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OrcaCyber Nano 1.0: how much memory, and what runs it

OrcaRouter's dense 27B vision-language model for defensive security work (reverse engineering, vulnerability analysis, secure code review, triage). Ships only as GGUF. The card says: "Loads in stock llama.cpp — no custom kernels". Text, image and video in, text out. Context 262,144. Hugging Face metadata says license: other, and the download is gated (accept the terms; auto-approval). No licence text is public. That is not Apache, MIT, or a Qwen licence. The card does not name the pretrained base. Do not read the Qwen3.5-shaped stack as a claim that this is a Qwen fine-tune. Architecture on the card: Qwen3_5ForConditionalGeneration, 64 layers, hidden size 5120, 48 linear-attention layers and 16 full-attention layers. KV heads (4) and head dim (256) are not on the card; the config is gated. Those two figures are assumed from the Qwen3.5-27B config, which has the same 64 layers, hidden size 5120 and full_attention_interval 4. KV dims assumed from Qwen3.5-27B architecture; OrcaCyber config is gated. Linear-attention layers are stored with a sliding window of 0 so this formula gives them no token KV. The KV figure uses only the 16 full-attention layers and is an estimate. Mix-15.7G is 15.70 GB and Mix-13G is 12.99 GB (decimal GB, Hugging Face file listing, 18 Oct 2026). About 4.65 and 3.85 bits per weight, estimated from 27B parameters. Vendor custom mixes, not Q4_K_M. The memory table does not include the vision projector or the drafter: +0.93 GB vision projector (mmproj F16) for image input; +2.06 GB optional DFlash2 drafter. The card says Mix-13G "fits a 16 GB card with the MTP drafter" and points Mix-15.7G at a "24 GB+ card". That is the vendor's guidance, not this site's fit score. An MTP head is built into both main files. Not in the Ollama library on 18 Oct 2026. Vendor-reported, unverified: AIME 2025 96.23% and MATH-500 98.60%. The card says the evaluation harness is being finalized.

OrcaCyber Nano 1.0 needs about 17.0 GB at Q4_K_M with 8K context (estimate). See which GPUs and Macs fit it, estimated tokens/sec, and EU prices.

QuantisationEstimated memory
Q417.0 GBestimate
Q520.5 GBestimate
Q830.2 GBestimate
FP1655.3 GBestimate

At 8,192 tokens of context. Catalog quant rates are a formula. sourceUrl on that memory stays null.

Parameter count source: huggingface.co/orcarouter/OrcaCyber-Nano-1.0. Marked estimated because the published count is rounded.

VRAM per quant and the cheapest rig for OrcaCyber Nano 1.0

Open in the calculator

Three setups

OrcaCyber Nano 1.0 · Q4 · 8,192 ctx

Estimated memory

17.0 GBestimate

15.7 GB weights · 0.5 GB KV cache · 0.8 GB overhead

  1. 01 · Fastest

    PC with RTX 5090 32GB

    Highest bandwidth in a single card. 32 GB is plenty for 32B, still too tight for 70B Q4.

    $8,283€7,399

    price as of 16 Oct 2026

    64 GB DDR4 (estimate): $672€600. From the 16 Oct 2026 fact-check.

    rest of system (estimate): $560€500. The total is the GPU price plus the RAM line plus this estimate. It is not a checked system price.

    price unverified

    GPU line is the new in-stock floor €6,299 from PriceSquirrel at Proshop (FI) (https://www.pricesquirrel.com/gpus/rtx-5090). 64 GB DDR4 (estimate) €600, from the 16 Oct 2026 fact-check. Rest of system (estimate) €500. Other parts from €500 in that fact-check. Not a shop listing. The parts list names 96 GB DDR5. That kit was not priced, so this total uses the sourced 64 GB DDR4 line. The total is the GPU price plus the RAM line plus that rest estimate. It is not a checked system price.

    tok/s estimate

    85

    estimate

    needed

    17.0 GB

    full fit

    usable

    31.4 GB

    VRAM

    Why this one

    • The model fits entirely in VRAM or unified memory. No CPU offload.
    • High memory bandwidth, so a higher tokens-per-second estimate.
    • Highest estimated speed in this shortlist.

