Kimi K2.5 Hardware Requirements
A trillion-parameter open model with native multimodality and agent-swarm tricks (its K2.6 sibling ties frontier closed models on SWE-Bench). Consumer hardware need not apply — but the weights are right there.
VRAM needed (Q4, 8k context)
608 GB
No single consumer GPU fits this model — see multi-GPU and Mac options below.
Updated July 2026. Estimates — see methodology below.
VRAM by Quantization
Weights + KV cache at 8k context + 1.2GB system overhead. Q4_K_M is the community default — quality loss is negligible for most use.
| Quantization | Weights | KV cache (8k) | Total VRAM | Cheapest GPU that fits |
|---|---|---|---|---|
| Q4_K_M Recommended — near-lossless for most use, half the size of Q8 | 606 GB | 0.5 GB | 608 GB | Multi-GPU / Mac territory |
| Q5_K_M Slightly higher quality than Q4 for ~18% more VRAM | 713 GB | 0.5 GB | 714 GB | Multi-GPU / Mac territory |
| Q8_0 Effectively lossless — use if you have VRAM to spare | 1063 GB | 0.5 GB | 1064 GB | Multi-GPU / Mac territory |
| FP16 Full precision — only for fine-tuning or maximum fidelity | 2000 GB | 0.5 GB | 2002 GB | Multi-GPU / Mac territory |
Longer context costs VRAM
KV cache grows linearly with context: 8k → 0.5 GB · 32k → 2.2 GB · 128k → 8.8 GB. If you plan to feed whole documents or codebases, size your GPU for the context you actually need, not just the weights.
Why Kimi K2.5 is fast but VRAM-hungry
Kimi K2.5 is a Mixture-of-Experts model: all 1000B parameters must sit in memory, but each token only activates 32B of them. Memory capacity requirements are those of a 1000B model, while speed is that of a 32B model — which is why MoE models feel so fast when they fit, and why Macs with large unified memory punch above their weight running them.
GPU Compatibility (Q4, 8k context)
Every GPU in our database, scored against Kimi K2.5. Speed is estimated decode rate — memory-bandwidth-bound, so VRAM and bandwidth matter more than shader count.
| GPU | VRAM | Verdict | Est. speed | Price | |
|---|---|---|---|---|---|
| RTX 3060 | 12 GB | Not enough VRAM | — | ~$238 used | |
| Arc B570 | 10 GB | Not enough VRAM | — | from $225 | |
| RTX 4060 | 8 GB | Not enough VRAM | — | ~$275 used | |
| RX 7600 | 8 GB | Not enough VRAM | — | from $250 | |
| Arc B580 | 12 GB | Not enough VRAM | — | from $250 | |
| RX 6700 XT | 12 GB | Not enough VRAM | — | ~$315 used | |
| Arc A770 | 16 GB | Not enough VRAM | — | from $300 | |
| RTX 4060 Ti | 8 GB | Not enough VRAM | — | ~$338 used | |
| RTX 3070 | 8 GB | Not enough VRAM | — | ~$338 used | |
| RTX 5060 | 8 GB | Not enough VRAM | — | from $325 | |
| RX 7700 XT | 12 GB | Not enough VRAM | — | ~$415 used | |
| RX 6800 XT | 16 GB | Not enough VRAM | — | ~$438 used | |
| RTX 3080 | 10 GB | Not enough VRAM | — | ~$463 used | |
| RX 7800 XT | 16 GB | Not enough VRAM | — | ~$488 used | |
| RTX 4070 | 12 GB | Not enough VRAM | — | ~$500 used | |
| RTX 4070 SUPER | 12 GB | Not enough VRAM | — | ~$563 used | |
| RX 7900 XT | 20 GB | Not enough VRAM | — | ~$588 used | |
| RTX 5060 Ti | 16 GB | Not enough VRAM | — | from $550 | |
| RX 9070 | 16 GB | Not enough VRAM | — | from $575 | |
| RTX 5070 | 12 GB | Not enough VRAM | — | from $600 | |
| RX 9070 XT | 16 GB | Not enough VRAM | — | from $600 | |
| RTX 4070 Ti SUPER | 16 GB | Not enough VRAM | — | ~$750 used | |
| RX 7900 XTX | 24 GB | Not enough VRAM | — | ~$838 used | |
| RTX 4080 SUPER | 16 GB | Not enough VRAM | — | ~$900 used | |
| RTX 5070 Ti | 16 GB | Not enough VRAM | — | from $900 | |
| RTX 3090 | 24 GB | Not enough VRAM | — | ~$1,150 used | |
| RTX 5080 | 16 GB | Not enough VRAM | — | from $1,250 | |
| RTX 4090 | 24 GB | Not enough VRAM | — | ~$2,375 used | |
| RTX 5090 | 32 GB | Not enough VRAM | — | from $2,800 |
Frequently Asked Questions
How much VRAM do I need to run Kimi K2.5?+
At the recommended Q4_K_M quantization with 8k context, Kimi K2.5 needs roughly 608GB of VRAM (606GB weights + KV cache + overhead). Q8 needs about 1064GB and full FP16 about 2002GB.
Can any single consumer GPU run Kimi K2.5?+
No single consumer GPU currently has enough VRAM to run Kimi K2.5 fully. Realistic options: a multi-GPU rig (e.g. dual 24GB cards), a Mac with enough unified memory, or a 128GB Ryzen AI Max "Strix Halo" mini-PC — the 2026 favorite for exactly this class of model.
Can I run Kimi K2.5 on an RTX 3060?+
No — 12GB is well below what Kimi K2.5 needs even at Q4 quantization.
Can I run Kimi K2.5 on a Mac?+
Yes, if the Mac has enough unified memory: budget roughly 608GB of RAM for the Q4 version (plus what macOS itself uses). Apple Silicon runs GGUF models well via Ollama or LM Studio, and MoE models like this one are particularly Mac-friendly — only 32B parameters are active per token, so memory bandwidth goes further.
Can I run Kimi K2.5 on CPU only?+
Sort of. Because only 32B of 1000B parameters are active per token, CPU inference is more viable than for dense models this size — expect single-digit tokens/sec with fast DDR5. A GPU is still dramatically better.
Is Kimi K2.5 free for commercial use?+
Yes. Kimi K2.5 is released under the Modified MIT, which permits commercial use.
Related Models
How we calculate these numbers
VRAM = model weights (parameters × bits per weight ÷ 8) + KV cache (architecture-specific bytes per token × context length) + ~1.2GB runtime overhead. Speed estimates assume decode is memory-bandwidth-bound at ~50% utilization (lower for MoE models, which pay routing overhead), matching typical llama.cpp performance on consumer cards; real results vary with runtime, drivers, and settings. Quant sizes reflect GGUF K-quants, which keep some layers at higher precision. Figures are estimates for planning, not guarantees — when in doubt, buy more VRAM than you need today. Prices shown are launch MSRP; mid-2026 street prices often run well above MSRP due to the ongoing memory shortage, and used 24GB cards are holding their value unusually well.