Can Yi Coder 9B run on Intel Arc B570 10GB?

YES — Tight Fit

B63Good

Estimated from fit model

Yi Coder 9B needs ~8.9 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q4_K_M quantization, expect ~41 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 8.9 GB, 40.6 tok/s, Tight fit

8.9 GB required10.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

40.6 tok/s

TTFT

4763 ms

Safe context

29K

Memory

8.9 GB / 10.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsYi Coder 9B on Intel Arc B570 10GB
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: 40.6 tok/s decode · 4.8s TTFT (warm) · 102 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	40.6 tok/s	2598 ms	29K
Coding	B	Tight fit	40.6 tok/s	4763 ms	29K
Agentic Coding	B	Runs with offload (needs ~0.2 GB host RAM)	29.2 tok/s	9660 ms	29K
Reasoning	B	Tight fit	40.6 tok/s	5629 ms	29K
RAG	B	Runs with offload (needs ~0.2 GB host RAM)	29.2 tok/s	12075 ms	29K

Quantization options

How Yi Coder 9B (9B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B64
Q3_K_S	3	4.4 GB	Low	B65
NVFP4	4	5.0 GB	Medium	B65
Q4_K_M	4	5.5 GB	Medium	B65
Q5_K_MBest for your GPU	5	6.5 GB	High	B64
Q6_K	6	7.4 GB	High	F0
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

Upgrade options

Hardware that runs Yi Coder 9B well

Intel Arc B580 12GBBudget pick

12 GB VRAM (+2)456 GB/s (+76)

The raw memory story may look fine, but the software ecosystem is still a constraint here.43.4 tok/s decode

~$249 MSRP

Intel Arc A770 16GBBest value

16 GB VRAM (+6)560 GB/s (+180)

Adds memory headroom for longer context windows and future model growth.49.9 tok/s decode

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

~$349 MSRP

Intel Arc Pro A60 12GBIntel upgrade

12 GB VRAM (+2)384 GB/s (+4)

The raw memory story may look fine, but the software ecosystem is still a constraint here.37.3 tok/s decode

~$499 MSRP

Frequently asked questions

Can Intel Arc B570 10GB run Yi Coder 9B?

Yes, Intel Arc B570 10GB can run Yi Coder 9B with a B grade (Tight fit). Expected decode speed: 40.6 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 8.9 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 Intel Arc B570 10GB?

On Intel Arc B570 10GB, Yi Coder 9B achieves approximately 40.6 tokens per second decode speed with a time-to-first-token of 4763ms using Q4_K_M quantization.

Can Intel Arc B570 10GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on Intel Arc B570 10GB receives a B grade with 40.6 tok/s and 29K context.

What context window can Yi Coder 9B use on Intel Arc B570 10GB?

On Intel Arc B570 10GB, Yi Coder 9B can safely use up to 29K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Yi Coder 9B feels slow on Intel Arc B570 10GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Would CUDA be a better path than Intel Arc B570 10GB for Yi Coder 9B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Arc B570 10GB See all hardware for Yi Coder 9B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

40.6 tok/s

2598 ms

29K

Coding

Tight fit

40.6 tok/s

4763 ms

29K

Agentic Coding

Runs with offload (needs ~0.2 GB host RAM)

29.2 tok/s

9660 ms

29K

Reasoning

Tight fit

40.6 tok/s

5629 ms

29K

RAG

Runs with offload (needs ~0.2 GB host RAM)

29.2 tok/s

12075 ms

29K

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

B64

Q3_K_S

4.4 GB

Low

B65

NVFP4

5.0 GB

Medium

B65

Q4_K_M

5.5 GB

Medium

B65

Q5_K_MBest for your GPU

6.5 GB

High

B64

Q6_K

7.4 GB

High

Q8_0

9.6 GB

Very High

F16

18.5 GB

Maximum

Frequently asked questions

Can Intel Arc B570 10GB run Yi Coder 9B?

Yes, Intel Arc B570 10GB can run Yi Coder 9B with a B grade (Tight fit). Expected decode speed: 40.6 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 8.9 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 Intel Arc B570 10GB?

On Intel Arc B570 10GB, Yi Coder 9B achieves approximately 40.6 tokens per second decode speed with a time-to-first-token of 4763ms using Q4_K_M quantization.

Can Intel Arc B570 10GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on Intel Arc B570 10GB receives a B grade with 40.6 tok/s and 29K context.

What context window can Yi Coder 9B use on Intel Arc B570 10GB?

On Intel Arc B570 10GB, Yi Coder 9B can safely use up to 29K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Yi Coder 9B feels slow on Intel Arc B570 10GB?

Would CUDA be a better path than Intel Arc B570 10GB for Yi Coder 9B?