    Parts

    • GeForce RTX 5090 32GB
    • 96 GB DDR5 is named here and was not priced.
    • 64 GB DDR4 (estimate): €600. from the 16 Oct 2026 fact-check.
    • Rest of system (estimate): €500. Other parts from €500 in that fact-check. Not a shop listing.
    • Ryzen 7 9700X or similar
    • 1200W PSU, roomy case

  2. 02 · Best value

    Used PC with RTX 3090 24GB

    Best single card here for 32B Q4. 70B Q4 does not fully fit.

    $2,602€2,324

    price as of 16 Oct 2026

    64 GB DDR4 (estimate): $672€600. From the 16 Oct 2026 fact-check.

    rest of system (estimate): $560€500. The total is the GPU price plus the RAM line plus this estimate. It is not a checked system price.

    price unverified

    GPU line is the used median €1,224, labelled used, from pcprice.watch (https://www.pcprice.watch/gpu-buying-guide/rtx-3090-price-used-and-specs). 64 GB DDR4 (estimate) €600, from the 16 Oct 2026 fact-check. Rest of system (estimate) €500. Other parts from €500 in that fact-check. Not a shop listing. The total is the GPU price plus the RAM line plus that rest estimate. It is not a checked system price.

    used

    tok/s estimate

    45

    estimate

    needed

    17.0 GB

    full fit

    usable

    23.4 GB

    VRAM

    Why this one

    • The model fits entirely in VRAM or unified memory. No CPU offload.
    • High memory bandwidth, so a higher tokens-per-second estimate.
    • Second-hand or a previous generation. The price moves around.
    • Most estimated tokens per euro in this shortlist.

    Parts

    • GeForce RTX 3090 24GB, used
    • 64 GB DDR4 (estimate): €600. from the 16 Oct 2026 fact-check.
    • Rest of system (estimate): €500. Other parts from €500 in that fact-check. Not a shop listing.
    • Ryzen 7 5700X or similar
    • 850W PSU, decent airflow

  3. 03 · Other platform

    Mac Studio M5 Ultra 96GB

    High bandwidth and enough memory for 70B Q4/Q5. FP16 70B does not fit.

    $7,444€6,649

    price as of 16 Oct 2026

    Belgian Apple Store

    Belgian Apple Store price for this config, from the 16 Oct 2026 fact-check.

    tok/s estimate

    35

    estimate

    needed

    17.0 GB

    full fit

    usable

    72.0 GB

    unified

    Why this one

    • The model fits entirely in VRAM or unified memory. No CPU offload.
    • Memory left for a longer context or a heavier quantisation.
    • Unified memory: weights and KV cache share one pool.
    • High memory bandwidth, so a higher tokens-per-second estimate.
    • One box, little noise, no discrete GPU.
    • A different brand, so you don't see the same PC three times.

    Parts

    • Mac Studio M5 Ultra, 96 GB, 1.2 TB/s

With CPU offload, and therefore slower, this could also work: Mid-range PC with RTX 4060 Ti 16GB, ~9.5 tok/s estimate, $2,238€1,999.

Tokens per second are estimated from memory bandwidth, not from a measured benchmark. Prices incl. VAT, excl. shipping. USD = FX estimate, excl. sales tax. A price without a checked listing is marked price unverified. Run llama-bench yourself before you buy.

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End of results

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[![OrcaCyber Nano 1.0 Q4 on RTX 3060 12GB: ✗ · needs 17.0 GB (estimate)](https://localllmrig.com/badge/orcacyber-nano-1.0/rtx-3060-12gb.svg)](https://localllmrig.com/can-i-run/orcacyber-nano-1.0/rtx-3060-12gb?utm_source=badge)

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Common questions

How much memory does OrcaCyber Nano 1.0 need at Q4?
About 17.0 GB at 8,192 tokens: 15.7 GB weights, 0.5 GB KV cache, 0.8 GB overhead. That is an estimate. sourceUrl stays null.
Does OrcaCyber Nano 1.0 fit on a single 24 GB GPU?
The estimate of 17.0 GB sits under the 23.4 GB we treat as usable on a 24 GB card. It is still an estimate, not a measured file.
Are the tokens per second measured?
No. Speed follows from bandwidth and an assumed utilisation. benchmarkSourceUrl stays null.