Can Kimi Linear 48B A3B run on NVIDIA A16 64GB?

YES — Runs Great

A81Great

○Estimated from fit model

Kimi Linear 48B A3B needs ~39.0 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~13 tok/s.

Runtime: vLLMCapacity: RoomyBandwidth: MediumStack: 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) — 39.0 GB, 12.8 tok/s, Runs well

39.0 GB required64.0 GB available

61% VRAM used

Fit status

Runs well

Decode

12.8 tok/s

TTFT

15141 ms

Safe context

447K

Memory

39.0 GB / 64.0 GB

Memory breakdown

Weights29.3 GB

KV Cache0.9 GB

Runtime2.4 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsKimi Linear 48B A3B on NVIDIA A16 64GB
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: 12.8 tok/s decode · 15.1s TTFT (warm) · 32 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	12.8 tok/s	8258 ms	447K
Coding	A	Runs well	12.8 tok/s	15141 ms	447K
Agentic Coding	A	Runs well	12.8 tok/s	22023 ms	447K
Reasoning	A	Runs well	12.8 tok/s	17893 ms	447K
RAG	A	Runs well	12.8 tok/s	27528 ms	447K

Quantization options

How Kimi Linear 48B A3B (48B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	18.7 GB	Low	A76
Q3_K_S	3	23.5 GB	Low	A77
NVFP4	4	26.9 GB	Medium	A78
Q4_K_M	4	29.3 GB	Medium	A78
Q5_K_M	5	34.6 GB	High	A80
Q6_K	6	39.4 GB	High	A80
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 A16 64GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 2.5 VL 72B	72B	S	9.3 tok/s
👁 Alibaba Qwen3-Coder-Next	80B	S	24 tok/s
👁 Meta Llama 3.3 70B	70B	A	9.5 tok/s

Frequently asked questions

See all results for NVIDIA A16 64GB See all hardware for Kimi Linear 48B A3B

URL: https://willitrunai.com/can-run/kimi-linear-48b-a3b-on-a16-64gb

⇱ Kimi Linear 48B A3B on NVIDIA A16 64GB? YES