VOOZH about

URL: https://willitrunai.com/can-run/kimi-linear-48b-a3b-on-rtx-5090-laptop-24gb

⇱ Kimi Linear 48B A3B on RTX 5090 Laptop 24GB? No — Alternati…

Can Kimi Linear 48B A3B run on RTX 5090 Laptop 24GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

Kimi Linear 48B A3B needs ~35.0 GB but RTX 5090 Laptop 24GB only has 24.0 GB. Try a smaller quantization or lighter model.

Runtime: vLLMCapacity: No fitBandwidth: HighStack: OptimizedBottleneck: Memory capacity
Share:

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) — 35.0 GB, exceeds 24.0 GB available
35.0 GB required24.0 GB available
146% VRAM needed

11.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.0 tok/s

TTFT

27787 ms

Safe context

4K

Memory

35.0 GB / 24.0 GB

Offload

30%

Memory breakdown

Weights29.3 GB
KV Cache0.9 GB
Runtime2.4 GB
Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsKimi Linear 48B A3B on RTX 5090 Laptop 24GB
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: 7.0 tok/s decode · 27.8s TTFT (warm) · 17 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 35.0 GB, but this setup only exposes 24.0 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

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy7.2 tok/s14737 ms4K
CodingFToo heavy7.0 tok/s27787 ms4K
Agentic CodingFToo heavy6.6 tok/s42703 ms4K
ReasoningFToo heavy7.0 tok/s32839 ms4K
RAGFToo heavy6.6 tok/s53379 ms4K

Quantization options

How Kimi Linear 48B A3B (48B params) fits at each quantization level on RTX 5090 Laptop 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
18.7 GB
LowF0
Q3_K_S
3
23.5 GB
LowF0
NVFP4
4
26.9 GB
MediumF0
Q4_K_M
4
29.3 GB
MediumF0
Q5_K_M
5
34.6 GB
HighF0
Q6_K
6
39.4 GB
HighF0
Q8_0
8
51.4 GB
Very HighF0
F16
16
98.4 GB
MaximumF0

Upgrade options

Hardware that runs Kimi Linear 48B A3B well

👁 NVIDIA
RTX A6000 48GBBudget pick
48 GB VRAM (+24)
A
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

👁 NVIDIA
RTX PRO 5000 Blackwell 48GBBest value
48 GB VRAM (+24)1344 GB/s (+448)
S
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
NVIDIA L40 48GBNVIDIA upgrade
48 GB VRAM (+24)
A
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

See all results for RTX 5090 Laptop 24GBSee all hardware for Kimi Linear 48B A3B