What GPU do I need to run zianglih/JoyAI-LLM-Flash-MXFP8-last-6-BF16?
50.6B parameters, published in F8_E4M3. View on Hugging Face
Set up JoyAI-LLM-F…XFP8-last-6-BF16JoyAI-LLM-Flash-MXFP8-last-6-BF16 is published by zianglih on Hugging Face, with 31,470 downloads and 0 likes to date. It's a DeepseekV3ForCausalLM model built for text-generation, published natively in F8_E4M3.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP8 (native) | 47.1 GB | 56.5 GB | RTX PRO 6000 | 1 | $1.38/hr |
| cheaper alt. | RTX 5060 Ti | 4 | $0.440/hr | ||
| INT4 (quantized) | 23.5 GB | 28.3 GB | RTX 4080 Super | 1 | $0.338/hr |
| cheaper alt. | RTX 5060 Ti | 2 | $0.220/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run JoyAI-LLM-Flash-MXFP8-last-6-BF16 at its published (F8_E4M3) precision: 1× RTX PRO 6000, at $1.38/hr per GPU ($1.38/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
JoyAI-LLM-Flash-MXFP8-last-6-BF16: common questions
Can JoyAI-LLM-Flash-MXFP8-last-6-BF16 run on a single GPU?
Yes, but not on a desktop card. At FP8 (native) it needs 56.5 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 95.0 GB RTX PRO 6000 at $1.38/hr.
Is JoyAI-LLM-Flash-MXFP8-last-6-BF16 already quantized?
Yes. It is published in FP8, one byte per parameter, so the 56.5 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 28.3 GB. A GPU without FP8 tensor cores cannot run it as published, which is why cards here are matched on precision support and not on VRAM alone.
What is the least VRAM JoyAI-LLM-Flash-MXFP8-last-6-BF16 can run in?
28.3 GB, at INT4 (quantized), which fits a 32 GB card, against 56.5 GB at FP8 (native). That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing JoyAI-LLM-Flash-MXFP8-last-6-BF16 lower the GPU bill?
Yes. At FP8 (native) the cheapest live fit is one RTX PRO 6000 at $1.38/hr. At INT4 (quantized) it drops to one RTX 4080 Super at $0.338/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
Related reading: RTX PRO 6000 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
