Can Yi Coder 9B Chat run on RX 7600 8GB?

Q: What should I upgrade first if Yi Coder 9B Chat feels slow on RX 7600 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

C49Usable

Estimated from fit model

Yi Coder 9B Chat needs ~8.2 GB VRAM. RX 7600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~21 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, 21.4 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

21.4 tok/s

TTFT

9039 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 feelsYi Coder 9B Chat on RX 7600 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: 21.4 tok/s decode · 9.0s TTFT (warm) · 54 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.

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	30.4 tok/s	3471 ms	12K
Coding	C	Runs with offload	21.4 tok/s	9039 ms	12K
Agentic Coding	D	Very compromised (needs ~0.8 GB host RAM)	16.6 tok/s	16940 ms	12K
Reasoning	C	Runs with offload (needs ~0.2 GB host RAM)	21.4 tok/s	10683 ms	12K
RAG	D	Very compromised (needs ~0.8 GB host RAM)	16.6 tok/s	21175 ms

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on RX 7600 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 Yi Coder 9B Chat on your machine.

Run

lms load hf-maziyarpanahi--yi-coder-9b-chat-gguf && lms server start

Upgrade options

Hardware that runs Yi Coder 9B Chat well

RX 7600 XT 16GBBudget pick

16 GB VRAM (+8)

Raises estimated decode speed by about 42%.30.4 tok/s decode

Raises estimated decode speed by about 42%.

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

~$329 MSRP

RX 9060 XT 16GBBest value

16 GB VRAM (+8)320 GB/s (+32)

Raises estimated decode speed by about 71%.36.7 tok/s decode

Raises estimated decode speed by about 71%.

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

~$349 MSRP

RX 7700 XT 12GBAMD upgrade

12 GB VRAM (+4)432 GB/s (+144)

Raises estimated decode speed by about 121%.47.2 tok/s decode

Raises estimated decode speed by about 121%.

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

~$449 MSRP

Frequently asked questions

Can RX 7600 8GB run Yi Coder 9B Chat?

Yes, RX 7600 8GB can run Yi Coder 9B Chat with a C grade (Runs with offload). Expected decode speed: 21.4 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 8.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 9B Chat?

The recommended quantization for Yi Coder 9B Chat is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi Coder 9B Chat run at on RX 7600 8GB?

On RX 7600 8GB, Yi Coder 9B Chat achieves approximately 21.4 tokens per second decode speed with a time-to-first-token of 9039ms using Q4_K_M quantization.

Can RX 7600 8GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on RX 7600 8GB receives a C grade with 21.4 tok/s and 12K context.

What context window can Yi Coder 9B Chat use on RX 7600 8GB?

On RX 7600 8GB, Yi Coder 9B Chat can safely use up to 12K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Yi Coder 9B Chat feels slow on RX 7600 8GB?

See all results for RX 7600 8GB See all hardware for Yi Coder 9B Chat

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--yi-coder-9b-chat-gguf-on-rx-7600-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: