Qwen2.5-32B-Instruct-AWQ GPU Requirements: VRAM & Cheapest GPU
Qwen2.5-32B-Instruct-AWQ has about 32.8B 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
- 32.8B
- Min VRAM (INT4)
- 18 GB
- Cheapest (INT4)
- $0.48/hr
- Deploy
- < 2 min
To run Qwen2.5-32B-Instruct-AWQ for inference at FP16, you need roughly 71 GB of VRAM. The cheapest fit on Spheron AI is A100 80GB from $1.19/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$1.43/hr1× A100 80GBCHEAPESTAmpere · HBM2e
- 02$0.78/hr$1.56/hr2× RTX 6000 Ada 48GBAda Lovelace · GDDR6 ECC
- 03$0.96/hr$1.92/hr2× L40S 48GBAda Lovelace · GDDR6
- 04$0.48/hr$1.92/hr4× RTX 4090 24GBAda Lovelace · GDDR6X
- 05$2.35/hr$2.35/hr1× RTX PRO 6000 96GBBlackwell · GDDR7
Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.
VRAM required to run Qwen2.5-32B-Instruct-AWQ
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 | 71 GB | 107 GB | 286 GB |
| INT8 | 36 GB | 54 GB | 143 GB |
| INT4 | 18 GB | 27 GB | 71 GB |
Cheapest GPU to run Qwen2.5-32B-Instruct-AWQ by precision
71GB
Full precision. Best quality, highest memory.
36GB
8-bit quantized. ~2x smaller, minimal quality loss.
18GB
4-bit quantized. ~4x smaller, runs on smaller GPUs.
Inference vs fine-tuning Qwen2.5-32B-Instruct-AWQ
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 Qwen2.5-32B-Instruct-AWQ, 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.
Qwen2.5-32B-Instruct-AWQ GPU questions
VRAM, GPU choice and cost questions for running Qwen2.5-32B-Instruct-AWQ on cloud GPUs.
Qwen2.5-32B-Instruct-AWQ has about 32.8B parameters. At FP16 it needs roughly 71 GB of VRAM for inference, including weights, activations, and KV cache. Quantized to INT4 that drops to around 18 GB. Leave about 10% headroom for production traffic.
For FP16 inference, the cheapest fit on Spheron AI is A100 80GB from $1.19/hr. If you quantize to INT4, RTX 4090 24GB at about $0.48/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 Qwen2.5-32B-Instruct-AWQ. 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, Qwen2.5-32B-Instruct-AWQ needs about 18 GB of VRAM, so it fits on a 24 GB RTX 4090 with room for a small batch. At FP16 it needs roughly 71 GB, which usually means a data center GPU. The precision picks above list the cheapest GPU that fits each setup.
Yes. At FP16 Qwen2.5-32B-Instruct-AWQ needs about 71 GB of VRAM, which fits one 80 GB H100 with about 8.6 GB left for longer contexts and larger batches. The precision picks above show the cheapest GPU that fits each setup on Spheron AI.
Qwen2.5-32B-Instruct-AWQ needs one 80 GB GPU at FP16 (about 71 GB of VRAM), one 80 GB GPU at INT8 (36 GB) and one 80 GB GPU at INT4 (18 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.
Qwen2.5-32B-Instruct-AWQ needs about 36 GB of VRAM at INT8, roughly half its FP16 footprint of 71 GB. The cheapest INT8 fit on Spheron AI is RTX 6000 Ada 48GB at about $0.78/hr. INT8 is the usual middle ground: half the memory of FP16, with a smaller quality drop than 4-bit quantization.
Running Qwen2.5-32B-Instruct-AWQ around the clock at FP16 costs about $869 a month on A100 80GB (730 hours at $1.19/hr at the spot rate, which can be reclaimed). Quantized to INT4 it costs about $350 a month on RTX 4090 24GB (730 hours at $0.48/hr). Spheron AI bills per minute, so stopping the instance when it is idle cuts that figure directly.
Qwen2.5-32B-Instruct-AWQ needs about 71 GB at FP16, 36 GB at INT8 and 18 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 RTX 4090 24GB at about $0.48/hr.