Can CodeNinja 1.0 OpenChat 7B i1 run on NVIDIA B200 180GB?

YES — Runs Great

C44Usable

Estimated from fit model

CodeNinja 1.0 OpenChat 7B i1 needs ~24.3 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~98 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) — 24.3 GB, 98.0 tok/s, Runs well

24.3 GB required180.0 GB available

14% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

3.1M

Memory

24.3 GB / 180.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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	C	Runs well	98.0 tok/s	1078 ms	3.1M
Coding	C	Runs well	98.0 tok/s	1976 ms	3.1M
Agentic Coding	C	Runs well	98.0 tok/s	2873 ms	3.1M
Reasoning	C	Runs well	98.0 tok/s	2335 ms	3.1M
RAG	C	Runs well	98.0 tok/s	3592 ms	3.1M

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	D37
Q3_K_S	3	3.4 GB	Low	D37
NVFP4	4

Get started

Copy-paste commands to run CodeNinja 1.0 OpenChat 7B i1 on your machine.

Run

lms load hf-mradermacher--codeninja-1-0-openchat-7b-i1-gguf && lms server start

Upgrade options

Hardware that runs CodeNinja 1.0 OpenChat 7B i1 well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+76)

This setup is broadly balanced for this model.98 tok/s decode

~$6,999 MSRP

Frequently asked questions

Can NVIDIA B200 180GB run CodeNinja 1.0 OpenChat 7B i1?

Yes, NVIDIA B200 180GB can run CodeNinja 1.0 OpenChat 7B i1 with a C grade (Runs well). Expected decode speed: 98.0 tok/s.

How much VRAM does CodeNinja 1.0 OpenChat 7B i1 need?

CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 24.3 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeNinja 1.0 OpenChat 7B i1?

The recommended quantization for CodeNinja 1.0 OpenChat 7B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeNinja 1.0 OpenChat 7B i1 run at on NVIDIA B200 180GB?

On NVIDIA B200 180GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run CodeNinja 1.0 OpenChat 7B i1 for coding?

For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on NVIDIA B200 180GB receives a C grade with 98.0 tok/s and 3.1M context.

What context window can CodeNinja 1.0 OpenChat 7B i1 use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 3.1M 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 CodeNinja 1.0 OpenChat 7B i1

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