Kimi K2 Instruct — Hardware Requirements & GPU Compatibility
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Kimi K2 Instruct is Moonshot AI's massive Mixture-of-Experts model, weighing in at over one trillion total parameters. It represents one of the largest open-weight models available, delivering frontier-class performance across reasoning, coding, and multilingual tasks through its sparse MoE architecture that activates only a fraction of its full parameter count per token. Running Kimi K2 locally is an extreme undertaking, requiring professional multi-GPU setups with hundreds of gigabytes of combined VRAM even at aggressive quantization. This model is best suited for research labs, enterprise deployments, or enthusiasts with access to server-grade hardware who want to explore trillion-parameter-scale inference.
Kimi K2 Instruct runs on a multi-GPU server
Q4_K_M needs ~617.3 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 100.1#57 of 106 open#53 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 →
100.1
- 90% range 96.8–103.4
- #57 of 106 open models
- #53 of 102 that fit ≤640 GB
Run Kimi K2 Instruct
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-Instruct --max-model-len 16384 --tensor-parallel-size 8Size the nodes from the table above
Download: Q4_K_M/Kimi-K2-Instruct-Q4_K_M-00001-of-00013.gguf620.78 GB + 12 partsunsloth
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 Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 440.1 GB | 438.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Runs on 7× H100 SXM 80 GB | |
| 452.9 GB | 451.7 GB | 3-bit small — 3-bit small quantization | Runs on 7× H100 SXM 80 GB | |
| 504.3 GB | 503 GB | 3-bit medium — 3-bit medium quantization | Runs on 7× H100 SXM 80 GB | |
| 517.1 GB | 515.8 GB | 4-bit · legacy — 4-bit legacy quantization | Runs on 8× H100 SXM 80 GB | |
| 619.7 GB | 618.5 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Runs on 5× H200 SXM 141 GB | |
| 735.2 GB | 733.9 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Runs on 6× H200 SXM 141 GB | |
| 850.7 GB | 849.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Runs on 7× H200 SXM 141 GB | |
| 1030.3 GB | 1029 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Runs on 6× B200 192 GB |
Selected Q4_K_M
4-bit medium quantization617.3GBat 16K context
- Weights
- 615.9 GB
- KV cache
- 1.2 GB
- Overhead
- 0.3 GB
Server-class: ~617.3 GB at 16K context · about 5× H200 SXM 141 GB
Which Hardware Can Run Kimi K2 Instruct?
Q4_K_M · 619.7 GBKimi K2 Instruct (Q4_K_M) requires 619.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 806+ GB is recommended. Using the full 131K context window can add up to 5.6 GB, bringing total usage to 625.4 GB. No consumer GPU has enough memory.
Needs server-class hardware
Kimi K2 Instruct runs on a multi-GPU server · Q4_K_M needs ~617.3 GB
| Hardware | Quant | Memory | Speed | Context | Hardware page | |
|---|---|---|---|---|---|---|
| Mac 512 GB · Mac Studio (M3 Ultra, 512GB) | Q3_K_M | 501.8 GB | ~25 tok/s | 64K ctx | Mac Studio → | Q3_K_M · 501.8 GB · ~25 tok/s · 64K ctx |
| 8× H100 SXM 80 GB · 640 GB | IQ4_XS | 553.2 GB | — | 128K ctx | H100 SXM → | IQ4_XS · 553.2 GB · 128K ctx |
| 5× H200 SXM 141 GB · 705 GB | Q4_K_M | 617.3 GB | — | 128K ctx | H200 SXM → | Q4_K_M · 617.3 GB · 128K ctx |
| 4× B200 192 GB · 768 GB | Q4_K_M | 617.3 GB | — | 128K ctx | B200 → | Q4_K_M · 617.3 GB · 128K 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 ≥ 283.7 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
How good is Kimi K2 Instruct?
The llmrun Score merges 10 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 Instruct
- Other models
- Best Score for its VRAM
#53 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 11 with a result for this model; badges show who ran the test.
- SWE-bench Verified
SWE-bench · Jul 2025Independent
Independent65.4%
#8/20 open
- LMArena Text
LMArena · Oct 2026Arena
Arena1417.8
#36/188 open
- Terminal-Bench
Epoch AI · Jul 2025Lab-reported
Lab-reported27.8%
#10/16 open
- MMLU-Pro
TIGER-LabIndependent
Independent81.0%
#21/146 open
- SWE-bench Bash Only
SWE-bench · Aug 2025Independent
Independent43.8%
#7/9 open
- Aider Polyglot
Epoch AI · Jul 2025Independent
Independent59.1%
#5/18 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 Instruct
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
- 33.2 higher Score
- 3.2 GB less VRAM
Score 133.3 · 614.1 GB
Fits in less VRAM
DeepSeek V4.1 Flash 1017
DeepSeek
- 34.3 higher Score
- 158.9 GB less VRAM
Score 134.4 · 458.4 GB
Other vendor
GLM 5.3
Z.ai
- 30 higher Score
- 163.5 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 |
| Kimi K2.6Moonshot AI | 120.9 | 617.6 GB | —needs > 24 GB | similar score |
| DeepSeek V3.2DeepSeek | 110.4 | 412.7 GB | —needs > 24 GB | similar score |
| NVIDIA Nemotron 3 Ultra 550B A55B BF16NVIDIA | 109.7 | 378.4 GB | —needs > 24 GB | similar score |
| DeepSeek V3.2 ExpDeepSeek | 109.0 | 412.7 GB | —needs > 24 GB | similar score |
| DeepSeek V3.1 TerminusDeepSeek | 107.3 | 412.2 GB | —needs > 24 GB | similar score |
Specifications
- Publisher
- Moonshot AI
- Family
- Kimi K2
- Parameters
- 1026.4B
- Active parameters
- 32.9B per token
- Architecture
- DeepseekV3ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 163,840
- Release Date
- 2025-07-11
- License
- Other
Frequently Asked Questions
How much VRAM does Kimi K2 Instruct need?
Kimi K2 Instruct requires 619.7 GB of VRAM at Q4_K_M, or 2056.7 GB at BF16. Full 131K context adds up to 5.6 GB (625.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1026.4B × 4.8 bits ÷ 8 = 615.8 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 ≈ 9.6 GB (at full 131K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run Kimi K2 Instruct?
No — Kimi K2 Instruct requires at least 286.1 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
What's the best quantization for Kimi K2 Instruct?
For Kimi K2 Instruct, Q4_K_M (619.7 GB) offers the best balance of quality and VRAM usage. Q5_K_S (709.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 286.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Kimi K2 Instruct on a Mac?
Yes, but only at lower quantizations. The smallest Mac that can run Kimi K2 Instruct 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 (619.7 GB) you need a Mac with more unified memory.
Can I run Kimi K2 Instruct locally?
Yes — Kimi K2 Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
What's the download size of Kimi K2 Instruct?
At Q4_K_M, the download is about 615.84 GB. The full-precision BF16 version is 2052.82 GB. The smallest option (IQ2_XXS) is 282.26 GB.
Which GPUs can run Kimi K2 Instruct?
No single consumer GPU has enough VRAM to run Kimi K2 Instruct at Q4_K_M (619.7 GB). Multi-GPU or professional hardware is required.
Which devices can run Kimi K2 Instruct?
Kimi K2 Instruct requires at least 619.7 GB at Q4_K_M, which exceeds the usable memory of every unified-memory device we list (the largest has 508.5 GB).