Kimi K2.7 Code — Hardware Requirements & GPU Compatibility
VisionCodeFunctions
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.
Kimi K2.7 Code runs on a multi-GPU server
Q4_K_M needs ~617.6 GB · about 5× H200 SXM 141 GB · or Mac Studio (M3 Ultra, 512GB) at Q3_K_M (~25 tok/s)
Macs from 512 GB (Q3_K_M · ~25 tok/s)
Score 118.1#19 of 106 open#16 of 102 that fit ≤640 GBOther
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
118.1
- 90% range 115.2–120.9
- #19 of 106 open models
- #16 of 102 that fit ≤640 GB
Run Kimi K2.7 Code
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve moonshotai/Kimi-K2.7-Code --max-model-len 16384 --tensor-parallel-size 8Size the nodes from the table above
Download: Kimi-K2.7-Code-Q4_K_M.gguf616.13 GBunsloth
Ways to runOther ways
each opens step-by-stepRun in cloud
Rent 5× H200 SXM · Q4_K_M
- Estimated hourly price
- ~$17.95/hr
- est., whole server
- GPUs
- 5 GPUs
- 705 GB of VRAM
$5+ bonus for new users
How Much VRAM Does Kimi K2.7 Code Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 286.3 GB | 285 GB | 2-bit · i-quant extreme compression — Importance-weighted 2-bit, extreme compression — significant quality loss | Runs on Instinct MI350X 288 GB | |
| 324.8 GB | 323.5 GB | 2-bit · i-quant small — Importance-weighted 2-bit, small | Runs on 5× H100 SXM 80 GB | |
| 440.3 GB | 439 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Runs on 7× H100 SXM 80 GB | |
| 504.5 GB | 503.2 GB | 3-bit medium — 3-bit medium quantization | Runs on 7× H100 SXM 80 GB | |
| 620.0 GB | 618.7 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Runs on 5× H200 SXM 141 GB | |
| 735.5 GB | 734.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Runs on 6× H200 SXM 141 GB | |
| 851.1 GB | 849.8 GB | 6-bit, very good — 6-bit quantization, very good quality | Runs on 7× H200 SXM 141 GB | |
| 1030.8 GB | 1029.5 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Runs on 6× B200 192 GB |
Selected Q4_K_M
4-bit medium quantization617.6GBat 16K context
- Weights
- 616.1 GB
- KV cache
- 1.2 GB
- Overhead
- 0.3 GB
Server-class: ~617.6 GB at 16K context · about 5× H200 SXM 141 GB
Which Hardware 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 14.8 GB, bringing total usage to 634.9 GB. No consumer GPU has enough memory.
Needs server-class hardware
Kimi K2.7 Code runs on a multi-GPU server · Q4_K_M needs ~617.6 GB
| Hardware | Quant | Memory | Speed | Context | Hardware page | |
|---|---|---|---|---|---|---|
| Mac 512 GB · Mac Studio (M3 Ultra, 512GB) | Q3_K_M | 502.1 GB | ~25 tok/s | 64K ctx | Mac Studio → | Q3_K_M · 502.1 GB · ~25 tok/s · 64K ctx |
| 8× H100 SXM 80 GB · 640 GB | IQ4_XS | 553.4 GB | — | 256K ctx | H100 SXM → | IQ4_XS · 553.4 GB · 256K ctx |
| 5× H200 SXM 141 GB · 705 GB | Q4_K_M | 617.6 GB | — | 128K ctx | H200 SXM → | Q4_K_M · 617.6 GB · 128K ctx |
| 4× B200 192 GB · 768 GB | Q4_K_M | 617.6 GB | — | 256K ctx | B200 → | Q4_K_M · 617.6 GB · 256K ctx |
Also: NVIDIA DGX A100 640GB · NVIDIA DGX H100
By VRAM tier · typical GPUs and Macs at Q4_K_M
Consumer GPUs (up to 32 GB) and Macs up to 256 GB: too small for this model (needs ≥ 283.8 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
How good is Kimi K2.7 Code?
The llmrun Score merges 19 public benchmark boards into a single figure, where 100 represents the average for open models.
Score vs VRAM in the ≤640 GB tier
- Kimi K2.7 Code
- Other models
- Best Score for its VRAM
#16 of 102 that fit ≤640 GB
Each dot is a model at the best quant that fits ≤640 GB. The line steps up to the best Score available at each amount of VRAM; a dot below it is beaten by a model that needs no more memory.
