Can Ornith 1.0 9B run on GTX 1650 4GB?

Q: What should I upgrade first if Ornith 1.0 9B feels slow on GTX 1650 4GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q2_0_G128

B65Good

Estimated from fit model

Ornith 1.0 9B needs ~4.3 GB VRAM. GTX 1650 4GB has 4.0 GB. With Q2_0_G128 quantization, expect ~12 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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.

Ornith 1.0 9B at Q4_K_M needs 9.0 GB — too much for GTX 1650 4GB (4.0 GB). Runs at Q2_0_G128 (4.3 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.5 GB, exceeds 4.0 GB available

7.5 GB required4.0 GB available

188% VRAM needed

3.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

7.5 GB / 4.0 GB

Offload

50%

Memory breakdown

Weights5.7 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsOrnith 1.0 9B 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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Inference speed

Ornith 1.0 9B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Ornith 1.0 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~132 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	131.6	Fits
RTX 4090 24GB	24 GB	Q4_K_M	131.6	Fits
RTX 3090 24GB	24 GB	Q4_K_M	122.8

Quantization options

How Ornith 1.0 9B (9.399999618530273B params) fits at each quantization level on GTX 1650 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128Best for your GPU	1.125	1.4 GB	Very Low	A83
Q2_0_G128	1.71	2.5 GB	Low	F0

Get started

Copy-paste commands to run Ornith 1.0 9B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "deepreinforce-ai/Ornith-1.0-9B" \
  --hf-file "Ornith-1.0-9B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs Ornith 1.0 9B well

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.17.6 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.38.8 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

RTX 5050 8GBNVIDIA upgrade

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

Makes the model fit on the accelerator instead of staying completely out of reach.26.7 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 Ornith 1.0 9B?

Yes, GTX 1650 4GB can run Ornith 1.0 9B at Q2_0_G128 quantization (Runs with offload (needs ~0.2 GB host RAM)). The recommended Q4_K_M requires 9.0 GB which exceeds available memory, but at Q2_0_G128 it needs only 4.3 GB. Expected decode speed: 12.3 tok/s.

How much VRAM does Ornith 1.0 9B need?

Ornith 1.0 9B (9.399999618530273B parameters) requires approximately 9.0 GB at Q4_K_M quantization. On GTX 1650 4GB, it fits at Q2_0_G128 using 4.3 GB.

What is the best quantization for Ornith 1.0 9B?

The recommended quantization is Q4_K_M, but on GTX 1650 4GB the best fitting quantization is Q2_0_G128, which uses 4.3 GB.

What speed will Ornith 1.0 9B run at on GTX 1650 4GB?

On GTX 1650 4GB, Ornith 1.0 9B achieves approximately 12.3 tokens per second decode speed with a time-to-first-token of 15748ms using Q2_0_G128 quantization.

Can GTX 1650 4GB run Ornith 1.0 9B for coding?

For coding workloads, Ornith 1.0 9B on GTX 1650 4GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Ornith 1.0 9B use on GTX 1650 4GB?

On GTX 1650 4GB, Ornith 1.0 9B can safely use up to 6K tokens of context at Q2_0_G128 quantization. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for GTX 1650 4GB See all hardware for Ornith 1.0 9B

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