Can CodeNinja 1.0 OpenChat 7B i1 run on RTX PRO 4000 Blackwell 24GB?

YES — Runs Great

C49Usable

Estimated from fit model

CodeNinja 1.0 OpenChat 7B i1 needs ~8.7 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 8.7 GB, 98.0 tok/s, Runs well

8.7 GB required24.0 GB available

36% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

315K

Memory

8.7 GB / 24.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 on RTX PRO 4000 Blackwell 24GB
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	315K
Coding	C	Runs well	98.0 tok/s	1976 ms	315K
Agentic Coding	C	Runs well	98.0 tok/s	2873 ms	315K
Reasoning	C	Runs well	98.0 tok/s	2335 ms	315K
RAG	C	Runs well	98.0 tok/s	3592 ms	315K

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on RTX PRO 4000 Blackwell 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C44
Q3_K_S	3	3.4 GB	Low	C44
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

Frequently asked questions

Can RTX PRO 4000 Blackwell 24GB run CodeNinja 1.0 OpenChat 7B i1?

Yes, RTX PRO 4000 Blackwell 24GB 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 8.7 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 RTX PRO 4000 Blackwell 24GB?

On RTX PRO 4000 Blackwell 24GB, 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 RTX PRO 4000 Blackwell 24GB run CodeNinja 1.0 OpenChat 7B i1 for coding?

For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on RTX PRO 4000 Blackwell 24GB receives a C grade with 98.0 tok/s and 315K context.

What context window can CodeNinja 1.0 OpenChat 7B i1 use on RTX PRO 4000 Blackwell 24GB?

On RTX PRO 4000 Blackwell 24GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 315K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX PRO 4000 Blackwell 24GB See all hardware for CodeNinja 1.0 OpenChat 7B i1

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