ToolSite Kimi K3 VRAM requirements
Kimi K3 has 2.78T parameters, of which about 104B are used per token; all 896 experts still have to be in memory. With an 8K-token context and one request it needs about 1,724 GB of GPU memory at Q4_K_M, 2,849 GB at FP8 and 5,697 GB at FP16/BF16. The published weights take 1,454 GB (4.5 bits/weight). No single GPU in the table holds it at Q4_K_M; it needs a multi-GPU setup.
Open Kimi K3 in the calculator
VRAM by quantization
Weights plus the KV cache for 8,192 tokens in FP16 and the runtime overhead (0.5 GB plus 10%). Each row opens the calculator with that setting. What the GGUF names mean.
| Precision | Weights | Total | Smallest setup |
|---|---|---|---|
| As published (4.5 bits/weight) | 1,454 GB | 1,600 GB | More than 8 GPUs |
| FP16 / BF16 | 5,178 GB | 5,697 GB | More than 8 GPUs |
| FP8 / INT8 | 2,589 GB | 2,849 GB | More than 8 GPUs |
| INT4 (AWQ / GPTQ) | 1,375 GB | 1,514 GB | More than 8 GPUs |
| GGUF Q8_0 | 2,751 GB | 3,027 GB | More than 8 GPUs |
| GGUF Q6_K | 2,123 GB | 2,336 GB | More than 8 GPUs |
| GGUF Q5_K_M | 1,835 GB | 2,019 GB | More than 8 GPUs |
| GGUF Q4_K_M | 1,566 GB | 1,724 GB | More than 8 GPUs |
| GGUF Q3_K_M | 1,265 GB | 1,393 GB | 8 × B200 |
| GGUF Q2_K | 1,084 GB | 1,193 GB | 8 × B200 |
KV cache at long context
24 of its 93 layers use multi-head latent attention and 69 are linear-attention layers with no growing cache. Each extra token of context adds 27 KB of FP16 cache per request. How the KV cache works.
| Context | KV cache, FP16 | KV cache, FP8 | Total at Q4_K_M |
|---|---|---|---|
| 4K tokens | 108 MB | 54 MB | 1,724 GB |
| 32K tokens | 864 MB | 432 MB | 1,724 GB |
| 128K tokens | 3.38 GB | 1.69 GB | 1,727 GB |
| 1M tokens | 27.0 GB | 13.5 GB | 1,753 GB |
Which GPUs can run Kimi K3
With 8,192 tokens of context. Several GPUs means one tensor-parallel group of 2, 4 or 8 cards.
| GPU | Memory | Q4_K_M | FP8 |
|---|---|---|---|
| RTX 3060 | 12 GB | Needs more than 8 | Needs more than 8 |
| RTX 4060 Ti 16GB | 16 GB | Needs more than 8 | Needs more than 8 |
| RTX 3090 / 4090 | 24 GB | Needs more than 8 | Needs more than 8 |
| RTX 5090 | 32 GB | Needs more than 8 | Needs more than 8 |
| A100 40GB | 40 GB | Needs more than 8 | Needs more than 8 |
| Mac, 64 GB unified memory about 75% of it is usable by the GPU by default | 48 GB | Needs more than 8 | Needs more than 8 |
| L40S / RTX 6000 Ada | 48 GB | Needs more than 8 | Needs more than 8 |
| A100 / H100 80GB | 80 GB | Needs more than 8 | Needs more than 8 |
| Mac, 128 GB unified memory about 75% of it is usable by the GPU by default | 96 GB | Needs more than 8 | Needs more than 8 |
| H200 | 141 GB | Needs more than 8 | Needs more than 8 |
| B200 | 180 GB | Needs more than 8 | Needs more than 8 |
How fast Kimi K3 writes
Tokens per second for one request with 8,192 tokens of context, estimated from memory bandwidth and the parameters read per token. A dash means it does not fit on one card. Try other settings in the speed calculator.
| Hardware | Bandwidth | Q4_K_M | FP8 |
|---|---|---|---|
| RTX 3060 12GB | 360 GB/s | — | — |
| RTX 4090 | 1,008 GB/s | — | — |
| RTX 5090 | 1,792 GB/s | — | — |
| M4 Max Mac (128 GB) | 546 GB/s | — | — |
| M3 Ultra Mac Studio (512 GB) | 819 GB/s | — | — |
| H100 SXM | 3,350 GB/s | — | — |
| H200 | 4,800 GB/s | — | — |
Longest context on one GPU
How many tokens of context fit on a single card with one request and an FP16 KV cache. "Full" means the model's whole context window fits.
| GPU | Memory | Q4_K_M | Q8_0 | FP8 |
|---|---|---|---|---|
| RTX 3060 | 12 GB | No | No | No |
| RTX 4060 Ti 16GB | 16 GB | No | No | No |
| RTX 3090 / 4090 | 24 GB | No | No | No |
| RTX 5090 | 32 GB | No | No | No |
| A100 40GB | 40 GB | No | No | No |
| Mac, 64 GB unified memory | 48 GB | No | No | No |
| L40S / RTX 6000 Ada | 48 GB | No | No | No |
| A100 / H100 80GB | 80 GB | No | No | No |
| Mac, 128 GB unified memory | 96 GB | No | No | No |
| H200 | 141 GB | No | No | No |
| B200 | 180 GB | No | No | No |
Model details
- Parameters
- 2.78T (2,779,931,837,184)
- Experts
- 896 routed experts, all loaded
- Active per token
- 104B
- Layers
- 24 of its 93 layers use multi-head latent attention and 69 are linear-attention layers with no growing cache
- Attention cache
- 576 values per token (compressed latent)
- Context length
- 1,048,576 tokens
- Published weights
- 1,454 GB (4.5 bits/weight)
- On Hugging Face
- moonshotai/Kimi-K3
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Numbers read from the model files on Hugging Face on .