Can Yi Coder 9B run on Radeon AI PRO R9700 32GB?

YES — Runs Great

B61Good

Estimated from fit model

Yi Coder 9B needs ~11.1 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~75 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) — 11.1 GB, 74.8 tok/s, Runs well

11.1 GB required32.0 GB available

35% VRAM used

Fit status

Runs well

Decode

74.8 tok/s

TTFT

2588 ms

Safe context

131K

Memory

11.1 GB / 32.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsYi Coder 9B on Radeon AI PRO R9700 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: 74.8 tok/s decode · 2.6s TTFT (warm) · 187 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	B	Runs well	74.8 tok/s	1412 ms	131K
Coding	B	Runs well	74.8 tok/s	2588 ms	131K
Agentic Coding	B	Runs well	74.8 tok/s	3765 ms	131K
Reasoning	B	Runs well	74.8 tok/s	3059 ms	131K
RAG	B	Runs well	74.8 tok/s	4706 ms	131K

Quantization options

How Yi Coder 9B (9B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B55
Q3_K_S	3	4.4 GB	Low	B56
NVFP4	4

Get started

Copy-paste commands to run Yi Coder 9B on your machine.

Run

lms load Yi-Coder-9B-Chat && lms server start

Upgrade options

Hardware that runs Yi Coder 9B well

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)

This setup is broadly balanced for this model.74.3 tok/s decode

~$2,499 MSRP

Mac Studio M2 Ultra 64GBBest value

64 GB Unified (+32)800 GB/s (+160)

Adds memory headroom for longer context windows and future model growth.91.9 tok/s decode

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

~$3,999 MSRP

Frequently asked questions

Can Radeon AI PRO R9700 32GB run Yi Coder 9B?

Yes, Radeon AI PRO R9700 32GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 74.8 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 11.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 9B?

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

What speed will Yi Coder 9B run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Yi Coder 9B achieves approximately 74.8 tokens per second decode speed with a time-to-first-token of 2588ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on Radeon AI PRO R9700 32GB receives a B grade with 74.8 tok/s and 131K context.

What context window can Yi Coder 9B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Yi Coder 9B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Radeon AI PRO R9700 32GB See all hardware for Yi Coder 9B

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