Kimi K2.6 VRAM Requirements
Kimi K2.6 needs about 556 GB of VRAM at INT4 (official) with an 8K context: 555 GB for weights, 0.5 GB for the KV cache and 0.9 GB of runtime overhead. At Q4_K_M it needs 566 GB. At Q8_0 it needs 991 GB and at full FP16 precision 1864 GB.
556 GB VRAM
Kimi K2.6 at INT4 (official) with 8K context and the KV cache in FP16.
- Weights 555 GB
- KV cache 0.5 GB
- Runtime overhead 0.9 GB
Mixture of experts: all 1000B parameters need memory, but only 32B are read per token, so it runs at the speed of a much smaller model.
Which GPUs can run it
- RTX 3060 12GBToo small
- RTX 4060 Ti 16GBToo small
- RTX 3090 24GBToo small
- Radeon RX 7900 XTX 24GBToo small
- RTX 4090 24GBToo small
- RTX 5090 32GBToo small
- RTX 6000 Ada 48GBToo small
- A100 80GBToo small
- H100 80GB SXMToo small
- Ryzen AI Max+ 395 128GBToo small
- Apple M4 Max 128GBToo small
- Apple M5 Max 128GBToo small
- RTX PRO 6000 Blackwell 96GBToo small
- NVIDIA DGX Spark 128GBToo small
- Apple M3 Ultra 512GBToo small
Kimi K2.6 VRAM by quantization
Total memory with an FP16 KV cache. "Smallest tier" is the smallest standard VRAM size with a 6% safety margin at 8K.
| Quantization | Bits/weight | Weights | Total at 8K | Total at 32K | Smallest tier |
|---|---|---|---|---|---|
| INT4 (official) | 4.77 | 555 GB | 556 GB | 558 GB | Multi-GPU / server |
| Q4_K_M | 4.85 | 565 GB | 566 GB | 568 GB | Multi-GPU / server |
| Q5_K_M | 5.69 | 662 GB | 664 GB | 665 GB | Multi-GPU / server |
| Q6_K | 6.59 | 767 GB | 769 GB | 770 GB | Multi-GPU / server |
| Q8_0 | 8.50 | 990 GB | 991 GB | 993 GB | Multi-GPU / server |
| FP16 / BF16 | 16.00 | 1863 GB | 1864 GB | 1866 GB | Multi-GPU / server |
| Q3_K_M | 3.91 | 455 GB | 457 GB | 458 GB | Multi-GPU / server |
| Q2_K | 3.00 | 349 GB | 351 GB | 352 GB | Multi-GPU / server |
VRAM by context length
At INT4 (official). This model uses Multi-head Latent Attention: each layer caches one compressed 576-value vector per token instead of full keys and values. Runtimes without MLA support use many times more memory.
| Context | KV cache (FP16) | KV cache (Q8_0) | Total, FP16 cache | Total, Q8_0 cache |
|---|---|---|---|---|
| 4K tokens | 0.3 GB | 0.1 GB | 556 GB | 556 GB |
| 8K tokens | 0.5 GB | 0.3 GB | 556 GB | 556 GB |
| 32K tokens | 2.1 GB | 1.1 GB | 558 GB | 557 GB |
| 128K tokens | 8.6 GB | 4.6 GB | 565 GB | 560 GB |
| 256K tokens | 17.2 GB | 9.1 GB | 573 GB | 565 GB |
GPUs that can run Kimi K2.6
At INT4 (official) with 8K context. "Best quant" is the highest-quality format that fits fully; "max context" is at INT4 (official).
| GPU | Usable memory | Status | Best quant | Max context | Speed |
|---|---|---|---|---|---|
| AMD Instinct MI300X 192GB | 192 GB | 32% on GPU | – | – | ~3.1 tok/s |
About Kimi K2.6
A one-trillion-parameter MoE (384 experts, 8 active, about 32B active) on the DeepSeek V3 architecture with latent attention. The experts ship natively in 4-bit integer format, so the official checkpoint is roughly 555 GB and further quantization saves little. The native INT4 size here is our estimate from the quantization config.
| Spec | Value |
|---|---|
| Parameters | 1000B total, 32B active (MoE) |
| Layers | 61 |
| Hidden size | 7168 |
| Attention heads / KV heads | 64 / MLA latent (576) |
| Head dimension | 128 |
| Vocabulary | 163,840 |
| Max context (used here) | 256K tokens |
| KV cache per token (FP16) | 69 KB |
| Released | 2026-04 |
| License | Modified MIT |
Kimi K2.6 VRAM: common questions
Can Kimi K2.6 run on a 512GB Mac Studio?
Only with lower-bit quants. The native INT4 weights are about 555 GB, more than the roughly 384 GB the GPU can use by default, so you need an IQ2 or IQ3 GGUF or a raised wired-memory limit.
How much VRAM does Kimi K2.6 need?
About 556 GB at INT4 (official) with an 8K context, 991 GB at Q8_0 and 1864 GB at FP16. Longer contexts add 0.07 GB per 1,000 tokens with an FP16 cache.
Can Kimi K2.6 run on a single GPU?
Not at INT4 (official). It needs about 556 GB, which takes a multi-GPU server or a very large unified-memory system.
Does Kimi K2.6 need VRAM for all 1000B parameters?
Yes. All experts must be loaded because any token can use any expert. Only 32B parameters are read per token, so it generates text about as fast as a 32B dense model.