Can Yi Coder 9B Chat run on RX 7900 XTX 24GB?

YES — Runs Great

C51Usable

○Estimated from fit model

Yi Coder 9B Chat needs ~9.8 GB VRAM. RX 7900 XTX 24GB has 24.0 GB. With Q4_K_M quantization, expect ~126 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 9.8 GB, 125.9 tok/s, Runs well

9.8 GB required24.0 GB available

41% VRAM used

Fit status

Runs well

Decode

125.9 tok/s

TTFT

1538 ms

Safe context

231K

Memory

9.8 GB / 24.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on RX 7900 XTX 24GB
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: 125.9 tok/s decode · 1.5s TTFT (warm) · 315 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	125.9 tok/s	839 ms	231K
Coding	C	Runs well	125.9 tok/s	1538 ms	231K
Agentic Coding	C	Runs well	125.9 tok/s	2237 ms	231K
Reasoning	C	Runs well	125.9 tok/s	1817 ms	231K
RAG	C	Runs well	125.9 tok/s	2796 ms	231K

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on RX 7900 XTX 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C45
Q3_K_S	3	4.4 GB	Low	C45
NVFP4	4	5.0 GB	Medium	C46
Q4_K_M	4	5.5 GB	Medium	C46
Q5_K_M	5	6.5 GB	High	C46
Q6_K	6	7.4 GB	High	C47
Q8_0	8	9.6 GB	Very High	C48
F16Best for your GPU	16	18.5 GB	Maximum	C50

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

Frequently asked questions

See all results for RX 7900 XTX 24GB See all hardware for Yi Coder 9B Chat

URL: https://willitrunai.com/can-run/hf-maziyarpanahi--yi-coder-9b-chat-gguf-on-rx-7900-xtx-24gb

⇱ Yi Coder 9B Chat on RX 7900 XTX 24GB? YES