Can Yi Coder 9B Chat run on Radeon Pro W7800 32GB?

YES — Runs Great

C48Usable

○Estimated from fit model

Yi Coder 9B Chat needs ~10.6 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~62 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) — 10.6 GB, 61.9 tok/s, Runs well

10.6 GB required32.0 GB available

33% VRAM used

Fit status

Runs well

Decode

61.9 tok/s

TTFT

3128 ms

Safe context

340K

Memory

10.6 GB / 32.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on Radeon Pro W7800 32GB
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: 61.9 tok/s decode · 3.1s TTFT (warm) · 155 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	61.9 tok/s	1706 ms	340K
Coding	C	Runs well	61.9 tok/s	3128 ms	340K
Agentic Coding	C	Runs well	61.9 tok/s	4549 ms	340K
Reasoning	C	Runs well	61.9 tok/s	3696 ms	340K
RAG	C	Runs well	61.9 tok/s	5686 ms	340K

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on Radeon Pro W7800 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C43
Q3_K_S	3	4.4 GB	Low	C44
NVFP4	4	5.0 GB	Medium	C44
Q4_K_M	4	5.5 GB	Medium	C44
Q5_K_M	5	6.5 GB	High	C44
Q6_K	6	7.4 GB	High	C45
Q8_0	8	9.6 GB	Very High	C46
F16Best for your GPU	16	18.5 GB	Maximum	C49