Ornith-1.5-122B-GGUF GPU Requirements: VRAM & Cheapest GPU
Ornith-1.5-122B-GGUF has about 125B parameters. See exactly how much GPU memory it needs at FP16, INT8, and INT4, and the cheapest GPU to run it, with live hourly pricing from 5+ data center partners.
- Parameters
- 125B
- Min VRAM (INT4)
- 68 GB
- Cheapest (INT4)
- $1.19/hr
- Deploy
- < 2 min
To run Ornith-1.5-122B-GGUF for inference at FP16, you need roughly 272 GB of VRAM. The cheapest fit on Spheron is 4x A100 80GB from $4.76/hr. Quantize to INT4 to run it on a smaller, cheaper GPU.
VRAM required
Estimated peak VRAM including weights, activations, and KV cache. Add 10% headroom for production traffic.
- 01$1.43/hr$5.72/hr4× A100 80GBCHEAPESTAmpere · HBM2e
- 02$0.78/hr$6.24/hr8× RTX 6000 Ada 48GBAda Lovelace · GDDR6 ECC
- 03$3.59/hr$7.18/hr2× MI300X 192GBAMD CDNA 3 · HBM3 · ROCm, check kernel support
- 04$0.96/hr$7.68/hr8× L40S 48GBAda Lovelace · GDDR6
- 05$2.35/hr$9.40/hr4× RTX PRO 6000 96GBBlackwell · GDDR7
Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.
Self-host Ornith-1.5-122B-GGUF or use managed inference
Rent the GPUs and run Ornith-1.5-122B-GGUF yourself, or skip the hardware with managed LLM inference: an OpenAI compatible API on open models, or dedicated inference endpoints for your own fine tuned model.
Run it yourself on GPUs
- VRAM at FP16
- 272 GB
- Cheapest fit
- 4x A100 80GB from $4.76/hr
- Full control of weights, serving stack and batch size
- Per-minute billing, stop when you are done
- You size, scale and keep the GPUs up
Use a managed inference API
- Endpoint
- OpenAI compatible
- Pricing
- A fixed monthly token plan
- No GPUs to size, run or scale
- Requests at once held for you, uptime SLA in the contract
- Dedicated endpoints for your own fine tuned model
VRAM required to run Ornith-1.5-122B-GGUF
Estimated peak VRAM at context length 4,096 and batch size 1, including weights, activations, and KV cache. Quantizing to INT8 (Q8) or INT4 (Q4) cuts memory roughly in half and in quarter.
| Precision | Inference | LoRA fine-tune | Full fine-tune |
|---|---|---|---|
| FP16 | 272 GB | 408 GB | 1087 GB |
| INT8 | 136 GB | 204 GB | 543 GB |
| INT4 | 68 GB | 102 GB | 272 GB |
Cheapest GPU to run Ornith-1.5-122B-GGUF by precision
272GB
Full precision. Best quality, highest memory.
136GB
8-bit quantized. ~2x smaller, minimal quality loss.
68GB
4-bit quantized. ~4x smaller, runs on smaller GPUs.
Inference vs fine-tuning Ornith-1.5-122B-GGUF
Inference only holds the model weights plus a KV cache, so it is the cheapest setup. LoRA fine-tuning adds a small adapter and roughly 50% more memory. Full fine-tuning holds gradients and optimizer state on top of the weights, which is about 4x the inference footprint, so it often needs multiple GPUs even when inference fits on one. For Ornith-1.5-122B-GGUF, an on-demand A100 80GB instance covers inference and LoRA, while a full fine-tune needs several times that memory and often spans multiple GPUs. Check the live GPU pricing for current rates.
Similar models
Compare GPU requirements for models in the same class.
Ornith-1.5-122B-GGUF GPU questions
VRAM, GPU choice and cost questions for running Ornith-1.5-122B-GGUF on cloud GPUs.
Ornith-1.5-122B-GGUF has about 125B parameters. At FP16 it needs roughly 272 GB of VRAM for inference, including weights, activations, and KV cache. Quantized to INT4 that drops to around 68 GB. Leave about 10% headroom for production traffic.
For FP16 inference, the cheapest fit on Spheron is 4x A100 80GB from $4.76/hr. If you quantize to INT4, A100 80GB from $1.19/hr runs it for less. Pricing is aggregated live from 5+ data center partners with per-minute billing.
LoRA fine-tuning adds roughly 50% on top of inference memory, so it usually fits the same class of GPU. Full fine-tuning holds gradients and optimizer state and needs about 4x the inference VRAM, which often means multiple GPUs for Ornith-1.5-122B-GGUF. The VRAM matrix above shows the exact estimate for each setup.
We read the parameter count from the model's GGUF metadata on HuggingFace, then estimate peak VRAM from weights, activations, KV cache, and framework overhead at your chosen precision. The estimate lands within about 15% of real-world use for most transformer models.
Quantized to INT4, Ornith-1.5-122B-GGUF needs about 68 GB of VRAM, so it is too large for a single 24 GB RTX 4090 and needs a bigger card or multiple GPUs. At FP16 it needs roughly 272 GB, which usually means a data center GPU. The precision picks above list the cheapest GPU that fits each setup.
Only at INT4. Ornith-1.5-122B-GGUF needs about 272 GB at FP16 and 136 GB at INT8, both more than one 80 GB H100 holds. Quantized to INT4 it needs about 68 GB and fits one H100. The precision picks above show the cheapest GPU that fits each setup on Spheron.
Ornith-1.5-122B-GGUF needs 4 x 80 GB GPUs at FP16 (about 272 GB of VRAM), 2 x 80 GB GPUs at INT8 (136 GB) and one 80 GB GPU at INT4 (68 GB). All of these fit inside a single 8-GPU node, so no multi-node setup is needed for inference. Smaller cards such as 48 GB or 24 GB GPUs need proportionally more of them.
Ornith-1.5-122B-GGUF needs about 136 GB of VRAM at INT8, roughly half its FP16 footprint of 272 GB. The cheapest INT8 fit on Spheron is 2x A100 80GB from $2.38/hr. INT8 is the usual middle ground: half the memory of FP16, with a smaller quality drop than 4-bit quantization.
Running Ornith-1.5-122B-GGUF around the clock at FP16 costs about $3,475 a month on 4x A100 80GB (730 hours at $4.76/hr at the spot rate, which can be reclaimed). Quantized to INT4 it costs about $869 a month on A100 80GB (730 hours at $1.19/hr at the spot rate, which can be reclaimed). Spheron bills per minute, so stopping the instance when it is idle cuts that figure directly.
Ornith-1.5-122B-GGUF needs about 272 GB at FP16, 136 GB at INT8 and 68 GB at INT4. Use FP16 when output quality matters most or you plan to fine-tune, INT8 for most production inference, and INT4 when cost or GPU size is the constraint: the cheapest INT4 fit is A100 80GB from $1.19/hr.