Full ≤640 GB ranking →Benchmarks
The 6 boards that weigh most in the llmrun Score, of 21 with a result for this model; badges show who ran the test.
- GPQA Diamond
Epoch AI · Jun 2026Independent
Independent87.9%
#17/100 open
- LiveBench Coding
LiveBench · Jun 2026Independent
Independent74.0
#12/20 open
- LiveBench Reasoning
LiveBench · Jun 2026Independent
Independent82.8
#10/20 open
- AIME 2024/2025
Epoch AI · Jun 2026Independent
Independent95.6%
#9/88 open
- FrontierMath
Epoch AI · Jun 2026Independent
Independent54.0%
#12/19 open
- LMArena WebDev
LMArena · Oct 2026Arena
Arena1473.5
#21/55 open
In the ≤640 GB class
102 models fit ≤640 GBSame Score, only models that fit ≤640 GB at their best quant: this model's peers by memory need.
- #1
DeepSeek V4.1 Flash 1017DeepSeek
134.4
458.4 GB
- #2
MiMo V2.6 Pro RLXiaomi
133.3
614.1 GB
- #3
MiMo V2.6 Flash RLXiaomi
131.9
186.5 GB
- #4
GLM 5.3Z.ai
130.1
453.8 GB
- #5
DeepSeek V4.1 FlashDeepSeek
129.4
458.4 GB
- #6
Step 5 PreviewStepFun
128.0
407.5 GB
Alternatives to Kimi K2.7 Code
Kimi K2 family · 8 models →Official models with an llmrun Score, each answering one question; sizes are each model's default quant (usually Q4_K_M) with a 16K-token context.
Best alternative at this size
MiMo V2.6 Pro RL
Xiaomi
- 15.2 higher Score
- 3.5 GB less VRAM
Score 133.3 · 614.1 GB
Fits in less VRAM
DeepSeek V4.1 Flash 1017
DeepSeek
- 16.3 higher Score
- 159.2 GB less VRAM
Score 134.4 · 458.4 GB
Other vendor
GLM 5.3
Z.ai
- 12 higher Score
- 163.8 GB less VRAM
Score 130.1 · 453.8 GB
Similar models
10| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| DeepSeek V4.1 FlashDeepSeek | 129.4 | 458.4 GB | —needs > 24 GB | similar score |
| DeepSeek V4 Pro 0813DeepSeek | 129.2 | 990.8 GB | —needs > 24 GB | similar score |
| Step 5 PreviewStepFun | 128.0 | 407.5 GB | —needs > 24 GB | similar score |
| Muse Spark Open 816B A42BMeta | 126.8 | 551.9 GB | —needs > 24 GB | similar score |
| GLM 5.2Z.ai | 122.9 | 453.8 GB | —needs > 24 GB | similar score |
| Qwen3.8 27BAlibaba | 122.0 | 17.9 GB | ~38 | similar score |
| Kimi K2.6Moonshot AI | 120.9 | 617.6 GB | —needs > 24 GB | similar score |
| Inkling SmallThinking Machines | 119.6 | 162.7 GB | —needs > 24 GB | similar score |
| Beam 1.0 501B A23Breflection | 119.5 | 304 GB | —needs > 24 GB | similar score |
| GLM 5.1Z.ai | 117.8 | 454.1 GB | —needs > 24 GB | similar score |
Fine-tunes & variants(2)
Specifications
- Publisher
- Moonshot AI
- Family
- Kimi K2
- Parameters
- 1026.9B
- Active parameters
- 33.3B per token
- Architecture
- KimiK25ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 163,840
- Release Date
- 2026-06-11
- License
- Other
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 14.8 GB (634.9 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 18.8 GB (at full 262K context)
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run Kimi K2.7 Code on a Mac?
Yes, but only at lower quantizations. The smallest Mac that can run Kimi K2.7 Code is Mac Studio (M3 Ultra, 512GB) at Q3_K_M; 1 of the 146 Macs we list can run it at some quantization. For Q4_K_M (620.0 GB) you need a Mac with more unified memory.
Can I run Kimi K2.7 Code locally?
Yes — Kimi K2.7 Code can run locally on consumer hardware. 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?
Kimi K2.7 Code requires at least 620.0 GB at Q4_K_M, which exceeds the usable memory of every unified-memory device we list (the largest has 508.5 GB).