LongCat-2.5 GPU Requirements: VRAM & Cheapest GPU
LongCat-2.5 has about 1780B 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
- 1780B
- Min VRAM (INT4)
- 970 GB
- Cheapest (INT4)
- $37.96/hr
- Deploy
- < 2 min
To run LongCat-2.5 for inference at FP16, you need roughly 3880 GB of VRAM. That exceeds a single 8-GPU node, so it needs a multi-node cluster. Talk to our team for a custom configuration.
VRAM required
Estimated peak VRAM including weights, activations, and KV cache. Add 10% headroom for production traffic.
Scale
This model needs a multi-node cluster
It needs more VRAM than eight of our largest GPUs in one node can hold. Our team can size a multi-node cluster for it.
Self-host LongCat-2.5 or use managed inference
Rent the GPUs and run LongCat-2.5 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
- 3880 GB
- 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 LongCat-2.5
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 | 3880 GB | 5820 GB | 15519 GB |
| INT8 | 1940 GB | 2910 GB | 7760 GB |
| INT4 | 970 GB | 1455 GB | 3880 GB |
Cheapest GPU to run LongCat-2.5 by precision
3880GB
Full precision. Best quality, highest memory.
1940GB
8-bit quantized. ~2x smaller, minimal quality loss.
970GB
4-bit quantized. ~4x smaller, runs on smaller GPUs.
Inference vs fine-tuning LongCat-2.5
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. A model this size needs multiple GPUs even for inference, and full fine-tuning multiplies that again. Browse the GPU catalog to size a node. Check the live GPU pricing for current rates.
Similar models
Compare GPU requirements for models in the same class.
LongCat-2.5 GPU questions
VRAM, GPU choice and cost questions for running LongCat-2.5 on cloud GPUs.
LongCat-2.5 has about 1780B parameters. At FP16 it needs roughly 3880 GB of VRAM for inference, including weights, activations, and KV cache. Quantized to INT4 that drops to around 970 GB. Leave about 10% headroom for production traffic.
For FP16 inference, the cheapest fit on Spheron is a multi-node GPU cluster. If you quantize to INT4, 4x B300 288GB at about $37.96/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 LongCat-2.5. The VRAM matrix above shows the exact estimate for each setup.
We read the parameter count directly from the model's safetensors 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, LongCat-2.5 needs about 970 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 3880 GB, which usually means a data center GPU. The precision picks above list the cheapest GPU that fits each setup.
No. Even quantized to INT4, LongCat-2.5 needs about 970 GB of VRAM, so it takes 13 x 80 GB GPUs at minimum, and 49 x 80 GB GPUs at FP16. The precision picks above show the cheapest GPU that fits each setup on Spheron.
LongCat-2.5 needs 49 x 80 GB GPUs at FP16 (about 3880 GB of VRAM), 25 x 80 GB GPUs at INT8 (1940 GB) and 13 x 80 GB GPUs at INT4 (970 GB). At FP16 that is more than one 8-GPU node, so plan for a multi-node cluster with a fast interconnect. Smaller cards such as 48 GB or 24 GB GPUs need proportionally more of them.
LongCat-2.5 needs about 1940 GB of VRAM at INT8, roughly half its FP16 footprint of 3880 GB. The cheapest INT8 fit on Spheron is 8x B300 288GB at about $75.92/hr. INT8 is the usual middle ground: half the memory of FP16, with a smaller quality drop than 4-bit quantization.
At FP16 LongCat-2.5 needs a multi-node GPU cluster, so a monthly figure depends on the cluster you reserve. Quantized to INT4 it runs on a single node for about $27,711 a month on 4x B300 288GB (730 hours at $37.96/hr). Talk to the Spheron team for reserved cluster pricing.
LongCat-2.5 needs about 3880 GB at FP16, 1940 GB at INT8 and 970 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 4x B300 288GB at about $37.96/hr.