EDGE OSOPEN-WEIGHT LLM COMPARISON
Kimi K2.6 vs Kimi K2.7 CodeSpecs, VRAM and the hardware to run them
Kimi K2.7 Code is built on K2.6 with the same 1T-parameter design (32B active), 256K context, native INT4 weights and modified MIT license. The difference is tuning: K2.7 Code is trained for coding agents and, according to Moonshot, uses about 30% fewer reasoning tokens per task.
Parameters
1T vs 1T
Active per token
32B vs 32B
Context
256K vs 256K
Memory, 1 user
626 GB vs 626 GB
Which should you choose?
CHOOSE KIMI K2.6 IF
You want a general-purpose assistant and agent model for many kinds of work, not only code.
CHOOSE KIMI K2.7 CODE IF
Most of your workload is software engineering and coding agents. Hardware needs are the same, so it is a drop-in swap.
Still deciding between Kimi K2.6 and Kimi K2.7 Code?
Tell us your models, users and data. We will recommend a setup and run it inside your own building with Vanik Edge OS. Free, no obligation.
Kimi K2.6 vs Kimi K2.7 Code specs
From each developer’s model card. Green marks the lighter figure where less is better for running cost.
| Specification | Kimi K2.6 | Kimi K2.7 Code |
|---|---|---|
| Developer | Moonshot AI | Moonshot AI |
| Total parameters | 1T | 1T |
| Active per token | 32B | 32B |
| Design | Mixture of experts | Mixture of experts |
| Experts | 384 experts, 8 active per token, plus 1 shared | 384 experts, 8 active per token, plus 1 shared |
| Context window | 256K tokens | 256K tokens |
| Input and output | Text, image and video input, text output | Text, image and video input, text output |
| Layers | 61 layers (1 dense) | 61 layers (1 dense) |
| Attention | Multi-head latent attention (MLA) | Multi-head latent attention (MLA) |
| License | Modified MIT | Modified MIT |
| Released weight formats | INT4 (native) | INT4 (native) |
| Weights at INT4 / INT4 | About 595 GB | About 595 GB |
VRAM needed to run them
GPU memory at the lowest released precision. Each user needs their own KV cache, so memory grows with users and context length.
| GPU memory needed (INT4 / INT4) | Kimi K2.6 | Kimi K2.7 Code |
|---|---|---|
| 1 user, 8K context | About 626 GB | About 626 GB |
| 1 user, 128K context | About 635 GB | About 635 GB |
| 16 users, 8K context each | About 635 GB | About 635 GB |
| 64 users, 8K context each | About 663 GB | About 663 GB |
Which GPUs can run Kimi K2.6 and Kimi K2.7 Code
Smallest setup on each system, with 8K context per user. Green fits on one GPU or system, amber needs several in one server, grey needs more than one 8-GPU server.
| Hardware | Kimi K2.6: 1 user | Kimi K2.6: 16 users | Kimi K2.7 Code: 1 user | Kimi K2.7 Code: 16 users |
|---|---|---|---|---|
| NVIDIA DGX Spark 128 GB per system | No | No | No | No |
| NVIDIA L40S 48 GB per GPU | No | No | No | No |
| NVIDIA RTX PRO 6000 Blackwell 96 GB per GPU | 8 GPUs | 8 GPUs | 8 GPUs | 8 GPUs |
| NVIDIA H100 NVL 94 GB per GPU | 8 GPUs | 8 GPUs | 8 GPUs | 8 GPUs |
| NVIDIA H200 NVL 141 GB per GPU | 8 GPUs | 8 GPUs | 8 GPUs | 8 GPUs |
| AMD Instinct MI300X 192 GB per GPU | 4 GPUs | 4 GPUs | 4 GPUs | 4 GPUs |
| AMD Instinct MI325X 256 GB per GPU | 4 GPUs | 4 GPUs | 4 GPUs | 4 GPUs |
| NVIDIA DGX B200 1.44 TB per system | Yes, 1 system | Yes, 1 system | Yes, 1 system | Yes, 1 system |
Setups in amber or grey need several GPUs or servers working as one. Buying the GPUs is the easy part. Vanik Edge OS splits the model across them, keeps it running if a server fails and manages it all from one console. See how Edge OS clusters GPU servers
Which is faster?
On the same hardware they should generate tokens at similar speeds: each reads about 19 GB to 19 GB of weights per token. Token generation is limited mostly by memory bandwidth. Real throughput also depends on batching, the inference engine and the GPU.
FROM SIZING TO RUNNING
Sizing the hardware is the easy part.
Getting several GPUs to work as one system for a whole company is where most projects slow down. That is the part Vanik Edge OS does for you.
STEP 01 · THIS TOOL
Size it
Work out the GPU memory your models need and which GPUs or servers can hold them.
STEP 02 · VANIK EDGE OS
Cluster it
Edge OS joins your GPU servers into one cluster, splits a large model across them and keeps it running if a server fails. NVIDIA and AMD servers work side by side.
STEP 03 · VANIK EDGE OS
Run it
Your team gets AI apps with single sign-on and an audit trail, managed from one console, with your data staying inside your building.
Kimi K2.6 vs Kimi K2.7 Code questions
What is the difference between Kimi K2.6 and Kimi K2.7 Code?
Kimi K2.7 Code is built on K2.6 with the same 1T-parameter design (32B active), 256K context, native INT4 weights and modified MIT license. The difference is tuning: K2.7 Code is trained for coding agents and, according to Moonshot, uses about 30% fewer reasoning tokens per task.
Kimi K2.6 vs Kimi K2.7 Code: which is better to run on-premise?
Choose Kimi K2.6 if you want a general-purpose assistant and agent model for many kinds of work, not only code. Choose Kimi K2.7 Code if most of your workload is software engineering and coding agents. Hardware needs are the same, so it is a drop-in swap.
Which needs less VRAM, Kimi K2.6 or Kimi K2.7 Code?
Kimi K2.7 Code. For one user with 8K context it needs about 626 GB, versus about 626 GB for Kimi K2.6. For 16 users it needs about 635 GB versus 635 GB.
Which is faster, Kimi K2.6 or Kimi K2.7 Code?
On the same hardware they should generate tokens at similar speeds: each reads about 19 GB to 19 GB of weights per token. Token generation is limited mostly by memory bandwidth.
Can I run Kimi K2.6 locally?
Yes. At INT4 it needs about 626 GB of GPU memory for one user with 8K context, which fits on 8 × NVIDIA H100 NVL. A team of 16 needs about 635 GB: 8 × NVIDIA H100 NVL.
Can I run Kimi K2.7 Code locally?
Yes. At INT4 it needs about 626 GB of GPU memory for one user with 8K context, which fits on 8 × NVIDIA H100 NVL. A team of 16 needs about 635 GB: 8 × NVIDIA H100 NVL.
Can I use Kimi K2.6 and Kimi K2.7 Code commercially?
Kimi K2.6 is released under Modified MIT. Kimi K2.7 Code is released under Modified MIT. Check each license before a commercial deployment.
How are these numbers calculated?
Memory figures come from our VRAM calculator method: weights at the lowest released precision, plus the KV cache for each user’s context, plus about 5 to 10% runtime overhead, keeping 10% of each GPU free. Where a developer does not publish the cache size we estimate it from the architecture. These are planning estimates, not benchmarks.
Related comparisons
Sources: Hugging Face: moonshotai/Kimi-K2.6; Hugging Face: moonshotai/Kimi-K2.7-Code. Memory figures are planning estimates; real use depends on the inference engine and settings. Last checked November 12, 2026.