Kimi K2.7-Code: VRAM Requirements & Local Setup Guide 2026
Moonshot AI's code-focused model. Exact parameter count not yet published. Strong coding capabilities with multimodal support.
Quick Facts
- Provider
- Moonshot AI
- Architecture
- Mixture-of-Experts (MoE)
- Total Parameters
- 1000B
- Active Params/Token
- 32B
- Context Window
- 262K tokens
- License
- open
- Modalities
- text, image
- HuggingFace
- moonshotai/Kimi-K2.7-Code
VRAM Requirements by Quantization & Context Length
All values in gigabytes (GB). Calculated using the KV-cache formula with model-specific head fractions. “–” means the context length exceeds this model’s maximum context window. Lower quantization = less VRAM but slightly reduced output quality.
| Quantization | 4K ctx | 8K ctx | 32K ctx | 128K ctx |
|---|---|---|---|---|
| Q4_K_M | 569.9 GB | 570.1 GB | 571.8 GB | 578.2 GB |
| Q5_K_M | 665.3 GB | 665.6 GB | 667.2 GB | 673.6 GB |
| Q6_K | 766.6 GB | 766.9 GB | 768.5 GB | 774.9 GB |
| Q8_0 | 991.3 GB | 991.6 GB | 993.2 GB | 999.6 GB |
| FP16 | 1864.4 GB | 1864.7 GB | 1866.3 GB | 1872.7 GB |
Weights = 1000B params × GGUF bits per weight (Q4_K_M ≈ 4.9) — all experts are loaded; plus runtime overhead; plus fp16 KV cache from config.json: MLA latent of 576 values per layer — ~69 KB per token at short context. Same engine as the Can I Run LLM calculator.
Best GPU for Kimi K2.7-Code by Budget
Recommendations assume Q4_K_M quantization at 4K context unless stated otherwise. Higher-end quantizations or longer context windows require more VRAM — consult the table above.
VRAM requirements pending — check back once parameter count is published.
Benchmark Scores
Scores reported by Moonshot AI or verified third-party evaluations. Higher is better for all benchmarks except where noted.
| Benchmark | Score |
|---|---|
| AIME 2024–25 (Epoch AI) | 95.6 |
| SimpleQA Verified | 36.5 |
| GPQA Diamond | 87.9 |
How to Run Kimi K2.7-Code Locally
Before downloading, verify your GPU has sufficient VRAM using the table above. Insufficient VRAM will cause the model to fall back to CPU offloading, which is significantly slower.
HuggingFace Hub
Kimi K2.7-Code is available on HuggingFace. Download with:
pip install huggingface_hub huggingface-cli download moonshotai/Kimi-K2.7-Code
Strengths and Use Cases
Optimised specifically for coding tasks. Multimodal input support. Full parameter details pending official release.
When choosing a local LLM, Kimi K2.7-Code is worth considering if your workload aligns with its design goals. As a Mixture-of-Experts (MoE) architecture from Moonshot AI, it offers a distinct trade-off between compute efficiency and capability. With MoE architecture, only a fraction of parameters are active at inference time, dramatically reducing VRAM requirements compared to a dense model of equivalent total size. Whether you are building a local AI pipeline, experimenting with self-hosted chat, or running automated workflows, understanding this model's hardware envelope helps you plan infrastructure realistically.
For deployment, start with the Q4_K_M quantization unless you have headroom for higher precision. Q5_K_M and Q6_K offer improved output quality at the cost of additional VRAM. FP16 is generally only practical on high-VRAM workstation GPUs or cloud instances. Always verify context length requirements before selecting a quantization — longer context windows multiply KV-cache memory consumption significantly, as shown in the VRAM table above.
Ready to Check Your Hardware?
Use our interactive calculator to see exactly whether your GPU can run Kimi K2.7-Code — and at what quantization level.
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