Kimi K2.7 Code — Hardware Requirements & GPU Compatibility
VisionCode
Kimi-K2.7-Code is Moonshot AI's coding-focused agentic model, built on top of Kimi K2.6 with improvements aimed at long-horizon software engineering workflows and reduced reasoning-token usage. It is a Mixture-of-Experts model with about 1 trillion total parameters and roughly 32 billion active per token, and it also includes a vision encoder, so it can take image input alongside code and text while working through complex, multi-step coding tasks. The model supports a 256K token context window and is released under a modified MIT license. At roughly 1 trillion total parameters, even 4-bit quantization needs several hundred gigabytes of memory, so this is squarely server or multi-GPU territory; most people will use it through a hosted API rather than running it locally.
104.8K downloads
1.4K likesSpecifications
- Publisher
- Moonshot AI
- Family
- Kimi K2
- Parameters
- 1026.9B
- Architecture
- KimiK25ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 163,840
- Release Date
- 2026-06-11
- License
- Other
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HuggingFace
How Much VRAM Does Kimi K2.7 Code Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 440.3 GB | 895.2 GB | 436.42 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 504.5 GB | 959.4 GB | 500.60 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 620.0 GB | 1074.9 GB | 616.13 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 735.5 GB | 1190.4 GB | 731.65 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 851.1 GB | 1306.0 GB | 847.18 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1030.8 GB | 1485.7 GB | 1026.88 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Kimi K2.7 Code?
Q4_K_M · 620.0 GBKimi K2.7 Code (Q4_K_M) requires 620.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 807+ GB is recommended. Using the full 262K context window can add up to 454.9 GB, bringing total usage to 1074.9 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Kimi K2.7 Code?
Q4_K_M · 620.0 GB2 devices with unified memory can run Kimi K2.7 Code, including NVIDIA DGX H100.
Decent
— Enough memory, may be tightWhere to Download Kimi K2.7 Code
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Kimi K2.7 Code need?
Kimi K2.7 Code requires 620.0 GB of VRAM at Q4_K_M, or 2057.6 GB at BF16. Full 262K context adds up to 454.9 GB (1074.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1026.9B × 4.8 bits ÷ 8 = 616.1 GB
KV Cache + Overhead ≈ 3.9 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 458.8 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M620.0 GBQ4_K_M + full context1074.9 GB- Can NVIDIA GeForce RTX 5090 run Kimi K2.7 Code?
No — Kimi K2.7 Code requires at least 286.3 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Kimi K2.7 Code?
For Kimi K2.7 Code, Q4_K_M (620.0 GB) offers the best balance of quality and VRAM usage. Q5_K_M (735.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 286.3 GB.
VRAM requirement by quantization
IQ2_XXS286.3 GBQ2_K440.3 GBIQ4_XS555.8 GBQ4_K_M ★620.0 GBQ6_K851.1 GBBF162057.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Kimi K2.7 Code on a Mac?
Kimi K2.7 Code requires at least 286.3 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Kimi K2.7 Code locally?
Yes — Kimi K2.7 Code can run locally on consumer hardware. At Q4_K_M quantization it needs 620.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- What's the download size of Kimi K2.7 Code?
At Q4_K_M, the download is about 616.13 GB. The full-precision BF16 version is 2053.76 GB. The smallest option (IQ2_XXS) is 282.39 GB.
- Which GPUs can run Kimi K2.7 Code?
No single consumer GPU has enough VRAM to run Kimi K2.7 Code at Q4_K_M (620.0 GB). Multi-GPU or professional hardware is required.
- Which devices can run Kimi K2.7 Code?
2 devices with unified memory can run Kimi K2.7 Code at Q4_K_M (620.0 GB), including NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.