Text model · Kimi
KI Kimi K2 Instruct: RAM and VRAM requirements
Kimi K2 Instruct needs about 586.6 GB to run at Q4_K_M (the Q4_K_M GGUF file is ~578.15 GB to download; KV cache and overhead add the rest), or about 1024.5 GB at Q8_0. The lightest hardware that runs it is a high-memory machine.
Kimi family · 1000B params (Mixture-of-Experts: activates only 32B of 1000B params per token, so generation is faster than the total size suggests) · released Jul 2025.
Shopping for hardware? See what runs Kimi K2 Instruct →
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 K2 Instruct runs on 0 of 40 tracked devices at Q4_K_M.
Memory breakdown
How context length changes it
Longer context grows the KV cache, which for Kimi K2 Instruct is sized by its 32B active params, not the full 1000B. It needs ~586.6 GB at 4k and ~821.9 GB at 128k.
Benchmark scores
Aider polyglot · coding
#3 of 10 ranked · Percent of 225 Exercism exercises solved across six languages.
BFCL · tool use
#1 of 19 ranked · Overall Acc on the Berkeley Function-Calling Leaderboard.
Sourced third-party benchmarks for the full-precision Kimi K2 Instruct, not a specific quant. See the full leaderboard.
Quantization sizes
| Quantization | Size on disk |
|---|---|
| Q2_K | 418.8 GB est |
| Q3_K_M | 488.8 GB est |
| Q4_K_M (default) | 578.15 GB |
| Q5_K_M | 712.5 GB est |
| Q6_K | 820 GB est |
| Q8_0 | 1016.12 GB |
| FP16 | 2000 GB est |
Lower quant = smaller and faster, slightly lower quality. Q4_K_M is the common default.
Run it
ollama run kimi-k2 llama-cli -hf unsloth/Kimi-K2-Instruct-GGUF:Q4_K_M lms get unsloth/Kimi-K2-Instruct-GGUF Which devices can run Kimi K2 Instruct?
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
NVIDIA GPUs
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 K2 Instruct need?
At Q4_K_M, Kimi K2 Instruct needs about 586.6 GB (weights ~578.15 GB + KV cache + overhead) at a 4k context. At Q8_0 budget ~1024.5 GB.
What is the Q4_K_M GGUF file size of Kimi K2 Instruct?
The Q4_K_M GGUF file is about 578.15 GB to download, and the Q8_0 GGUF is about 1016.12 GB. That is the weights file on disk; to run it you also need room for the KV cache and overhead, so budget ~586.6 GB of memory at Q4_K_M.
Can Kimi K2 Instruct run on a laptop?
Kimi K2 Instruct is large; you need a high-memory Mac or multi-GPU setup at Q4_K_M.
Is Kimi K2 Instruct cheaper to run because it is a MoE model?
It is faster, not lighter. Kimi K2 Instruct activates only 32B of 1000B params per token (so it runs quickly), but all experts must stay in memory, so it still needs memory for the full 1000B.
Can I use Kimi K2 Instruct commercially?
Conditionally. Modified MIT; commercial use allowed below a large-user threshold.
Understand the numbers
Short guides to the ideas behind Kimi K2 Instruct's memory and quant figures.
Moonshot's 1T-parameter MoE (32B active). One of the largest open models; needs a serious cluster even at Q4.
Sources
Last validated 2026-08-03. Memory figures are estimates. See methodology.