What GPU do I need to run lightseekorg/kimi-k2.6-eagle3-mla?
3.0B parameters, published in BF16. View on Hugging Face
Set up kimi-k2.6-eagle3-mlakimi-k2.6-eagle3-mla is published by lightseekorg on Hugging Face, with 112,398 downloads and 7 likes to date. It's a Eagle3DeepseekV2ForCausalLM 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 | 5.6 GB | 6.7 GB | RTX 3070 | 1 | $0.088/hr |
| FP8 (quantized) | 2.8 GB | 3.4 GB | RTX 4070 | 1 | $0.121/hr |
| INT4 (quantized) | 1.4 GB | 1.7 GB | RTX 3070 | 1 | $0.088/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-k2.6-eagle3-mla at its published (BF16) precision: 1× RTX 3070, at $0.088/hr per GPU ($0.088/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
kimi-k2.6-eagle3-mla: common questions
Does kimi-k2.6-eagle3-mla fit on a 8 GB GPU?
Yes. At BF16 it needs 6.7 GB of VRAM, so an 8 GB card holds it with 1.3 GB to spare. A 6 GB card is not enough for it at BF16.
What is the least VRAM kimi-k2.6-eagle3-mla can run in?
1.7 GB, at INT4 (quantized), which fits a 6 GB card, against 6.7 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.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More lightseekorg models
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
- Qwen3.5-9B (9.7B, BF16)
Related reading: RTX 3070 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.
