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

YES — Runs Great

C48Usable

Estimated from fit model

Yi Coder 9B Chat needs ~10.6 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~69 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, 68.8 tok/s, Runs well

10.6 GB required32.0 GB available

33% VRAM used

Fit status

Runs well

Decode

68.8 tok/s

TTFT

2815 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 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: 68.8 tok/s decode · 2.8s TTFT (warm) · 172 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	68.8 tok/s	1535 ms	340K
Coding	C	Runs well	68.8 tok/s	2815 ms	340K
Agentic Coding	C	Runs well	68.8 tok/s	4094 ms	340K
Reasoning	C	Runs well	68.8 tok/s	3327 ms	340K
RAG	C	Runs well	68.8 tok/s	5118 ms	340K

Quantization options

How Yi Coder 9B Chat (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	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

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 (+160)

Adds memory headroom for longer context windows and future model growth.84.5 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 Chat?

Yes, Radeon AI PRO R9700 32GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 68.8 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 AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Yi Coder 9B Chat achieves approximately 68.8 tokens per second decode speed with a time-to-first-token of 2815ms using Q4_K_M quantization.

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

For coding workloads, Yi Coder 9B Chat on Radeon AI PRO R9700 32GB receives a C grade with 68.8 tok/s and 340K context.

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

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

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<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--yi-coder-9b-chat-gguf-on-radeon-ai-pro-r9700-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

68.8 tok/s

1535 ms

340K

Coding

Runs well

68.8 tok/s

2815 ms

340K

Agentic Coding

Runs well

68.8 tok/s

4094 ms

340K

Reasoning

Runs well

68.8 tok/s

3327 ms

340K

RAG

Runs well

68.8 tok/s

5118 ms

340K

Quantization options

How Yi Coder 9B Chat (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	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

Frequently asked questions

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

Yes, Radeon AI PRO R9700 32GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 68.8 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 AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Yi Coder 9B Chat achieves approximately 68.8 tokens per second decode speed with a time-to-first-token of 2815ms using Q4_K_M quantization.

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

For coding workloads, Yi Coder 9B Chat on Radeon AI PRO R9700 32GB receives a C grade with 68.8 tok/s and 340K context.

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

On Radeon AI PRO R9700 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.