Qwen3.5-122B-A10B GPU Requirements: VRAM & Cheapest GPU
Qwen3.5-122B-A10B has about 125B 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
- 125B
- Min VRAM (INT4)
- 68 GB
- Cheapest (INT4)
- $1.19/hr
- Deploy
- < 2 min
To run Qwen3.5-122B-A10B for inference at FP16, you need roughly 273 GB of VRAM. The cheapest fit on Spheron AI is 4x A100 80GB from $4.76/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$5.72/hr4× A100 80GBCHEAPESTAmpere · HBM2e
- 02$0.78/hr$6.24/hr8× RTX 6000 Ada 48GBAda Lovelace · GDDR6 ECC
- 03$3.59/hr$7.18/hr2× MI300X 192GBAMD CDNA 3 · HBM3 · ROCm, check kernel support
- 04$7.47/hr$7.47/hr1× B300 288GBBlackwell Ultra · HBM3e
- 05$0.96/hr$7.68/hr8× L40S 48GBAda Lovelace · GDDR6
Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.
VRAM required to run Qwen3.5-122B-A10B
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 | 273 GB | 409 GB | 1091 GB |
| INT8 | 136 GB | 205 GB | 545 GB |
| INT4 | 68 GB | 102 GB | 273 GB |
Cheapest GPU to run Qwen3.5-122B-A10B by precision
273GB
Full precision. Best quality, highest memory.
136GB
8-bit quantized. ~2x smaller, minimal quality loss.
68GB
4-bit quantized. ~4x smaller, runs on smaller GPUs.
Inference vs fine-tuning Qwen3.5-122B-A10B
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, 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.Read guideSimilar models
Compare GPU requirements for models in the same class.
Qwen3.5-122B-A10B GPU questions
VRAM, GPU choice and cost questions for running Qwen3.5-122B-A10B on cloud GPUs.
Qwen3.5-122B-A10B has about 125B parameters. At FP16 it needs roughly 273 GB of VRAM for inference, including weights, activations, and KV cache. Quantized to INT4 that drops to around 68 GB. Leave about 10% headroom for production traffic.
For FP16 inference, the cheapest fit on Spheron AI is 4x A100 80GB from $4.76/hr. If you quantize to INT4, A100 80GB from $1.19/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. 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 needs about 68 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 273 GB, which usually means a data center GPU. The precision picks above list the cheapest GPU that fits each setup.
Only at INT4. Qwen3.5-122B-A10B needs about 273 GB at FP16 and 136 GB at INT8, both more than one 80 GB H100 holds. Quantized to INT4 it needs about 68 GB and fits one H100. The precision picks above show the cheapest GPU that fits each setup on Spheron AI.
Qwen3.5-122B-A10B needs 4 x 80 GB GPUs at FP16 (about 273 GB of VRAM), 2 x 80 GB GPUs at INT8 (136 GB) and one 80 GB GPU at INT4 (68 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 needs about 136 GB of VRAM at INT8, roughly half its FP16 footprint of 273 GB. The cheapest INT8 fit on Spheron AI is 2x A100 80GB from $2.38/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 around the clock at FP16 costs about $3,475 a month on 4x A100 80GB (730 hours at $4.76/hr at the spot rate, which can be reclaimed). Quantized to INT4 it costs about $869 a month on A100 80GB (730 hours at $1.19/hr at the spot rate, which can be reclaimed). Spheron AI bills per minute, so stopping the instance when it is idle cuts that figure directly.
Qwen3.5-122B-A10B needs about 273 GB at FP16, 136 GB at INT8 and 68 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 A100 80GB from $1.19/hr.