Text model · Kimi
KI Kimi K3: RAM and VRAM requirements
Kimi K3 needs about 1522.2 GB to run at Q4_K_M (the Q4_K_M GGUF file is ~1508.67 GB to download; KV cache and overhead add the rest), or about 2988.5 GB at Q8_0. The lightest hardware that runs it is a high-memory machine.
Kimi family · 2800B params (Mixture-of-Experts: activates only 104B of 2800B params per token, so generation is faster than the total size suggests) · released Jun 2026.
Shopping for hardware? See what runs Kimi K3 →
Q4_K_M and Q8_0 are GGUF download (file) sizes. Memory @ Q4 includes the KV cache and overhead, so it runs larger than the file on disk.
Will it run on your device?
Kimi K3 runs on 0 of 43 tracked devices at Q4_K_M.
Memory breakdown
How context length changes it
Longer context grows the KV cache, which for Kimi K3 is sized by its 104B active params, not the full 2800B. It needs ~1522.2 GB at 4k and ~1915.9 GB at 128k.
Quantization sizes
| Quantization | Size on disk |
|---|---|
| Q2_K | 1172.5 GB est |
| Q3_K_M | 1368.5 GB est |
| Q4_K_M (default) | 1508.67 GB |
| Q5_K_M | 1995 GB est |
| Q6_K | 2296 GB est |
| Q8_0 | 2975 GB est |
| FP16 | 5600 GB est |
Lower quant = smaller and faster, slightly lower quality. Q4_K_M is the common default.
Run it
llama-cli -hf unsloth/Kimi-K3-GGUF:Q4_K_M lms get unsloth/Kimi-K3-GGUF Which devices can run Kimi K3?
NVIDIA GPUs
- Nvidia GeForce RTX 2060 (6GB) No
- Nvidia GeForce RTX 3060 Ti (8GB) No
- Nvidia GeForce GTX 1070 (8GB) No
- Nvidia GeForce RTX 3060 (12GB) No
- Nvidia GeForce RTX 4070 (12GB) No
- Nvidia GeForce RTX 4060 Ti (16GB) No
- Nvidia GeForce RTX 4080 (16GB) No
- Nvidia GeForce RTX 4090 (24GB) No
- Nvidia GeForce RTX 3090 (24GB) No
- Nvidia GeForce RTX 5090 (32GB) No
Apple Silicon Macs
- Apple M1 (8GB) No
- Apple M2 (16GB) No
- Apple M4 (16GB) No
- Apple M5 (16GB) No
- Apple M3 Pro (18GB) No
- Apple M4 (24GB) No
- Apple M4 Pro (24GB) No
- Apple M5 (32GB) No
- Apple M4 Pro (48GB) No
- Apple M5 Pro (48GB) No
- Apple M4 Max (64GB) No
- Apple M4 Max (128GB) No
- Apple M5 Max (128GB) No
- Apple M3 Ultra (256GB) No
RAM-only laptops
iPhone & iPad
Android
Same job, different size
Pick by what fits your memory: step down to free up VRAM, or step up if you have headroom.
Similar models
Head-to-head
FAQ
How much VRAM or RAM does Kimi K3 need?
At Q4_K_M, Kimi K3 needs about 1522.2 GB (weights ~1508.67 GB + KV cache + overhead) at a 4k context. At Q8_0 budget ~2988.5 GB.
What is the Q4_K_M GGUF file size of Kimi K3?
The Q4_K_M GGUF file is about 1508.67 GB to download, and the Q8_0 GGUF is about 2975 GB. That is the weights file on disk; to run it you also need room for the KV cache and overhead, so budget ~1522.2 GB of memory at Q4_K_M.
Can Kimi K3 run on a laptop?
Kimi K3 is large; you need a high-memory Mac or multi-GPU setup at Q4_K_M.
Is Kimi K3 cheaper to run because it is a MoE model?
It is faster, not lighter. Kimi K3 activates only 104B of 2800B params per token (so it runs quickly), but all experts must stay in memory, so it still needs memory for the full 2800B.
Can I use Kimi K3 commercially?
Conditionally. Kimi K3 License (MIT-style); products over 100M MAU or 20M USD monthly revenue owe attribution, and Model-as-a-Service businesses over 20M USD yearly revenue have extra terms.
Understand the numbers
Short guides to the ideas behind Kimi K3's memory and quant figures.
The largest open-weight model tracked here alongside DeepSeek-V4-Pro: 2.8T total / 104B active (LatentMoE, 16 of 896 experts), Kimi Delta Attention, native vision, 1M context, trained quantization-aware in MXFP4. Q4 figure is unsloth's UD-Q4_K_XL split sum (32 files); no full-precision GGUF exists. Ollama lists it cloud-only; strictly multi-node territory locally.
Sources
Last validated 2026-09-07. Memory figures are estimates. See methodology.