok/s	30751 ms	6K

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

S95

Q3_K_S

4.4 GB

Low

S95

NVFP4Best for your GPU

5.0 GB

Medium

S94

Q4_K_M

5.5 GB