Can Kimi Linear 48B A3B run on RTX 4000 Ada Laptop 12GB?

Q: What should I upgrade first if Kimi Linear 48B A3B feels slow on RTX 4000 Ada Laptop 12GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

NO — Won't Fit

F0Won't run

Estimated from fit model

Kimi Linear 48B A3B needs ~33.8 GB but RTX 4000 Ada Laptop 12GB only has 12.0 GB. Try a smaller quantization or lighter model.

Runtime: vLLMCapacity: No fitBandwidth: LowStack: OptimizedBottleneck: Memory capacity

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) — 33.8 GB, exceeds 12.0 GB available

33.8 GB required12.0 GB available

282% VRAM needed

21.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

33.8 GB / 12.0 GB

Offload

60%

Memory breakdown

Weights29.3 GB

KV Cache0.9 GB

Runtime2.4 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsKimi Linear 48B A3B on RTX 4000 Ada Laptop 12GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 33.8 GB, but this setup only exposes 12.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Quantization options

How Kimi Linear 48B A3B (48B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	18.7 GB	Low	F0
Q3_K_S	3	23.5 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Kimi Linear 48B A3B well

RTX A6000 48GBBudget pick

48 GB VRAM (+36)768 GB/s (+336)

Makes the model fit on the accelerator instead of staying completely out of reach.15.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$4,650 MSRP

RTX PRO 5000 Blackwell 48GBBest value

48 GB VRAM (+36)1344 GB/s (+912)

Makes the model fit on the accelerator instead of staying completely out of reach.30.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$4,999 MSRP

NVIDIA L40 48GBNVIDIA upgrade

48 GB VRAM (+36)864 GB/s (+432)

Makes the model fit on the accelerator instead of staying completely out of reach.18.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$5,500 MSRP

Frequently asked questions

Can RTX 4000 Ada Laptop 12GB run Kimi Linear 48B A3B?

No, Kimi Linear 48B A3B requires more memory than RTX 4000 Ada Laptop 12GB provides.

How much VRAM does Kimi Linear 48B A3B need?

Kimi Linear 48B A3B (48B parameters) requires approximately 33.8 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 RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, Kimi Linear 48B A3B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can RTX 4000 Ada Laptop 12GB run Kimi Linear 48B A3B for coding?

For coding workloads, Kimi Linear 48B A3B on RTX 4000 Ada Laptop 12GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Kimi Linear 48B A3B use on RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, 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.

See all results for RTX 4000 Ada Laptop 12GB See all hardware for Kimi Linear 48B A3B

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