Can Ornith 1.0 35B A3B run on RTX A5000 24GB?

YES — With Offload

A83Great

Estimated from fit model

Ornith 1.0 35B A3B needs ~25.0 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~51 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) — 25.0 GB, 50.9 tok/s, Runs with offload (needs ~0.9 GB host RAM)

25.0 GB required24.0 GB available

104% VRAM needed

1.0 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.9 GB host RAM)

Decode

50.9 tok/s

TTFT

3800 ms

Safe context

Memory

25.0 GB / 24.0 GB

Memory breakdown

Weights21.4 GB

KV Cache0.3 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsOrnith 1.0 35B A3B on RTX A5000 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: 50.9 tok/s decode · 3.8s TTFT (warm) · 127 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

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 (needs ~0.7 GB host RAM)	51.6 tok/s	2046 ms	4K
Coding	A	Runs with offload (needs ~0.9 GB host RAM)	50.9 tok/s	3800 ms	4K
Agentic Coding	A	Runs with offload (needs ~1.1 GB host RAM)	49.7 tok/s	5670 ms	4K
Reasoning	A	Runs with offload (needs ~0.9 GB host RAM)	50.9 tok/s	4491 ms	4K
RAG	A	Runs with offload (needs ~1.1 GB host RAM)	49.7 tok/s	7088 ms	4K

Inference speed

Ornith 1.0 35B A3B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Ornith 1.0 35B A3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~139 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	139.1	Fits
RTX 4090 24GB	24 GB	Q4_K_M	77.7	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	76.8	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	65.5	Offloads
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	64.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	62.1	Offloads
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	60.7	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	47.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	47.4	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	36.4	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	33.4	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	31.9	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	28.3	Too big
RTX 4070 12GB	12 GB	Q4_K_M	9.9	Too big
RTX 3060 12GB	12 GB	Q4_K_M	5.8	Too big
RTX 4060 8GB	8 GB	Q4_K_M	4.4	Too big

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 35B A3B (35.099998474121094B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	A80
Q3_K_SBest for your GPU	3	17.2 GB	Low	A80
NVFP4	4	19.7 GB	Medium	F0
Q4_K_M	4	21.4 GB	Medium	F0
Q5_K_M	5	25.3 GB	High	F0
Q6_K	6	28.8 GB	High	F0
Q8_0	8	37.6 GB	Very High	F0
F16	16	72.0 GB	Maximum	F0

Get started

Copy-paste commands to run Ornith 1.0 35B A3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "deepreinforce-ai/Ornith-1.0-35B" \
  --hf-file "Ornith-1.0-35B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can RTX A5000 24GB run Ornith 1.0 35B A3B?

Yes, RTX A5000 24GB can run Ornith 1.0 35B A3B with a A grade (Runs with offload (needs ~0.9 GB host RAM)). Expected decode speed: 50.9 tok/s.

How much VRAM does Ornith 1.0 35B A3B need?

Ornith 1.0 35B A3B (35.099998474121094B parameters) requires approximately 25.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Ornith 1.0 35B A3B?

The recommended quantization for Ornith 1.0 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Ornith 1.0 35B A3B run at on RTX A5000 24GB?

On RTX A5000 24GB, Ornith 1.0 35B A3B achieves approximately 50.9 tokens per second decode speed with a time-to-first-token of 3800ms using Q4_K_M quantization.

Can RTX A5000 24GB run Ornith 1.0 35B A3B for coding?

For coding workloads, Ornith 1.0 35B A3B on RTX A5000 24GB receives a A grade with 50.9 tok/s and 4K context.

What context window can Ornith 1.0 35B A3B use on RTX A5000 24GB?

On RTX A5000 24GB, Ornith 1.0 35B A3B can safely use up to 4K 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 35B A3B feels slow on RTX A5000 24GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX A5000 24GB See all hardware for Ornith 1.0 35B A3B

Embed this result▼

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Preview:

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload (needs ~0.7 GB host RAM)	51.6 tok/s	2046 ms	4K
Coding	A	Runs with offload (needs ~0.9 GB host RAM)	50.9 tok/s	3800 ms	4K
Agentic Coding	A	Runs with offload (needs ~1.1 GB host RAM)	49.7 tok/s	5670 ms	4K
Reasoning	A	Runs with offload (needs ~0.9 GB host RAM)	50.9 tok/s	4491 ms	4K
RAG	A	Runs with offload (needs ~1.1 GB host RAM)	49.7 tok/s	7088 ms	4K

Ornith 1.0 35B A3B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

139.1

Fits

RTX 4090 24GB

24 GB

Q4_K_M

77.7

Offloads

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

76.8

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

65.5

Offloads

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

64.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

62.1

Offloads

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

60.7

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

47.4

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

47.4

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

36.4

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

33.4

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

31.9

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

28.3

Too big

RTX 4070 12GB

12 GB

Q4_K_M

9.9

Too big

RTX 3060 12GB

12 GB

Q4_K_M

5.8

Too big

RTX 4060 8GB

8 GB

Q4_K_M

4.4

Too big

Quantization options

How Ornith 1.0 35B A3B (35.099998474121094B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	A80
Q3_K_SBest for your GPU	3	17.2 GB	Low	A80
NVFP4	4	19.7 GB	Medium	F0
Q4_K_M	4	21.4 GB	Medium	F0
Q5_K_M	5	25.3 GB	High	F0
Q6_K	6	28.8 GB	High	F0
Q8_0	8	37.6 GB	Very High	F0
F16	16	72.0 GB	Maximum	F0

Frequently asked questions

Can RTX A5000 24GB run Ornith 1.0 35B A3B?

Yes, RTX A5000 24GB can run Ornith 1.0 35B A3B with a A grade (Runs with offload (needs ~0.9 GB host RAM)). Expected decode speed: 50.9 tok/s.

How much VRAM does Ornith 1.0 35B A3B need?

Ornith 1.0 35B A3B (35.099998474121094B parameters) requires approximately 25.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Ornith 1.0 35B A3B?

The recommended quantization for Ornith 1.0 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Ornith 1.0 35B A3B run at on RTX A5000 24GB?

On RTX A5000 24GB, Ornith 1.0 35B A3B achieves approximately 50.9 tokens per second decode speed with a time-to-first-token of 3800ms using Q4_K_M quantization.

Can RTX A5000 24GB run Ornith 1.0 35B A3B for coding?

For coding workloads, Ornith 1.0 35B A3B on RTX A5000 24GB receives a A grade with 50.9 tok/s and 4K context.

What context window can Ornith 1.0 35B A3B use on RTX A5000 24GB?

On RTX A5000 24GB, Ornith 1.0 35B A3B can safely use up to 4K 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 35B A3B feels slow on RTX A5000 24GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.