Can Yi Coder 9B run on RX 6700 XT 12GB?

YES — Runs Great

B66Good

Estimated from fit model

Yi Coder 9B needs ~9.1 GB VRAM. RX 6700 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~40 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) — 9.1 GB, 39.6 tok/s, Runs well

9.1 GB required12.0 GB available

76% VRAM used

Fit status

Runs well

Decode

39.6 tok/s

TTFT

4895 ms

Safe context

48K

Memory

9.1 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsYi Coder 9B on RX 6700 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: 39.6 tok/s decode · 4.9s TTFT (warm) · 99 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	39.6 tok/s	2670 ms	48K
Coding	B	Runs well	39.6 tok/s	4895 ms	48K
Agentic Coding	B	Tight fit	39.6 tok/s	7119 ms	48K
Reasoning	B	Runs well	39.6 tok/s	5784 ms	48K
RAG	B	Tight fit	39.6 tok/s	8899 ms	48K

Quantization options

How Yi Coder 9B (9B params) fits at each quantization level on RX 6700 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B62
Q3_K_S	3	4.4 GB	Low	B63
NVFP4	4	5.0 GB	Medium	B64
Q4_K_M	4	5.5 GB	Medium	B65
Q5_K_M	5	6.5 GB	High	B64
Q6_KBest for your GPU	6	7.4 GB	High	B64
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 on your machine.

Run

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

Opciones de mejora

Hardware que ejecuta bien Yi Coder 9B

RX 9070 XT 16GBOpción económica

16 GB VRAM (+4)640 GB/s (+256)

Sube la velocidad estimada de decodificación alrededor de un 105%.81.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 105%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$549 MSRP

RTX 5070 Ti 16GBMejor relación calidad-precio

16 GB VRAM (+4)896 GB/s (+512)

Sube la velocidad estimada de decodificación alrededor de un 187%.113.6 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 187%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$749 MSRP

Frequently asked questions

Can RX 6700 XT 12GB run Yi Coder 9B?

Yes, RX 6700 XT 12GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 39.6 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 9.1 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 RX 6700 XT 12GB?

On RX 6700 XT 12GB, Yi Coder 9B achieves approximately 39.6 tokens per second decode speed with a time-to-first-token of 4895ms using Q4_K_M quantization.

Can RX 6700 XT 12GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on RX 6700 XT 12GB receives a B grade with 39.6 tok/s and 48K context.

What context window can Yi Coder 9B use on RX 6700 XT 12GB?

On RX 6700 XT 12GB, Yi Coder 9B can safely use up to 48K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RX 6700 XT 12GB See all hardware for Yi Coder 9B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/yi-coder-9b-on-rx-6700-xt-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: