Kimi-K2-Instruct requirements — can you run it?
~1026.6B parameters · 27 quantizations measured · updated 2025-12-29 · source: unsloth/Kimi-K2-Instruct-GGUF
Every quantization: real file size vs what you actually need
| Quant | File (measured) | VRAM/RAM needed (8K ctx) | Runs on (first fit) |
|---|---|---|---|
| UD-TQ1_0 | 243.6 GB | 293.8 GB | Mac 512GB unified |
| UD-IQ1_S | 280.1 GB | 337.6 GB | Mac 512GB unified |
| UD-IQ1_M | 304.2 GB | 366.6 GB | Mac 512GB unified |
| UD-IQ2_XXS | 328.8 GB | 396.0 GB | Mac 512GB unified |
| UD-IQ2_M | 347.1 GB | 418.0 GB | Mac 512GB unified |
| Q2_K | 373.2 GB | 449.3 GB | Mac 512GB unified |
| Q2_K_L | 373.5 GB | 449.6 GB | Mac 512GB unified |
| UD-Q2_K_XL | 381.9 GB | 459.7 GB | Mac 512GB unified |
| UD-IQ3_XXS | 416.6 GB | 501.4 GB | 1TB server RAM (CPU) |
| Q3_K_S | 442.4 GB | 532.4 GB | 1TB server RAM (CPU) |
| UD-Q3_K_XL | 452.1 GB | 544.0 GB | 1TB server RAM (CPU) |
| Q3_K_M | 489.4 GB | 588.8 GB | 1TB server RAM (CPU) |
| IQ4_XS | 546.5 GB | 657.3 GB | 1TB server RAM (CPU) |
| IQ4_NL | 578.5 GB | 695.7 GB | 1TB server RAM (CPU) |
| Q4_0 | 580.6 GB | 698.2 GB | 1TB server RAM (CPU) |
| Q4_K_S | 582.7 GB | 700.8 GB | 1TB server RAM (CPU) |
| UD-Q4_K_XL | 587.1 GB | 706.0 GB | 1TB server RAM (CPU) |
| Q4_K_M | 620.8 GB | 746.4 GB | 1TB server RAM (CPU) |
| Q4_1 | 642.5 GB | 772.5 GB | 1TB server RAM (CPU) |
| Q5_K_S | 706.6 GB | 849.4 GB | 1TB server RAM (CPU) |
| Q5_K_M | 728.3 GB | 875.5 GB | 1TB server RAM (CPU) |
| UD-Q5_K_XL | 730.6 GB | 878.2 GB | 1TB server RAM (CPU) |
| Q6_K | 842.6 GB | 1.01 TB | 2TB server RAM (CPU) |
| UD-Q6_K_XL | 879.1 GB | 1.06 TB | 2TB server RAM (CPU) |
| Q8_0 | 1.09 TB | 1.31 TB | 2TB server RAM (CPU) |
| UD-Q8_K_XL | 1.19 TB | 1.43 TB | 2TB server RAM (CPU) |
| BF16 | 2.05 TB | 2.47 TB | server-grade |
KV cache uses a flat +20% context/overhead rule because this repo does not publish its config; exact numbers may differ for long contexts. MoE models keep all experts in memory — total size counts, not just active params.
Can I run Kimi-K2-Instruct on my GPU? (UD-IQ2_XXS recommended quant)
| Hardware | Memory | Verdict |
|---|---|---|
| RTX 3060 12GB | 12 GB | no |
| RTX 4060 Ti 16GB | 16 GB | no |
| RTX 3090 24GB | 24 GB | no |
| RTX 4090 24GB | 24 GB | no |
| RTX 5090 32GB | 32 GB | no |
| RTX PRO 6000 96GB | 96 GB | no |
| Mac 16GB unified | 16 GB | no |
| Mac 32GB unified | 32 GB | no |
| Mac 64GB unified | 64 GB | no |
| Mac 128GB unified | 128 GB | no |
| Mac 256GB unified | 256 GB | no |
| Mac 512GB unified | 512 GB | RUNS |
| 32GB system RAM (CPU) | 32 GB | no |
| 64GB system RAM (CPU) | 64 GB | no |
| 128GB system RAM (CPU) | 128 GB | no |
| 256GB system RAM (CPU) | 256 GB | no |
| 1TB server RAM (CPU) | 1024 GB | RUNS |
| 2TB server RAM (CPU) | 2048 GB | RUNS |
How much VRAM does Kimi-K2-Instruct need?
The honest answer: 396.0 GB at the UD-IQ2_XXS quant with 8K context — that is the measured file size (328.8 GB) plus KV cache and runtime overhead. The smallest published build needs 293.8 GB; the lossless one needs 2.47 TB.
Can I run it with CPU offload?
Yes, if combined RAM+VRAM ≥ file size — but generation speed is limited by memory bandwidth. Expect roughly: DDR5 dual channel ~10-30 tok/s for small MoE actives, single digits for big ones, 0.1-1 tok/s when experts page from disk. CPU-only is fine for batch jobs, painful for chat.
Why do other sites show different numbers?
Most pages compute weights from a formula (params × bits/8). We use the actual file sizes published in GGUF repos, which include the embedding table, unquantized tensors, and container overhead — that is why our numbers can differ from a naive calculation.
FAQ
How much VRAM does Kimi-K2-Instruct need?
The measured answer: 396.0 GB at the UD-IQ2_XXS quant with 8K context. The smallest published build needs 293.8 GB; the lossless (F16/BF16) build needs 2.47 TB. These are real GGUF file sizes from Hugging Face, not formula estimates.
Can I run Kimi-K2-Instruct on a 24GB GPU (RTX 3090/4090)?
Not at UD-IQ2_XXS (396.0 GB). Nothing fits 24GB — the smallest build needs 293.8 GB. Use the hosted API or a smaller model.
Can I run Kimi-K2-Instruct on CPU with system RAM?
Yes, if your usable RAM is at least the file size. At UD-IQ2_XXS you need ~396.0 GB of RAM. CPU generation is memory-bandwidth-bound: expect single-digit tok/s for large MoE models, 10-30 tok/s for small active params, and below 1 tok/s when experts page from disk.
Why do VRAM numbers for Kimi-K2-Instruct differ between sites?
Most sites compute weights from a formula (params x bits/8). ModelFit uses the actual GGUF file sizes published on Hugging Face, which include the embedding table, unquantized tensors, and container overhead — so our numbers reflect what you really download and load.