ERNIE-5.5-Flash-Base GPU Requirements: VRAM & Cheapest GPU
ERNIE-5.5-Flash-Base has about 286B 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
- 286B
- Min VRAM (INT4)
- 156 GB
- Cheapest (INT4)
- $2.38/hr
- Deploy
- < 2 min
To run ERNIE-5.5-Flash-Base for inference at FP16, you need roughly 624 GB of VRAM. The cheapest fit on Spheron is 8x A100 80GB from $9.52/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$11.44/hr8× A100 80GBCHEAPESTAmpere · HBM2e
- 02$2.39/hr$19.12/hr8× RTX PRO 6000 96GBBlackwell · GDDR7
- 03$2.64/hr$21.12/hr8× H100 80GBHopper · HBM3
- 04$3.02/hr$24.16/hr8× GH200 96GBGrace Hopper · HBM3
- 05$4.80/hr$38.40/hr8× H200 141GBHopper · HBM3e
Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.
Self-host ERNIE-5.5-Flash-Base or use managed inference
Rent the GPUs and run ERNIE-5.5-Flash-Base 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
- 624 GB
- Cheapest fit
- 8x A100 80GB from $9.52/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 ERNIE-5.5-Flash-Base
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 | 624 GB | 936 GB | 2496 GB |
| INT8 | 312 GB | 468 GB | 1248 GB |
| INT4 | 156 GB | 234 GB | 624 GB |
Cheapest GPU to run ERNIE-5.5-Flash-Base by precision
624GB
Full precision. Best quality, highest memory.
312GB
8-bit quantized. ~2x smaller, minimal quality loss.
156GB
4-bit quantized. ~4x smaller, runs on smaller GPUs.
Inference vs fine-tuning ERNIE-5.5-Flash-Base
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 ERNIE-5.5-Flash-Base, 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.
ERNIE-5.5-Flash-Base GPU questions
VRAM, GPU choice and cost questions for running ERNIE-5.5-Flash-Base on cloud GPUs.
ERNIE-5.5-Flash-Base has about 286B parameters. At FP16 it needs roughly 624 GB of VRAM for inference, including weights, activations, and KV cache. Quantized to INT4 that drops to around 156 GB. Leave about 10% headroom for production traffic.
For FP16 inference, the cheapest fit on Spheron is 8x A100 80GB from $9.52/hr. If you quantize to INT4, 2x A100 80GB from $2.38/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 ERNIE-5.5-Flash-Base. 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, ERNIE-5.5-Flash-Base needs about 156 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 624 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, ERNIE-5.5-Flash-Base needs about 156 GB of VRAM, so it takes 2 x 80 GB GPUs at minimum, and 8 x 80 GB GPUs at FP16. The precision picks above show the cheapest GPU that fits each setup on Spheron.
ERNIE-5.5-Flash-Base needs 8 x 80 GB GPUs at FP16 (about 624 GB of VRAM), 4 x 80 GB GPUs at INT8 (312 GB) and 2 x 80 GB GPUs at INT4 (156 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.
ERNIE-5.5-Flash-Base needs about 312 GB of VRAM at INT8, roughly half its FP16 footprint of 624 GB. The cheapest INT8 fit on Spheron is 4x A100 80GB from $4.76/hr. INT8 is the usual middle ground: half the memory of FP16, with a smaller quality drop than 4-bit quantization.
Running ERNIE-5.5-Flash-Base around the clock at FP16 costs about $6,950 a month on 8x A100 80GB (730 hours at $9.52/hr at the spot rate, which can be reclaimed). Quantized to INT4 it costs about $1,737 a month on 2x A100 80GB (730 hours at $2.38/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.
ERNIE-5.5-Flash-Base needs about 624 GB at FP16, 312 GB at INT8 and 156 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 2x A100 80GB from $2.38/hr.