Can Ornith 1.0 35B A3B run on RX 9070 XT 16GB?

YES — With Q2_K

A83Great

Estimated from fit model

Ornith 1.0 35B A3B needs ~16.5 GB VRAM. RX 9070 XT 16GB has 16.0 GB. With Q2_K quantization, expect ~54 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.

Ornith 1.0 35B A3B at Q4_K_M needs 24.2 GB — too much for RX 9070 XT 16GB (16.0 GB). Runs at Q2_K (16.5 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 24.2 GB, exceeds 16.0 GB available

24.2 GB required16.0 GB available

151% VRAM needed

8.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

18.5 tok/s

TTFT

10440 ms

Safe context

Memory

24.2 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights21.4 GB

KV Cache0.3 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsOrnith 1.0 35B A3B on RX 9070 XT 16GB
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: 18.5 tok/s decode · 10.4s TTFT (warm) · 46 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	F	Too heavy	18.8 tok/s	5622 ms	4K
Coding	F	Too heavy	18.5 tok/s	10440 ms	4K
Agentic Coding	F	Too heavy	18.1 tok/s	15579 ms	4K
Reasoning	F	Too heavy	18.5 tok/s	12339 ms	4K
RAG	F	Too heavy	18.1 tok/s	19474 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 RX 9070 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	F0
Q3_K_S	3	17.2 GB	Low	F0
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

Upgrade options

Hardware that runs Ornith 1.0 35B A3B well

RX 7900 XTX 24GBBudget pick

24 GB VRAM (+8)960 GB/s (+320)

Makes the model fit on the accelerator instead of staying completely out of reach.65.5 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.

~$999 MSRP

Radeon AI PRO R9700 32GBBest value

32 GB VRAM (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.52.1 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.

~$1,899 MSRP

Radeon Pro W6800 32GBAMD upgrade

32 GB VRAM (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.39.5 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.

~$2,249 MSRP

Frequently asked questions

Can RX 9070 XT 16GB run Ornith 1.0 35B A3B?

Yes, RX 9070 XT 16GB can run Ornith 1.0 35B A3B at Q2_K quantization (Runs with offload (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 24.2 GB which exceeds available memory, but at Q2_K it needs only 16.5 GB. Expected decode speed: 54.0 tok/s.

How much VRAM does Ornith 1.0 35B A3B need?

Ornith 1.0 35B A3B (35.099998474121094B parameters) requires approximately 24.2 GB at Q4_K_M quantization. On RX 9070 XT 16GB, it fits at Q2_K using 16.5 GB.

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

The recommended quantization is Q4_K_M, but on RX 9070 XT 16GB the best fitting quantization is Q2_K, which uses 16.5 GB.

What speed will Ornith 1.0 35B A3B run at on RX 9070 XT 16GB?

On RX 9070 XT 16GB, Ornith 1.0 35B A3B achieves approximately 54.0 tokens per second decode speed with a time-to-first-token of 3588ms using Q2_K quantization.

Can RX 9070 XT 16GB run Ornith 1.0 35B A3B for coding?

For coding workloads, Ornith 1.0 35B A3B on RX 9070 XT 16GB receives a F grade with 18.5 tok/s and 4K context.

What context window can Ornith 1.0 35B A3B use on RX 9070 XT 16GB?

On RX 9070 XT 16GB, Ornith 1.0 35B A3B can safely use up to 4K tokens of context at Q2_K quantization. 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 RX 9070 XT 16GB?

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 RX 9070 XT 16GB See all hardware for Ornith 1.0 35B A3B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/ornith-1.0-35b-a3b-on-rx-9070-xt-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

18.8 tok/s

5622 ms

Coding

Too heavy

18.5 tok/s

10440 ms

Agentic Coding

Too heavy

18.1 tok/s

15579 ms

Reasoning

Too heavy

18.5 tok/s

12339 ms

RAG

Too heavy

18.1 tok/s

19474 ms

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 RX 9070 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	F0
Q3_K_S	3	17.2 GB	Low	F0
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 RX 9070 XT 16GB run Ornith 1.0 35B A3B?

How much VRAM does Ornith 1.0 35B A3B need?

Ornith 1.0 35B A3B (35.099998474121094B parameters) requires approximately 24.2 GB at Q4_K_M quantization. On RX 9070 XT 16GB, it fits at Q2_K using 16.5 GB.

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

The recommended quantization is Q4_K_M, but on RX 9070 XT 16GB the best fitting quantization is Q2_K, which uses 16.5 GB.

What speed will Ornith 1.0 35B A3B run at on RX 9070 XT 16GB?

On RX 9070 XT 16GB, Ornith 1.0 35B A3B achieves approximately 54.0 tokens per second decode speed with a time-to-first-token of 3588ms using Q2_K quantization.

Can RX 9070 XT 16GB run Ornith 1.0 35B A3B for coding?

For coding workloads, Ornith 1.0 35B A3B on RX 9070 XT 16GB receives a F grade with 18.5 tok/s and 4K context.

What context window can Ornith 1.0 35B A3B use on RX 9070 XT 16GB?

On RX 9070 XT 16GB, Ornith 1.0 35B A3B can safely use up to 4K tokens of context at Q2_K quantization. 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 RX 9070 XT 16GB?

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