GigaChat3.5-432B-A28B GPU Requirements: VRAM & Cheapest GPU
GigaChat3.5-432B-A28B has about 434B 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
- 434B
- Min VRAM (INT4)
- 236 GB
- Cheapest (INT4)
- $4.76/hr
- Deploy
- < 2 min
To run GigaChat3.5-432B-A28B for inference at FP16, you need roughly 946 GB of VRAM. The cheapest fit on Spheron AI is 4x B300 288GB at about $29.88/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$7.47/hr$29.88/hr4× B300 288GBCHEAPESTBlackwell Ultra · HBM3e
- 02$4.80/hr$38.40/hr8× H200 141GBHopper · HBM3e
- 03$11.06/hr$88.48/hr8× B200 192GBBlackwell · HBM3e
Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.
VRAM required to run GigaChat3.5-432B-A28B
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 | 946 GB | 1418 GB | 3783 GB |
| INT8 | 473 GB | 709 GB | 1891 GB |
| INT4 | 236 GB | 355 GB | 946 GB |
Cheapest GPU to run GigaChat3.5-432B-A28B by precision
946GB
Full precision. Best quality, highest memory.
473GB
8-bit quantized. ~2x smaller, minimal quality loss.
236GB
4-bit quantized. ~4x smaller, runs on smaller GPUs.
Inference vs fine-tuning GigaChat3.5-432B-A28B
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 GigaChat3.5-432B-A28B, an on-demand B300 288GB 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.
GigaChat3.5-432B-A28B GPU questions
VRAM, GPU choice and cost questions for running GigaChat3.5-432B-A28B on cloud GPUs.
GigaChat3.5-432B-A28B has about 434B parameters. At FP16 it needs roughly 946 GB of VRAM for inference, including weights, activations, and KV cache. Quantized to INT4 that drops to around 236 GB. Leave about 10% headroom for production traffic.
For FP16 inference, the cheapest fit on Spheron AI is 4x B300 288GB at about $29.88/hr. If you quantize to INT4, 4x A100 80GB from $4.76/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 GigaChat3.5-432B-A28B. 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, GigaChat3.5-432B-A28B needs about 236 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 946 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, GigaChat3.5-432B-A28B needs about 236 GB of VRAM, so it takes 3 x 80 GB GPUs at minimum, and 12 x 80 GB GPUs at FP16. The precision picks above show the cheapest GPU that fits each setup on Spheron AI.
GigaChat3.5-432B-A28B needs 12 x 80 GB GPUs at FP16 (about 946 GB of VRAM), 6 x 80 GB GPUs at INT8 (473 GB) and 3 x 80 GB GPUs at INT4 (236 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.
GigaChat3.5-432B-A28B needs about 473 GB of VRAM at INT8, roughly half its FP16 footprint of 946 GB. The cheapest INT8 fit on Spheron AI is 8x A100 80GB from $9.52/hr. INT8 is the usual middle ground: half the memory of FP16, with a smaller quality drop than 4-bit quantization.
Running GigaChat3.5-432B-A28B around the clock at FP16 costs about $21,812 a month on 4x B300 288GB (730 hours at $29.88/hr). Quantized to INT4 it costs about $3,475 a month on 4x A100 80GB (730 hours at $4.76/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.
GigaChat3.5-432B-A28B needs about 946 GB at FP16, 473 GB at INT8 and 236 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 4x A100 80GB from $4.76/hr.