Can Kimi Linear 48B A3B run on Quadro RTX 8000 48GB?

YES — Runs Great

A83Great

Estimated from fit model

Kimi Linear 48B A3B needs ~37.4 GB VRAM. Quadro RTX 8000 48GB has 48.0 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) — 37.4 GB, 12.7 tok/s, Runs well

37.4 GB required48.0 GB available

78% VRAM used

Fit status

Runs well

Decode

12.7 tok/s

TTFT

15282 ms

Safe context

199K

Memory

37.4 GB / 48.0 GB

Memory breakdown

Weights29.3 GB

KV Cache0.9 GB

Runtime2.4 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsKimi Linear 48B A3B on Quadro RTX 8000 48GB
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: 12.7 tok/s decode · 15.3s TTFT (warm) · 32 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	12.7 tok/s	8335 ms	199K
Coding	A	Runs well	12.7 tok/s	15282 ms	199K
Agentic Coding	A	Runs well	12.7 tok/s	22228 ms	199K
Reasoning	A	Runs well	12.7 tok/s	18060 ms	199K
RAG	A	Runs well	12.7 tok/s	27785 ms	199K

Quantization options

How Kimi Linear 48B A3B (48B params) fits at each quantization level on Quadro RTX 8000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	18.7 GB	Low	A78
Q3_K_S	3	23.5 GB	Low	A80
NVFP4	4	26.9 GB	Medium	A80
Q4_K_M	4	29.3 GB	Medium	A80
Q5_K_MBest for your GPU	5	34.6 GB	High	A80
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

Can Quadro RTX 8000 48GB run Kimi Linear 48B A3B?

Yes, Quadro RTX 8000 48GB can run Kimi Linear 48B A3B with a A grade (Runs well). Expected decode speed: 12.7 tok/s.

How much VRAM does Kimi Linear 48B A3B need?

Kimi Linear 48B A3B (48B parameters) requires approximately 37.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Kimi Linear 48B A3B?

The recommended quantization for Kimi Linear 48B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Kimi Linear 48B A3B run at on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Kimi Linear 48B A3B achieves approximately 12.7 tokens per second decode speed with a time-to-first-token of 15282ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run Kimi Linear 48B A3B for coding?

For coding workloads, Kimi Linear 48B A3B on Quadro RTX 8000 48GB receives a A grade with 12.7 tok/s and 199K context.

What context window can Kimi Linear 48B A3B use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Kimi Linear 48B A3B can safely use up to 199K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.

See all results for Quadro RTX 8000 48GB See all hardware for Kimi Linear 48B A3B

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