Qwen3.8-2.4T-A95B GPU Requirements: VRAM & Cheapest GPU
Qwen3.8-2.4T-A95B 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.
- Parameters
- 2446B
- Min VRAM (INT4)
- 1333 GB
- Cheapest (INT4)
- $59.76/hr
- Deploy
- < 2 min
To run Qwen3.8-2.4T-A95B 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.
VRAM required
Estimated peak VRAM including weights, activations, and KV cache. Add 10% headroom for production traffic.
This model needs a multi-node cluster
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
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 by precision
5333GB
Full precision. Best quality, highest memory.
2666GB
8-bit quantized. ~2x smaller, minimal quality loss.
1333GB
4-bit quantized. ~4x smaller, runs on smaller GPUs.
Inference vs fine-tuning Qwen3.8-2.4T-A95B
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.
Qwen3.8-2.4T-A95B GPU questions
VRAM, GPU choice and cost questions for running Qwen3.8-2.4T-A95B on cloud GPUs.
Qwen3.8-2.4T-A95B 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 AI is a multi-node GPU cluster. If you quantize to INT4, 8x B300 288GB at about $59.76/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. 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 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.
No. Even quantized to INT4, Qwen3.8-2.4T-A95B needs about 1333 GB of VRAM, so it takes 17 x 80 GB GPUs at minimum, and 67 x 80 GB GPUs at FP16. The precision picks above show the cheapest GPU that fits each setup on Spheron AI.
Qwen3.8-2.4T-A95B needs 67 x 80 GB GPUs at FP16 (about 5333 GB of VRAM), 34 x 80 GB GPUs at INT8 (2666 GB) and 17 x 80 GB GPUs at INT4 (1333 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.
Qwen3.8-2.4T-A95B needs about 2666 GB of VRAM at INT8, roughly half its FP16 footprint of 5333 GB. No single 8-GPU node holds that, so INT8 needs a multi-node cluster. INT8 is the usual middle ground: half the memory of FP16, with a smaller quality drop than 4-bit quantization.
At FP16 Qwen3.8-2.4T-A95B 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 $43,625 a month on 8x B300 288GB (730 hours at $59.76/hr). Talk to the Spheron AI team for reserved cluster pricing.
Qwen3.8-2.4T-A95B needs about 5333 GB at FP16, 2666 GB at INT8 and 1333 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 8x B300 288GB at about $59.76/hr.