Kimi K2.7 Code
By Moonshot AI · China
Updated 2026-08-28
Overview
Kimi K2.7 Code is Moonshot AI's code-focused, multimodal variant of Kimi K2.7 at ~1059B parameters, with a 262K-token context and a very heavy ~614GB VRAM footprint at Q4.
When to pick this model
- Large-scale code generation and agentic coding workloads
- Coding agents that need multimodal (image + text) input
- Long-context codebases up to 262K tokens
- Multi-GPU server deployments chasing frontier-level code quality
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 614 GB |
| Q5_K_M | 752 GB |
| Q8_0 | 1133 GB |
| FP16 (no quantization) | 2118 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Kimi K2.7 Code is server-class even at Q4_K_M (614 GB). Stepping up to Q8_0 nearly doubles the footprint to 1133 GB, and unquantized FP16 weights take 2118 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Kimi K2.7 Code needs roughly 1377 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 1.5 tokens/sec on entry-level GPUs, on the order of 2.5 tokens/sec on a mid-range card, and up to 5 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Kimi K2.7 Code to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Kimi K2.7 Code |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 614 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 614 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 614 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 614 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 614 GB at Q4_K_M |
Which GPU should you buy to run Kimi K2.7 Code?
To run Kimi K2.7 Code locally at Q4, you need ~614 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
As an Amazon Associate, BestLLMfor earns from qualifying purchases, at no extra cost to you. It does not influence our independent rankings.
Strengths
- Specialized for code generation and understanding
- Multimodal support for image-plus-text input
- Extended context window up to 262K tokens
- Frontier scale at ~1059B parameters
Limitations
- Enormous footprint at ~614GB VRAM in Q4 — multi-GPU servers only
- Slow local throughput (~5 tok/s at Q4)
- Custom 'other' license — review terms before commercial use
Typical workloads
In our catalog grid, Kimi K2.7 Code is filed under Code Generation, Coding Agents, Long Context — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline); vision-language work — screenshots, charts, scanned documents.
The 256k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. It ships under the Autre (open-weights) license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: Multimodal model, ~1059B parameters · Kimi K2.7 series, code-focused · 262K-token context window
Training: A code-specialized variant of Kimi K2.7 from Moonshot AI. Training details not published.
A frontier-scale code model for teams with serious multi-GPU infrastructure, not a local dev-box option.
Quick start
ollama pull kimi-k2.7-codeOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
Similar models worth comparing
Frequently asked questions
How much VRAM does Kimi K2.7 Code need?
At the recommended Q4_K_M quantization, Kimi K2.7 Code needs about 614 GB of VRAM. Q8_0 takes 1133 GB, and unquantized FP16 weights take 2118 GB.
Can Kimi K2.7 Code run without a GPU?
Yes — with roughly 1377 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Kimi K2.7 Code support?
Kimi K2.7 Code supports a 256k-token context window (262,144 tokens).
Can I use Kimi K2.7 Code commercially?
Kimi K2.7 Code ships under the Autre (open-weights) license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Kimi K2.7 Code on consumer hardware?
Our compatibility engine estimates on the order of 2.5 tokens/sec on a mid-range GPU and up to 5 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Kimi K2.7 Code should I download first?
Start with Q4_K_M (614 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.