Can Yi Coder 1.5B run on Intel Arc A370M 4GB?

YES — Runs Great

C50Usable

○Estimated from fit model

Yi Coder 1.5B needs ~2.4 GB VRAM. Intel Arc A370M 4GB has 4.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 2.4 GB, 21.0 tok/s, Runs well

2.4 GB required4.0 GB available

60% VRAM used

Fit status

Runs well

Decode

21.0 tok/s

TTFT

9219 ms

Safe context

162K

Memory

2.4 GB / 4.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom0.4 GB

See how fast it feels

See how fast it feelsYi Coder 1.5B on Intel Arc A370M 4GB
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: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 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	C	Runs well	21.0 tok/s	5029 ms	143K
Coding	C	Runs well	21.0 tok/s	9219 ms	162K
Agentic Coding	C	Runs well	21.0 tok/s	13410 ms	162K
Reasoning	C	Runs well	21.0 tok/s	10895 ms	162K
RAG	C	Runs well	21.0 tok/s	16762 ms	162K

Quantization options

How Yi Coder 1.5B (1.5B params) fits at each quantization level on Intel Arc A370M 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	B56
Q3_K_S	3	0.7 GB	Low	B56
NVFP4	4	0.8 GB	Medium	B56
Q4_K_M	4	0.9 GB	Medium	B56
Q5_K_M	5	1.1 GB	High	B55
Q6_K	6	1.2 GB	High	B55
Q8_0Best for your GPU	8	1.6 GB	Very High	C55
F16	16	3.1 GB	Maximum	F0

Get started

Copy-paste commands to run Yi Coder 1.5B on your machine.

Run

lms load hf-lmstudio-community--yi-coder-1-5b-gguf && lms server start

Frequently asked questions

See all results for Intel Arc A370M 4GB See all hardware for Yi Coder 1.5B

URL: https://willitrunai.com/can-run/hf-lmstudio-community--yi-coder-1-5b-gguf-on-arc-a370m-4gb

⇱ Yi Coder 1.5B on Intel Arc A370M 4GB? YES