Can I run Ornith-1.5-122B on an RX 9060 XT 16GB?
Key facts
No: it needs about 77 GB and this card has 16 GB of graphics memory.
It needs about 76.8 GB at standard quality (estimate). Checked . Q4_K_M GGUF (official).
Updated · Ornith-1.5-122B on every card
How much memory it needs
That is about 5.0 times what this card has free.
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What runs Ornith-1.5-122B instead
The cheapest machine on our list that holds the whole model in its graphics memory:
Mac Studio M4 Max 128GBabout $3,683€3,290
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We never pass off an estimate as a benchmark. Commissions never change which hardware we recommend. How we calculate
Price details
$3,683€3,290
How we priced this
Not checked against a shop listing (price unverified). Price from 16 Oct 2026.
Used-market euro amount from an earlier check of ours. Used prices cannot be checked on the Apple Store.
Prices excl. shipping unless marked. Euro prices incl. VAT where the seller charges it. Dollar prices are converted from our euro prices without the 21% EU VAT, before US sales tax.
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For comparison, an RX 9060 XT 16GB costs $522€564, new (cheapest in-stock board on GPU PRIX's German board list). Card details
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Ornith-1.5-122B on other graphics cards
Runs slowly2
Part of the model spills into normal memory, which is much slower.
Doesn't fit8
Not enough graphics memory: it needs about 76.8 GB.
- RTX 3060 12GBhas 12 GB
- RTX 4060 Ti 16GBhas 16 GB
- RTX 3090has 24 GB
- RTX 4090has 24 GB
- RTX 5080has 16 GB
- RX 9060 XT 16GBhas 16 GB
- RTX 5090has 32 GB
- RX 7900 XTXhas 24 GB
More about the model itself, what it is good at and its licence: Ornith-1.5-122B.
The numbers behind this answer
A model comes in several sizes. Q4 is the standard one most people use; the bigger ones answer a little better but need more memory. Each row adds up the model file, the memory a conversation takes, and some working space, and compares the total with what is free on this card.
Swipe the table sideways to see every column.
| Quality | Conversation | Result | Model file | Conversation memory | Working space | Total needed | Free memory |
|---|---|---|---|---|---|---|---|
| Q4 · standard (smallest that keeps quality) | ~12 pages8,192 tokens | Doesn't fit | 75.8 GBmeasured file | 0.2 GB | 0.8 GB | 76.8 GB | 15.4 GB |
| Q5 · a bit sharper, a bit bigger | ~12 pages8,192 tokens | Doesn't fit | 88.8 GB | 0.2 GB | 0.8 GB | 89.8 GB | 15.4 GB |
| Q8 · near full quality, almost twice Q4 | ~12 pages8,192 tokens | Doesn't fit | 133.8 GB | 0.2 GB | 0.8 GB | 134.8 GB | 15.4 GB |
| FP16 · full quality, over three times Q4 | ~12 pages8,192 tokens | Doesn't fit | 250.2 GB | 0.2 GB | 0.8 GB | 251.2 GB | 15.4 GB |
| Q4 · standard, longer conversation | ~49 pages32,768 tokens | Doesn't fit | 75.8 GBmeasured file | 0.8 GB | 0.8 GB | 77.4 GB | 15.4 GB |
speed: not estimated for MoE yet. This model only uses part of itself for each word (a mixture of experts), and our speed estimate does not handle that yet.
Sources and method
Memory and speed on this page are our estimate, not a measured test. Checked . Q4_K_M GGUF (official).
