Qwen3.5-122B-A10B-NVFP4 GPU Requirements: VRAM & Cheapest GPU
Qwen3.5-122B-A10B-NVFP4 has about 64.6B 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
- 64.6B
- Min VRAM (INT4)
- 35 GB
- Cheapest (INT4)
- $0.78/hr
- Deploy
- < 2 min
To run Qwen3.5-122B-A10B-NVFP4 for inference at FP16, you need roughly 141 GB of VRAM. The cheapest fit on Spheron AI is 2x A100 80GB from $2.38/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$2.86/hr2× A100 80GBCHEAPESTAmpere · HBM2e
- 02$0.78/hr$3.12/hr4× RTX 6000 Ada 48GBAda Lovelace · GDDR6 ECC
- 03$3.59/hr$3.59/hr1× MI300X 192GBAMD CDNA 3 · HBM3 · ROCm, check kernel support
- 04$0.96/hr$3.84/hr4× L40S 48GBAda Lovelace · GDDR6
- 05$0.48/hr$3.84/hr8× RTX 4090 24GBAda Lovelace · GDDR6X
Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.
VRAM required to run Qwen3.5-122B-A10B-NVFP4
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 | 141 GB | 211 GB | 563 GB |
| INT8 | 70 GB | 106 GB | 282 GB |
| INT4 | 35 GB | 53 GB | 141 GB |
Cheapest GPU to run Qwen3.5-122B-A10B-NVFP4 by precision
141GB
Full precision. Best quality, highest memory.
70GB
8-bit quantized. ~2x smaller, minimal quality loss.
35GB
4-bit quantized. ~4x smaller, runs on smaller GPUs.
Inference vs fine-tuning Qwen3.5-122B-A10B-NVFP4
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 Qwen3.5-122B-A10B-NVFP4, 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.
Deployment guideDeploy Qwen 3.5 step by stepHands-on production setup, GPU configs, and benchmarks for Qwen3.5-122B-A10B-NVFP4.Read guideSimilar models
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Qwen3.5-122B-A10B-NVFP4 GPU questions
VRAM, GPU choice and cost questions for running Qwen3.5-122B-A10B-NVFP4 on cloud GPUs.
Qwen3.5-122B-A10B-NVFP4 has about 64.6B parameters. At FP16 it needs roughly 141 GB of VRAM for inference, including weights, activations, and KV cache. Quantized to INT4 that drops to around 35 GB. Leave about 10% headroom for production traffic.
For FP16 inference, the cheapest fit on Spheron AI is 2x A100 80GB from $2.38/hr. If you quantize to INT4, RTX 6000 Ada 48GB at about $0.78/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 Qwen3.5-122B-A10B-NVFP4. 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, Qwen3.5-122B-A10B-NVFP4 needs about 35 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 141 GB, which usually means a data center GPU. The precision picks above list the cheapest GPU that fits each setup.
Not at FP16. Qwen3.5-122B-A10B-NVFP4 needs about 141 GB at FP16, more than one 80 GB H100 holds, so plan on 2 x 80 GB GPUs. Quantized to INT8 it needs about 70 GB and fits a single H100. The precision picks above show the cheapest GPU that fits each setup on Spheron AI.
Qwen3.5-122B-A10B-NVFP4 needs 2 x 80 GB GPUs at FP16 (about 141 GB of VRAM), one 80 GB GPU at INT8 (70 GB) and one 80 GB GPU at INT4 (35 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.
Qwen3.5-122B-A10B-NVFP4 needs about 70 GB of VRAM at INT8, roughly half its FP16 footprint of 141 GB. The cheapest INT8 fit on Spheron AI is A100 80GB from $1.19/hr. INT8 is the usual middle ground: half the memory of FP16, with a smaller quality drop than 4-bit quantization.
Running Qwen3.5-122B-A10B-NVFP4 around the clock at FP16 costs about $1,737 a month on 2x A100 80GB (730 hours at $2.38/hr at the spot rate, which can be reclaimed). Quantized to INT4 it costs about $569 a month on RTX 6000 Ada 48GB (730 hours at $0.78/hr). Spheron AI bills per minute, so stopping the instance when it is idle cuts that figure directly.
Qwen3.5-122B-A10B-NVFP4 needs about 141 GB at FP16, 70 GB at INT8 and 35 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 6000 Ada 48GB at about $0.78/hr.