DeepSeek-V4.1-Flash-1017-NVFP4 GPU Requirements: VRAM & Cheapest GPU
DeepSeek-V4.1-Flash-1017-NVFP4 has about 665B 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
- 665B
- Min VRAM (INT4)
- 362 GB
- Cheapest (INT4)
- $6.24/hr
- Deploy
- < 2 min
To run DeepSeek-V4.1-Flash-1017-NVFP4 for inference at FP16, you need roughly 1449 GB of VRAM. The cheapest fit on Spheron is 8x B200 192GB from $42.00/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$10.25/hr$82.00/hr8× B200 192GBCHEAPESTBlackwell · HBM3e
- 02$11.47/hr$91.76/hr8× B300 288GBBlackwell Ultra · HBM3e
Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.
Self-host DeepSeek-V4.1-Flash-1017-NVFP4 or use managed inference
Rent the GPUs and run DeepSeek-V4.1-Flash-1017-NVFP4 yourself, or skip the hardware with managed LLM inference: an OpenAI compatible API on open models, or dedicated inference endpoints for your own fine tuned model.
Run it yourself on GPUs
- VRAM at FP16
- 1449 GB
- Cheapest fit
- 8x B200 192GB from $42.00/hr
- Full control of weights, serving stack and batch size
- Per-minute billing, stop when you are done
- You size, scale and keep the GPUs up
Use a managed inference API
- Endpoint
- OpenAI compatible
- Pricing
- A fixed monthly token plan
- No GPUs to size, run or scale
- Requests at once held for you, uptime SLA in the contract
- Dedicated endpoints for your own fine tuned model
VRAM required to run DeepSeek-V4.1-Flash-1017-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 | 1449 GB | 2174 GB | 5798 GB |
| INT8 | 725 GB | 1087 GB | 2899 GB |
| INT4 | 362 GB | 544 GB | 1449 GB |
Cheapest GPU to run DeepSeek-V4.1-Flash-1017-NVFP4 by precision
1449GB
Full precision. Best quality, highest memory.
725GB
8-bit quantized. ~2x smaller, minimal quality loss.
362GB
4-bit quantized. ~4x smaller, runs on smaller GPUs.
Inference vs fine-tuning DeepSeek-V4.1-Flash-1017-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 DeepSeek-V4.1-Flash-1017-NVFP4, an on-demand B200 192GB 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 DeepSeek V4 step by stepHands-on production setup, GPU configs, and benchmarks for DeepSeek-V4.1-Flash-1017-NVFP4.Read guideSimilar models
Compare GPU requirements for models in the same class.
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763Bdealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8
763Bdealignai/DeepSeek-V4.1-Flash-1017-UNCENSORED-FP8
DeepSeek-V4.1-Flash-1017-NVFP4 GPU questions
VRAM, GPU choice and cost questions for running DeepSeek-V4.1-Flash-1017-NVFP4 on cloud GPUs.
DeepSeek-V4.1-Flash-1017-NVFP4 has about 665B parameters. At FP16 it needs roughly 1449 GB of VRAM for inference, including weights, activations, and KV cache. Quantized to INT4 that drops to around 362 GB. Leave about 10% headroom for production traffic.
For FP16 inference, the cheapest fit on Spheron is 8x B200 192GB from $42.00/hr. If you quantize to INT4, 8x RTX 6000 Ada 48GB at about $6.24/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 DeepSeek-V4.1-Flash-1017-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, DeepSeek-V4.1-Flash-1017-NVFP4 needs about 362 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 1449 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, DeepSeek-V4.1-Flash-1017-NVFP4 needs about 362 GB of VRAM, so it takes 5 x 80 GB GPUs at minimum, and 19 x 80 GB GPUs at FP16. The precision picks above show the cheapest GPU that fits each setup on Spheron.
DeepSeek-V4.1-Flash-1017-NVFP4 needs 19 x 80 GB GPUs at FP16 (about 1449 GB of VRAM), 10 x 80 GB GPUs at INT8 (725 GB) and 5 x 80 GB GPUs at INT4 (362 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.
DeepSeek-V4.1-Flash-1017-NVFP4 needs about 725 GB of VRAM at INT8, roughly half its FP16 footprint of 1449 GB. The cheapest INT8 fit on Spheron is 8x RTX PRO 6000 96GB from $8.88/hr. INT8 is the usual middle ground: half the memory of FP16, with a smaller quality drop than 4-bit quantization.
Running DeepSeek-V4.1-Flash-1017-NVFP4 around the clock at FP16 costs about $30,660 a month on 8x B200 192GB (730 hours at $42.00/hr at the spot rate, which can be reclaimed). Quantized to INT4 it costs about $4,555 a month on 8x RTX 6000 Ada 48GB (730 hours at $6.24/hr). Spheron bills per minute, so stopping the instance when it is idle cuts that figure directly.
DeepSeek-V4.1-Flash-1017-NVFP4 needs about 1449 GB at FP16, 725 GB at INT8 and 362 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 RTX 6000 Ada 48GB at about $6.24/hr.