Can Kimi Linear 48B A3B run on NVIDIA A100 40GB?

YES — Tight Fit

A83Great

○Estimated from fit model

Kimi Linear 48B A3B needs ~36.6 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~36 tok/s.

Runtime: vLLMCapacity: TightBandwidth: 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) — 36.6 GB, 35.7 tok/s, Tight fit

36.6 GB required40.0 GB available

92% VRAM used

Fit status

Tight fit

Decode

35.7 tok/s

TTFT

5425 ms

Safe context

75K

Memory

36.6 GB / 40.0 GB

Memory breakdown

Weights29.3 GB

KV Cache0.9 GB

Runtime2.4 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsKimi Linear 48B A3B on NVIDIA A100 40GB
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: 35.7 tok/s decode · 5.4s TTFT (warm) · 89 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	Tight fit	35.7 tok/s	2959 ms	75K
Coding	A	Tight fit	35.7 tok/s	5425 ms	75K
Agentic Coding	A	Tight fit	35.7 tok/s	7890 ms	75K
Reasoning	A	Tight fit	35.7 tok/s	6411 ms	75K
RAG	A	Tight fit	35.7 tok/s	9863 ms	75K

Quantization options

How Kimi Linear 48B A3B (48B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	18.7 GB	Low	A81
Q3_K_S	3	23.5 GB	Low	A81
NVFP4	4	26.9 GB	Medium	A81
Q4_K_MBest for your GPU	4	29.3 GB	Medium	A80
Q5_K_M	5	34.6 GB	High	F0
Q6_K	6	39.4 GB	High	F0
Q8_0	8	51.4 GB	Very High	F0
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

Frequently asked questions

See all results for NVIDIA A100 40GB See all hardware for Kimi Linear 48B A3B

URL: https://willitrunai.com/can-run/kimi-linear-48b-a3b-on-a100-40gb

⇱ Kimi Linear 48B A3B on NVIDIA A100 40GB? TIGHT FIT