Can Yi Coder 9B Chat run on RX 6750 XT 12GB?

YES — Runs Great

C54Usable

○Estimated from fit model

Yi Coder 9B Chat needs ~8.6 GB VRAM. RX 6750 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~42 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) — 8.6 GB, 41.7 tok/s, Runs well

8.6 GB required12.0 GB available

72% VRAM used

Fit status

Runs well

Decode

41.7 tok/s

TTFT

4642 ms

Safe context

67K

Memory

8.6 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on RX 6750 XT 12GB
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: 41.7 tok/s decode · 4.6s TTFT (warm) · 104 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	41.7 tok/s	2532 ms	67K
Coding	C	Runs well	41.7 tok/s	4642 ms	67K
Agentic Coding	C	Runs well	41.7 tok/s	6752 ms	67K
Reasoning	C	Runs well	41.7 tok/s	5486 ms	67K
RAG	C	Runs well	41.7 tok/s	8440 ms	67K

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on RX 6750 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C50
Q3_K_S	3	4.4 GB	Low	C51
NVFP4	4	5.0 GB	Medium	C52
Q4_K_M	4	5.5 GB	Medium	C53
Q5_K_M	5	6.5 GB	High	C52
Q6_KBest for your GPU	6	7.4 GB	High	C52
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

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 6750 XT 12GB See all hardware for Yi Coder 9B Chat

URL: https://willitrunai.com/can-run/hf-maziyarpanahi--yi-coder-9b-chat-gguf-on-rx-6750-xt-12gb

⇱ Yi Coder 9B Chat on RX 6750 XT 12GB? YES