Can Ornith 1.0 35B A3B run on RTX 4070 Ti Super 16GB?

YES — With Q2_K

A84Great

Estimated — low-sample bucket· few comparable runs

Ornith 1.0 35B A3B needs ~16.5 GB VRAM. RTX 4070 Ti Super 16GB has 16.0 GB. With Q2_K quantization, expect ~74 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 RTX 4070 Ti Super 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

24.9 tok/s

TTFT

7787 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 RTX 4070 Ti Super 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: 24.9 tok/s decode · 7.8s TTFT (warm) · 62 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	25.2 tok/s	4191 ms	4K
Coding	F	Too heavy	24.9 tok/s	7787 ms	4K
Agentic Coding	F	Too heavy	24.2 tok/s	11629 ms	4K
Reasoning	F	Too heavy	24.9 tok/s	9203 ms	4K
RAG	F	Too heavy	24.2 tok/s	14536 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 4070 Ti Super 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

アップグレードオプション

Ornith 1.0 35B A3Bを快適に動かすハードウェア

RTX 3090 24GBコスパ重視

24 GB VRAM (+8)936 GB/s (+264)

Makes the model fit on the accelerator instead of staying completely out of reach.62.1 tok/sデコード

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.

〜$1,499 MSRP

RTX 4090 24GBコスパ最良

24 GB VRAM (+8)1008 GB/s (+336)

Makes the model fit on the accelerator instead of staying completely out of reach.77.7 tok/sデコード

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.

〜$1,599 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIAアップグレード

24 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.54.7 tok/sデコード

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.

〜$1,599 MSRP

Frequently asked questions

Can RTX 4070 Ti Super 16GB run Ornith 1.0 35B A3B?

Yes, RTX 4070 Ti Super 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: 74.2 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 RTX 4070 Ti Super 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 RTX 4070 Ti Super 16GB the best fitting quantization is Q2_K, which uses 16.5 GB.

What speed will Ornith 1.0 35B A3B run at on RTX 4070 Ti Super 16GB?

On RTX 4070 Ti Super 16GB, Ornith 1.0 35B A3B achieves approximately 74.2 tokens per second decode speed with a time-to-first-token of 2610ms using Q2_K quantization.

Can RTX 4070 Ti Super 16GB run Ornith 1.0 35B A3B for coding?

For coding workloads, Ornith 1.0 35B A3B on RTX 4070 Ti Super 16GB receives a F grade with 24.9 tok/s and 4K context.

What context window can Ornith 1.0 35B A3B use on RTX 4070 Ti Super 16GB?

On RTX 4070 Ti Super 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 RTX 4070 Ti Super 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 RTX 4070 Ti Super 16GB See all hardware for Ornith 1.0 35B A3B

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