What GPU do I need to run moonshotai/Kimi-Linear-48B-A3B-Instruct?
49.1B parameters, published in BF16. View on Hugging Face
Set up Kimi-Linear-48B-A3B-InstructKimi-Linear-48B-A3B-Instruct is published by moonshotai on Hugging Face, with 184,990 downloads and 589 likes to date. It's a KimiLinearForCausalLM model built for text-generation, published natively in BF16.
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) |
|---|---|---|---|---|---|
| BF16 | 91.5 GB | 109.8 GB | RTX A5000 | 5 | $0.880/hr |
| FP8 (quantized) | 45.7 GB | 54.9 GB | RTX PRO 6000 | 1 | $1.54/hr |
| cheaper alt. | RTX 4000 SFF Ada | 3 | $0.594/hr | ||
| INT4 (quantized) | 22.9 GB | 27.4 GB | RTX A6000 | 1 | $0.363/hr |
| cheaper alt. | RTX 3060 | 3 | $0.330/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 Kimi-Linear-48B-A3B-Instruct at its published (BF16) precision: 5× RTX A5000, at $0.176/hr per GPU ($0.880/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Kimi-Linear-48B-A3B-Instruct: common questions
Can Kimi-Linear-48B-A3B-Instruct run on a single GPU?
No. At BF16 it needs 109.8 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 24.0 GB RTX A5000, and it takes 5 of them.
How many GPUs do I need to run Kimi-Linear-48B-A3B-Instruct?
5 at BF16. It needs 109.8 GB of VRAM and the cheapest capable live offer is a 24.0 GB RTX A5000, so 5 of them come to $0.880/hr in total.
What is the least VRAM Kimi-Linear-48B-A3B-Instruct can run in?
27.4 GB, at INT4 (quantized), which fits a 32 GB card, against 109.8 GB at BF16. 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 Kimi-Linear-48B-A3B-Instruct lower the GPU bill?
Yes. At BF16 the cheapest live fit is 5 RTX A5000 cards at $0.880/hr. At INT4 (quantized) it drops to one RTX A6000 at $0.363/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More moonshotai models
- Kimi-VL-A3B-Instruct (16.4B, BF16)
- Kimi-K2-Instruct (1026.4B, F8_E4M3)
- Moonlight-16B-A3B-Instruct (16.0B, BF16)
- Kimi-K2-Instruct-0905 (1026.5B, F8_E4M3)
- Moonlight-16B-A3B (16.0B, BF16)
- Kimi-K2-Base (1026.5B, F8_E4M3)
Related reading: RTX A5000 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.
