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

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

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)

This setup is broadly balanced for this model.68.3 tok/s decode

~$2,499 MSRP

Mac Studio M2 Ultra 64GBBest value

64 GB Unified (+32)800 GB/s (+224)

Raises estimated decode speed by about 37%.84.5 tok/s decode

Raises estimated decode speed by about 37%.

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

~$3,999 MSRP

Frequently asked questions

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

Yes, Radeon Pro W7800 32GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 61.9 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 10.6 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 Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, Yi Coder 9B Chat achieves approximately 61.9 tokens per second decode speed with a time-to-first-token of 3128ms using Q4_K_M quantization.

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

For coding workloads, Yi Coder 9B Chat on Radeon Pro W7800 32GB receives a C grade with 61.9 tok/s and 340K context.

What context window can Yi Coder 9B Chat use on Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, Yi Coder 9B Chat can safely use up to 340K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon Pro W7800 32GB See all hardware for Yi Coder 9B Chat

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