Can CodeNinja 1.0 OpenChat 7B i1 run on GTX 1650 4GB?

Q: What should I upgrade first if CodeNinja 1.0 OpenChat 7B i1 feels slow on GTX 1650 4GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

NO — Won't Fit

F0Won't run

Estimated from fit model

CodeNinja 1.0 OpenChat 7B i1 needs ~6.7 GB but GTX 1650 4GB only has 4.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: Very lowStack: BasicBottleneck: Memory capacity

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) — 6.7 GB, exceeds 4.0 GB available

6.7 GB required4.0 GB available

168% VRAM needed

2.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.5 tok/s

TTFT

54795 ms

Safe context

Memory

6.7 GB / 4.0 GB

Offload

40%

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 on GTX 1650 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: 3.5 tok/s decode · 54.8s TTFT (warm) · 9 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 6.7 GB, but this setup only exposes 4.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	4.1 tok/s	26011 ms	4K
Coding	F	Too heavy	3.5 tok/s	54795 ms	4K
Agentic Coding	F	Too heavy	2.7 tok/s	102747 ms	4K
Reasoning	F	Too heavy	3.5 tok/s	64757 ms	4K
RAG	F	Too heavy	2.7 tok/s	128434 ms	4K

Inference speed

CodeNinja 1.0 OpenChat 7B i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for CodeNinja 1.0 OpenChat 7B i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on GTX 1650 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	F0
Q3_K_S	3	3.4 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs CodeNinja 1.0 OpenChat 7B i1 well

GTX 1660 Super 6GBNVIDIA upgrade

6 GB VRAM (+2)336 GB/s (+208)

Makes the model fit on the accelerator instead of staying completely out of reach.23.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 566%.

~$229 MSRP

RTX 3050 8GBBudget pick

8 GB VRAM (+4)224 GB/s (+96)

Makes the model fit on the accelerator instead of staying completely out of reach.34.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$249 MSRP

RTX 5060 8GBBest value

8 GB VRAM (+4)448 GB/s (+320)

Makes the model fit on the accelerator instead of staying completely out of reach.64 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$299 MSRP

Frequently asked questions

Can GTX 1650 4GB run CodeNinja 1.0 OpenChat 7B i1?

No, CodeNinja 1.0 OpenChat 7B i1 requires more memory than GTX 1650 4GB provides.

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

CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 6.7 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 GTX 1650 4GB?

On GTX 1650 4GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 3.5 tokens per second decode speed with a time-to-first-token of 54795ms using Q4_K_M quantization.

Can GTX 1650 4GB run CodeNinja 1.0 OpenChat 7B i1 for coding?

For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on GTX 1650 4GB receives a F grade with 3.5 tok/s and 4K context.

What context window can CodeNinja 1.0 OpenChat 7B i1 use on GTX 1650 4GB?

On GTX 1650 4GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for GTX 1650 4GB See all hardware for CodeNinja 1.0 OpenChat 7B i1

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