Ornith's open-weight mixture-of-experts vision-language model, MIT (card frontmatter license: mit; LICENSE at https://huggingface.co/ornith-ai/Ornith-1.5-122B/blob/main/LICENSE). The download is not gated. The card says "122B MOE". Hugging Face safetensors metadata lists 125,086,497,008 parameters, all BF16. 125.09B is that count to two decimals, so we use 125.09 as a counted figure, not an estimate. The card does not state how many parameters are active. Community posts that say about 10B active are unverified and are not stored. Config: Qwen3_5MoeForConditionalGeneration, model_type qwen3_5_moe. 48 layers, 12 full_attention and 36 linear_attention (full_attention_interval 4), so the full-attention share is 12/48. Hidden size 3072. 32 attention heads, 2 KV heads, head dim 256, vocab 248,320. 256 experts, 8 routed per token, moe intermediate size 1024, shared expert intermediate size 1024. Linear-attention layers are stored with a sliding window of 0 so our estimate gives them no conversation memory. The conversation-memory figure uses only the 12 full-attention layers and is an FP16 estimate. Config max_position_embeddings is 262,144. The card says YaRN with a scaling factor of 4 grows that window to roughly 1M tokens; we use 262,144. Text, image and video in, text out. The card's example reads a <think> trace from reasoning_content. Public launch 20 Oct 2026: https://x.com/ornith_/status/2112553477457268799. Hugging Face lists the repo as created on 16 Oct 2026. Speed is not estimated: our speed estimate reads every weight for each token and would understate a sparse MoE, so we show no speed figure. A separate community measurement is stored on this row. It is not a fit score and not a quant comparison. Official GGUF sizes, decimal GB, Hugging Face tree checked 24 Oct 2026, mmproj excluded (https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF): Q4_K_M 75.84 (75,843,911,648 bytes), Q5_K_M 88.71, Q6_K 102.38, Q8_0 132.56. Unsloth GGUF, shards summed the same day (https://huggingface.co/unsloth/Ornith-1.5-122B-GGUF): UD-IQ1_M 40.36, UD-IQ2_XXS 41.97, UD-Q2_K_XL 44.53, UD-IQ3_XXS 49.52, UD-Q3_K_XL 59.76, UD-Q4_K_XL 79.71, UD-Q5_K_XL 94.61, UD-Q6_K_XL 114.22. The bits column is the quant name's nominal width, not a measured bits-per-weight. Official BF16 GGUF is 249.36 GB (249,362,980,832 bytes) and is not a memory-table row. mmproj-Ornith-1.5-122B-BF16.gguf is 0.91 GB (912,263,200 bytes) and is not in the table. Community mixed quants, not rows in this table, checked 24 Oct 2026: AtomicChat AD-IQ2_XXS-IQ1_M 34.96 GB and AD-IQ3_XXS-IQ2_S 47.64 GB (https://huggingface.co/AtomicChat/Ornith-1.5-122B-GGUF). Ollama, checked 24 Oct 2026 (https://ollama.com/library/ornith-1.5/tags): ornith-1.5:122b is listed as 73GB–77GB, ornith-1.5:122b-nvfp4 as 73GB, and ornith-1.5:122b-q4_K_M as 77GB.
Measured by others
Someone else ran Ornith-1.5-122B and published the speed (community measurement, single post, not run by us). They may have used a smaller or bigger version than the standard size above, so their result can differ from our answer: "How they measured" has the details.
- one DGX Spark, official Q6_K, llama.cpp with built-in MTP drafter, 262K context, code
- one DGX Spark, official Q6_K, llama.cpp with built-in MTP drafter, 262K context, math
How they measured
Community measurement, single post, not run by us. Official Q6_K on one DGX Spark (the post says 95 GiB; this catalog stores that official Q6_K file as 102.38 GB). llama.cpp with the checkpoint's built-in MTP drafter, 262K context: code 29.2 tok/s, math 30.8 tok/s, 8K prefill 1,092 tok/s. Not a quant comparison and not a fit score.
Questions
- Can I run Ornith-1.5-122B on an RX 9060 XT 16GB?
- No: it needs about 77 GB and this card has 16 GB of graphics memory.
- How much memory does Ornith-1.5-122B need?
- About 76.8 GB at the standard quality (Q4) for a conversation of up to about 12 pages of text (8,192 tokens), by our estimate: 75.8 GB for the model file (measured), 0.2 GB to hold the conversation and 0.8 GB of working space.
- How fast is Ornith-1.5-122B on an RX 9060 XT 16GB?
- We don't give a speed for this model yet. This model only uses part of itself for each word (a mixture of experts), and our speed estimate does not handle that yet.
Speeds and memory are our estimate from the hardware specs, not a measured test. Prices are dated and may change. How we calculate