Can Ornith 1.0 9B run on Intel Arc A580 8GB?

YES — With Offload

A79Great

Estimated from fit model

Ornith 1.0 9B needs ~7.9 GB VRAM. Intel Arc A580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~36 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: 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) — 7.9 GB, 35.6 tok/s, Runs with offload

7.9 GB required8.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

35.6 tok/s

TTFT

5431 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 Intel Arc A580 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: 35.6 tok/s decode · 5.4s TTFT (warm) · 89 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload	35.6 tok/s	2963 ms	19K
Coding	A	Runs with offload	35.6 tok/s	5431 ms	19K
Agentic Coding	A	Runs with offload (needs ~0.3 GB host RAM)	24.1 tok/s	11704 ms	19K
Reasoning	A	Runs with offload	35.6 tok/s	6419 ms	19K
RAG	A	Runs with offload (needs ~0.3 GB host RAM)	24.1 tok/s	14629 ms	19K

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	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	114.5	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	84.9	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	79.1	Fits
RTX 4070 12GB	12 GB	Q4_K_M	70.9	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	70.3	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	65.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	62.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	53.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	45.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	44.5	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	41.2	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	36.3	Fits
RTX 4060 8GB	8 GB	Q4_K_M	26.4	Offloads

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Ornith 1.0 9B (9.399999618530273B params) fits at each quantization level on Intel Arc A580 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	3.7 GB	Low	A81
Q3_K_S	3	4.6 GB	Low	A81
NVFP4Best for your GPU	4	5.3 GB	Medium	A81
Q4_K_M	4	5.7 GB	Medium	F0
Q5_K_M	5	6.8 GB	High	F0
Q6_K	6	7.7 GB	High	F0
Q8_0	8	10.1 GB	Very High	F0
F16	16	19.3 GB	Maximum	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

Your hardware

More models your Intel Arc A580 8GB can run

Model	Params	Grade	Decode	Capabilities
1-bit Bonsai 27B	27B	S	55.6 tok/s

Frequently asked questions

Can Intel Arc A580 8GB run Ornith 1.0 9B?

Yes, Intel Arc A580 8GB can run Ornith 1.0 9B with a A grade (Runs with offload). Expected decode speed: 35.6 tok/s.

How much VRAM does Ornith 1.0 9B need?

Ornith 1.0 9B (9.399999618530273B parameters) requires approximately 7.9 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 Intel Arc A580 8GB?

On Intel Arc A580 8GB, Ornith 1.0 9B achieves approximately 35.6 tokens per second decode speed with a time-to-first-token of 5431ms using Q4_K_M quantization.

Can Intel Arc A580 8GB run Ornith 1.0 9B for coding?

For coding workloads, Ornith 1.0 9B on Intel Arc A580 8GB receives a A grade with 35.6 tok/s and 19K context.

What context window can Ornith 1.0 9B use on Intel Arc A580 8GB?

On Intel Arc A580 8GB, Ornith 1.0 9B can safely use up to 19K 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 Intel Arc A580 8GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Would CUDA be a better path than Intel Arc A580 8GB for Ornith 1.0 9B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Arc A580 8GB See all hardware for Ornith 1.0 9B

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