Can Ornith 1.0 9B run on RTX 5050 8GB?

BARELY — Tight on Memory

B66Good

Estimated from fit model

Ornith 1.0 9B needs ~9.4 GB VRAM. RTX 5050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~14 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.9 GB, 26.7 tok/s, Runs with offload

7.9 GB required8.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

26.7 tok/s

TTFT

7242 ms

Safe context

19K

Memory

7.9 GB / 8.0 GB

Memory breakdown

Weights5.7 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsOrnith 1.0 9B on RTX 5050 8GB
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: 26.7 tok/s decode · 7.2s TTFT (warm) · 67 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.

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 {ram} 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	A	Runs with offload	17.2 tok/s	6150 ms	5K
Coding	B	Very compromised	13.7 tok/s	14106 ms	5K
Agentic Coding	F	Too heavy	9.3 tok/s	30172 ms	5K
Reasoning	B	Very compromised	13.7 tok/s	16671 ms	5K
RAG	F	Too heavy	9.3 tok/s	37715 ms	5K

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 RTX 5050 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	1.4 GB	Very Low	A78
Q2_0_G128	1.71	2.5 GB	Low	A81
Q2_K	2

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 3060 12GBBudget pick

12 GB VRAM (+4)360 GB/s (+136)

Adds memory headroom for longer context windows and future model growth.28.4 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+8)448 GB/s (+224)

Raises estimated decode speed by about 48%.39.5 tok/s decode

Raises estimated decode speed by about 48%.

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

RTX 5070 12GBNVIDIA upgrade

12 GB VRAM (+4)672 GB/s (+448)

Raises estimated decode speed by about 125%.60.2 tok/s decode

Raises estimated decode speed by about 125%.

Adds memory headroom for longer context windows and future model growth.

~$549 MSRP

Frequently asked questions

Can RTX 5050 8GB run Ornith 1.0 9B?

Yes, RTX 5050 8GB can run Ornith 1.0 9B with a B grade (Very compromised). Expected decode speed: 13.7 tok/s.

How much VRAM does Ornith 1.0 9B need?

Ornith 1.0 9B (9.399999618530273B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Ornith 1.0 9B?

The recommended quantization for Ornith 1.0 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Ornith 1.0 9B run at on RTX 5050 8GB?

On RTX 5050 8GB, Ornith 1.0 9B achieves approximately 13.7 tokens per second decode speed with a time-to-first-token of 14106ms using Q4_K_M quantization.

Can RTX 5050 8GB run Ornith 1.0 9B for coding?

For coding workloads, Ornith 1.0 9B on RTX 5050 8GB receives a B grade with 13.7 tok/s and 5K context.

What context window can Ornith 1.0 9B use on RTX 5050 8GB?

On RTX 5050 8GB, Ornith 1.0 9B can safely use up to 5K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Ornith 1.0 9B feels slow on RTX 5050 8GB?

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.

See all results for RTX 5050 8GB See all hardware for Ornith 1.0 9B

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