Qwen3.8-2.4T-A95B-FP8 GPU Requirements: VRAM & Cheapest GPU
Qwen3.8-2.4T-A95B-FP8 has about 2446B 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.
To run Qwen3.8-2.4T-A95B-FP8 for inference at FP16, you need roughly 5333 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.
Estimated peak VRAM including weights, activations, and KV cache. Add 10% headroom for production traffic.
This model needs multi-node training
Required VRAM exceeds 8× our largest single-node GPU. Talk to our team about a custom multi-node cluster.
VRAM required to run Qwen3.8-2.4T-A95B-FP8
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 | 5333 GB | 7999 GB | 21331 GB |
| INT8 | 2666 GB | 4000 GB | 10665 GB |
| INT4 | 1333 GB | 2000 GB | 5333 GB |
Cheapest GPU to run Qwen3.8-2.4T-A95B-FP8 by precision
Full precision. Best quality, highest memory.
8-bit quantized. ~2x smaller, minimal quality loss.
4-bit quantized. ~4x smaller, runs on smaller GPUs.
Inference vs fine-tuning Qwen3.8-2.4T-A95B-FP8
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.
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Qwen3.8-2.4T-A95B-FP8 GPU questions
Qwen3.8-2.4T-A95B-FP8 has about 2446B parameters. At FP16 it needs roughly 5333 GB of VRAM for inference, including weights, activations, and KV cache. Quantized to INT4 that drops to around 1333 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, 8x B300 288GB at about $72.16/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 Qwen3.8-2.4T-A95B-FP8. 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, Qwen3.8-2.4T-A95B-FP8 needs about 1333 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 5333 GB, which usually means a data center GPU. The precision picks above list the cheapest GPU that fits each setup.