Can Kimi Linear 48B A3B run on NVIDIA V100 32GB?

BARELY — Tight on Memory

A70Great

Estimated from fit model

Kimi Linear 48B A3B needs ~35.2 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: TransformersCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload

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) — 35.2 GB, 15.0 tok/s, Very compromised (needs ~2.7 GB host RAM)

35.2 GB required32.0 GB available

110% VRAM needed

3.2 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.7 GB host RAM)

Decode

15.0 tok/s

TTFT

12903 ms

Safe context

Memory

35.2 GB / 32.0 GB

Offload

10%

Memory breakdown

Weights29.3 GB

KV Cache0.9 GB

Runtime1.8 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsKimi Linear 48B A3B on NVIDIA V100 32GB
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: 15.0 tok/s decode · 12.9s TTFT (warm) · 38 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Very compromised	15.3 tok/s	6886 ms	4K
Coding	A	Very compromised (needs ~2.7 GB host RAM)	15.0 tok/s	12903 ms	4K
Agentic Coding	A	Very compromised (needs ~3.3 GB host RAM)	14.4 tok/s	19590 ms	4K
Reasoning	A	Very compromised (needs ~2.7 GB host RAM)	15.0 tok/s	15249 ms	4K
RAG	A	Very compromised (needs ~3.3 GB host RAM)	14.4 tok/s	24487 ms	4K

Quantization options

How Kimi Linear 48B A3B (48B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	18.7 GB	Low	A81
Q3_K_SBest for your GPU	3	23.5 GB	Low	A81
NVFP4	4	26.9 GB	Medium	F0
Q4_K_M	4	29.3 GB	Medium	F0
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

Can NVIDIA V100 32GB run Kimi Linear 48B A3B?

Yes, NVIDIA V100 32GB can run Kimi Linear 48B A3B with a A grade (Very compromised (needs ~2.7 GB host RAM)). Expected decode speed: 15.0 tok/s.

How much VRAM does Kimi Linear 48B A3B need?

Kimi Linear 48B A3B (48B parameters) requires approximately 35.2 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 NVIDIA V100 32GB?

On NVIDIA V100 32GB, Kimi Linear 48B A3B achieves approximately 15.0 tokens per second decode speed with a time-to-first-token of 12903ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Kimi Linear 48B A3B for coding?

For coding workloads, Kimi Linear 48B A3B on NVIDIA V100 32GB receives a A grade with 15.0 tok/s and 4K context.

What context window can Kimi Linear 48B A3B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Kimi Linear 48B A3B can safely use up to 4K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.

What should I upgrade first if Kimi Linear 48B A3B feels slow on NVIDIA V100 32GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for NVIDIA V100 32GB See all hardware for Kimi Linear 48B A3B

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