Kimi-K2.5 has 1026.9B parameters (33B active per token). In BF16 its weights alone take 1913 GiB; quantized to 4-bit, about 577 GiB. Each 32K-token request adds 2.14 GiB of KV cache. Cheapest way to run it today: 4 × AMD MI300X (192 GB) at $9.56 per hour.
Weights plus the KV cache of one request (BF16 cache), in GiB. Add 1 to 3 GiB for the inference engine itself.
Weights format
Weights only
+ 8K context
+ 32K context
+ 128K context
+ 256K context
INT4 (native)
554
555
556
563
571
BF16 / FP16
1913
1913
1915
1921
1930
FP8 / INT8
959
959
961
967
976
4-bit (GGUF Q4_K_M)
577
578
580
586
595
Kimi-K2.5 GGUF files
Exact size of each quantization in unsloth/Kimi-K2.5-GGUF (opens in a new tab), and the smallest GPU that runs it with llama.cpp, Ollama or LM Studio and an 8K-token context.
Quantization
File size
Smallest GPU
BF16
1912 GiB
More than 96 GB: see the calculator
UD-Q8_K_XL
1108 GiB
More than 96 GB: see the calculator
Q8_0
1016 GiB
More than 96 GB: see the calculator
UD-Q6_K_XL
817 GiB
More than 96 GB: see the calculator
Q6_K
785 GiB
More than 96 GB: see the calculator
UD-Q5_K_XL
681 GiB
More than 96 GB: see the calculator
Q5_K_M
679 GiB
More than 96 GB: see the calculator
Q5_K_S
658 GiB
More than 96 GB: see the calculator
Q4_1
599 GiB
More than 96 GB: see the calculator
UD-Q4_K_XL
579 GiB
More than 96 GB: see the calculator
Q4_K_M
579 GiB
More than 96 GB: see the calculator
Q4_K_S
543 GiB
More than 96 GB: see the calculator
Q4_0
541 GiB
More than 96 GB: see the calculator
IQ4_NL
539 GiB
More than 96 GB: see the calculator
IQ4_XS
510 GiB
More than 96 GB: see the calculator
UD-Q3_K_XL
457 GiB
More than 96 GB: see the calculator
Q3_K_M
456 GiB
More than 96 GB: see the calculator
Q3_K_S
413 GiB
More than 96 GB: see the calculator
UD-IQ3_XXS
386 GiB
More than 96 GB: see the calculator
UD-Q2_K_XL
349 GiB
More than 96 GB: see the calculator
Q2_K_L
348 GiB
More than 96 GB: see the calculator
Q2_K
348 GiB
More than 96 GB: see the calculator
UD-IQ2_M
322 GiB
More than 96 GB: see the calculator
UD-IQ2_XXS
304 GiB
More than 96 GB: see the calculator
UD-IQ1_M
280 GiB
More than 96 GB: see the calculator
UD-IQ1_S
257 GiB
More than 96 GB: see the calculator
UD-TQ1_0
223 GiB
More than 96 GB: see the calculator
Read every day from Hugging Face. Split files are added together; vision projectors are left out.
How many GPUs to run Kimi-K2.5
Fewest GPUs that fit the weights plus one 8K-token request. Data-center GPUs use vLLM (90% of memory usable), the others llama.cpp. Several GPUs split the model with tensor or pipeline parallelism.
GPU
INT4 (native)
BF16 / FP16
8-bit
4-bit
GeForce RTX 4090 24 GB
— (not supported on this GPU)
128 (several servers)
64 (several servers)
32 (several servers)
GeForce RTX 5090 32 GB
— (not supported on this GPU)
96 (several servers)
48 (several servers)
24 (several servers)
RTX PRO 6000 Blackwell 96 GB
— (not supported on this GPU)
24 (several servers)
16 (several servers)
7
Mac M5 Max 128 GB 96 GB usable
— (not supported on this GPU)
24 (several servers)
16 (several servers)
7
NVIDIA H100 80 GB
16 (several servers)
32 (several servers)
16 (several servers)
16 (several servers)
NVIDIA H200 141 GB
5
16 (several servers)
8
5
NVIDIA B200 (HGX) 180 GB
4
16 (several servers)
7
4
8-bit: FP8 with vLLM (INT8 on GPUs without FP8), Q8_0 with llama.cpp. 4-bit: AWQ / GPTQ with vLLM, Q4_K_M with llama.cpp, both counted at the Q4_K_M size. —: the official INT4 weights do not run there: llama.cpp needs a GGUF version.
Cheapest way to run Kimi-K2.5 today
For each GPU: the fewest cards that fit Kimi-K2.5 with one 8K-token request, the most faithful weight format at that count, and the cheapest on-demand price (2026-10-11).
On-demand prices from RunPod, Vast.ai, Verda and Azure, checked every hour. Serving many users needs more KV cache, so more memory: size it in the calculator. Some links are affiliate links: StudioTV may earn a commission, at no extra cost to you.
KV cache: how memory grows with context
Architecture: 61 layers · MLA (512 + 64 latent) on 61. Its cache stays compact because MLA stores one compressed latent per token and layer instead of full keys and values. Only 33B parameters are active per token, but all 1027B must sit in memory: VRAM depends on the total, speed on the active part.
Context per request
BF16 cache
FP8 cache
8K tokens
0.54 GiB
0.27 GiB
32K tokens
2.14 GiB
1.07 GiB
128K tokens
8.58 GiB
4.29 GiB
256K tokens
17.2 GiB
8.58 GiB
Kimi-K2.5 VRAM FAQ
How much VRAM does Kimi-K2.5 need?
In BF16 the weights alone take 1913 GiB (2054 GB). In FP8 that is 959 GiB, and about 577 GiB with 4-bit quantization (Q4_K_M); the official INT4 checkpoint is 554 GiB. Each request then adds KV cache: 0.54 GiB at 8K tokens and 2.14 GiB at 32K (BF16 cache).
Can Kimi-K2.5 run on a single RTX 4090 (24 GB)?
No. Even in 4-bit it needs 32 (several servers) RTX 4090s. In 4-bit it needs 16 (several servers) H100 GPUs.
What is the cheapest way to run Kimi-K2.5?
On 2026-10-11, the cheapest on-demand setup is 4 × AMD MI300X (192 GB) with INT4 weights on RunPod, at $9.56 per hour (about $6,978.80 per month). Next: 7 × RTX PRO 6000 Blackwell (96 GB) with Q4_K_M weights on Vast.ai, at $9.80 per hour. Sized for one 8K-token request; prices are checked every hour.
How many H100 GPUs does Kimi-K2.5 need?
With one request and an 8K-token context: 32 (several servers) in BF16, 16 (several servers) in FP8 and 16 (several servers) in 4-bit (vLLM, 90% of memory usable). Serving many users at once needs more memory for their KV caches: the calculator sizes that for you.
Kimi-K2.5 VRAM badge
For a model card or a README: the 4-bit size and the smallest GPU it fits on, linked to this page. Data as JSON: /api/models/kimi-k2-5.json.
Estimates, not guarantees: computed from the official config.json with the same engine as the LLM VRAM Calculator. Real usage depends on your engine version and settings.