Can Yi Coder 9B run on RX 9070 16GB?

YES — Runs Great

B66Good

Estimated from fit model

Yi Coder 9B needs ~9.5 GB VRAM. RX 9070 16GB has 16.0 GB. With Q4_K_M quantization, expect ~79 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) — 9.5 GB, 78.6 tok/s, Runs well

9.5 GB required16.0 GB available

59% VRAM used

Fit status

Runs well

Decode

78.6 tok/s

TTFT

2463 ms

Safe context

87K

Memory

9.5 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsYi Coder 9B on RX 9070 16GB
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: 78.6 tok/s decode · 2.5s TTFT (warm) · 197 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	78.6 tok/s	1343 ms	87K
Coding	B	Runs well	78.6 tok/s	2463 ms	87K
Agentic Coding	B	Runs well	78.6 tok/s	3583 ms	87K
Reasoning	B	Runs well	78.6 tok/s	2911 ms	87K
RAG	B	Runs well	78.6 tok/s	4478 ms	87K

Inference speed

Yi Coder 9B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Yi Coder 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	126.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	121.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	110.3	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	91.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	87.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	74.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	74.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	74.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	47.5	Fits
RTX 3060 12GB	12 GB	Q4_K_M	47.1	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	43.6	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	38.3	Fits
RTX 4060 8GB	8 GB	Q4_K_M	23.3	Heavy offload

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Yi Coder 9B (9B params) fits at each quantization level on RX 9070 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B60
Q3_K_S	3	4.4 GB	Low	B60
NVFP4	4	5.0 GB	Medium	B61
Q4_K_M	4	5.5 GB	Medium	B61
Q5_K_M	5	6.5 GB	High	B62
Q6_K	6	7.4 GB	High	B63
Q8_0Best for your GPU	8	9.6 GB	Very High	B63
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

Frequently asked questions

Can RX 9070 16GB run Yi Coder 9B?

Yes, RX 9070 16GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 78.6 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 9.5 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 9070 16GB?

On RX 9070 16GB, Yi Coder 9B achieves approximately 78.6 tokens per second decode speed with a time-to-first-token of 2463ms using Q4_K_M quantization.

Can RX 9070 16GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on RX 9070 16GB receives a B grade with 78.6 tok/s and 87K context.

What context window can Yi Coder 9B use on RX 9070 16GB?

On RX 9070 16GB, Yi Coder 9B can safely use up to 87K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RX 9070 16GB See all hardware for Yi Coder 9B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

78.6 tok/s

1343 ms

87K

Coding

Runs well

78.6 tok/s

2463 ms

87K

Agentic Coding

Runs well

78.6 tok/s

3583 ms

87K

Reasoning

Runs well

78.6 tok/s

2911 ms

87K

RAG

Runs well

78.6 tok/s

4478 ms

87K

Yi Coder 9B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

126.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

126.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

126.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

126.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

121.0

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

110.3

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

91.9

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

87.2

Fits

RTX 4070 12GB

12 GB

Q4_K_M

74.9

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

74.3

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

74.3

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

47.5

Fits

RTX 3060 12GB

12 GB

Q4_K_M

47.1

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

43.6

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

38.3

Fits

RTX 4060 8GB

8 GB

Q4_K_M

23.3

Heavy offload

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

B60

Q3_K_S

4.4 GB

Low

B60

NVFP4

5.0 GB

Medium

B61

Q4_K_M

5.5 GB

Medium

B61

Q5_K_M

6.5 GB

High

B62

Q6_K

7.4 GB

High

B63

Q8_0Best for your GPU

9.6 GB

Very High

B63

F16

18.5 GB

Maximum

Frequently asked questions

Can RX 9070 16GB run Yi Coder 9B?

Yes, RX 9070 16GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 78.6 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 9.5 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 9070 16GB?

On RX 9070 16GB, Yi Coder 9B achieves approximately 78.6 tokens per second decode speed with a time-to-first-token of 2463ms using Q4_K_M quantization.

Can RX 9070 16GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on RX 9070 16GB receives a B grade with 78.6 tok/s and 87K context.

What context window can Yi Coder 9B use on RX 9070 16GB?

On RX 9070 16GB, Yi Coder 9B can safely use up to 87K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.