Can Yi Coder 1.5B run on NVIDIA B200 180GB?

YES — Runs Great

D39Poor

Estimated from fit model

Yi Coder 1.5B needs ~20.3 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 20.3 GB, 21.0 tok/s, Runs well

20.3 GB required180.0 GB available

11% VRAM used

Fit status

Runs well

Decode

21.0 tok/s

TTFT

9219 ms

Safe context

14.6M

Memory

20.3 GB / 180.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.2 GB

Runtime1.2 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsYi Coder 1.5B on NVIDIA B200 180GB
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

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	D	Runs well	21.0 tok/s	5029 ms	12.8M
Coding	D	Runs well	21.0 tok/s	9219 ms	14.6M
Agentic Coding	D	Runs well	21.0 tok/s	13410 ms	14.6M
Reasoning	D	Runs well	21.0 tok/s	10895 ms	14.6M
RAG	D	Runs well	21.0 tok/s	16762 ms	14.6M

Quantization options

How Yi Coder 1.5B (1.5B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	D37
Q3_K_S	3	0.7 GB	Low	D37
NVFP4	4	0.8 GB	Medium	D37
Q4_K_M	4	0.9 GB	Medium	D37
Q5_K_M	5	1.1 GB	High	D37
Q6_K	6	1.2 GB	High	D37
Q8_0	8	1.6 GB	Very High	D37
F16Best for your GPU	16	3.1 GB	Maximum	D37

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

Upgrade-Optionen

Hardware, die Yi Coder 1.5B gut ausführt

Mac Studio M3 Ultra 256GBGünstige Wahl

256 GB Unified (+76)

This setup is broadly balanced for this model.21 tok/s Dekodierung

ca. $6,999 MSRP

Frequently asked questions

Can NVIDIA B200 180GB run Yi Coder 1.5B?

Yes, NVIDIA B200 180GB can run Yi Coder 1.5B with a D grade (Runs well). Expected decode speed: 21.0 tok/s.

How much VRAM does Yi Coder 1.5B need?

Yi Coder 1.5B (1.5B parameters) requires approximately 20.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 1.5B?

The recommended quantization for Yi Coder 1.5B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi Coder 1.5B run at on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Yi Coder 1.5B achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run Yi Coder 1.5B for coding?

For coding workloads, Yi Coder 1.5B on NVIDIA B200 180GB receives a D grade with 21.0 tok/s and 14.6M context.

What context window can Yi Coder 1.5B use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Yi Coder 1.5B can safely use up to 14.6M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA B200 180GB See all hardware for Yi Coder 1.5B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/hf-lmstudio-community--yi-coder-1-5b-gguf-on-b200-180gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: