Can Kimi Linear 48B A3B run on NVIDIA H20 96GB?

YES — Runs Great

A84Great

○Estimated from fit model

Kimi Linear 48B A3B needs ~42.2 GB VRAM. NVIDIA H20 96GB has 96.0 GB. With Q4_K_M quantization, expect ~89 tok/s.

Runtime: vLLMCapacity: RoomyBandwidth: HighStack: OptimizedBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 42.2 GB, 88.5 tok/s, Runs well

42.2 GB required96.0 GB available

44% VRAM used

Fit status

Runs well

Decode

88.5 tok/s

TTFT

2187 ms

Safe context

944K

Memory

42.2 GB / 96.0 GB

Memory breakdown

Weights29.3 GB

KV Cache0.9 GB

Runtime2.4 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsKimi Linear 48B A3B on NVIDIA H20 96GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
 if len(arr) <= 1:
 return arr
 pivot = arr[len(arr) // 2]
 left = [x for x in arr if x < pivot]
 middle = [x for x in arr if x == pivot]
 right = [x for x in arr if x > pivot]
 return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
 """Sort a list using the quicksort algorithm.

 Args:
 arr: List of integers to sort.

 Returns:
 A new sorted list.
 """
 if len(arr) <= 1:
 return arr
 pivot = arr[len(arr) // 2]
 left = [x for x in arr if x < pivot]
 middle = [x for x in arr if x == pivot]
 right = [x for x in arr if x > pivot]
 return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 88.5 tok/s decode · 2.2s TTFT (warm) · 221 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	88.5 tok/s	1193 ms	944K
Coding	A	Runs well	88.5 tok/s	2187 ms	944K
Agentic Coding	A	Runs well	88.5 tok/s	3181 ms	944K
Reasoning	A	Runs well	88.5 tok/s	2585 ms	944K
RAG	A	Runs well	88.5 tok/s	3976 ms	944K

Quantization options

How Kimi Linear 48B A3B (48B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	18.7 GB	Low	A73
Q3_K_S	3	23.5 GB	Low	A74
NVFP4	4	26.9 GB	Medium	A74
Q4_K_M	4	29.3 GB	Medium	A75
Q5_K_M	5	34.6 GB	High	A76
Q6_K	6	39.4 GB	High	A77
Q8_0Best for your GPU	8	51.4 GB	Very High	A80
F16	16	98.4 GB	Maximum	F0

Get started

Copy-paste commands to run Kimi Linear 48B A3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "moonshotai/Kimi-Linear-48B-A3B-Instruct" \
 --hf-file "Kimi-Linear-48B-A3B-Instruct-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Your hardware

More models your NVIDIA H20 96GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 122B A10B	122B	S	71.3 tok/s
👁 Mistral Mistral Small 4 119B	119B	S	77.3 tok/s
👁 OpenAI GPT-OSS 120B	117B	S	39.5 tok/s
👁 Cohere Command A 111B	111B	S	41.8 tok/s
👁 Alibaba Qwen 2.5 VL 72B	72B	S	64.2 tok/s

Frequently asked questions

See all results for NVIDIA H20 96GB See all hardware for Kimi Linear 48B A3B

URL: https://willitrunai.com/can-run/kimi-linear-48b-a3b-on-h20-96gb

⇱ Kimi Linear 48B A3B on NVIDIA H20 96GB? YES