Can Kimi K2.5 run on RTX 3000 Ada Laptop 8GB?

Q: What should I upgrade first if Kimi K2.5 feels slow on RTX 3000 Ada Laptop 8GB?

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 K2.5 needs ~620.6 GB but RTX 3000 Ada Laptop 8GB only has 8.0 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) — 620.6 GB, exceeds 8.0 GB available

620.6 GB required8.0 GB available

7758% VRAM needed

612.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

620.6 GB / 8.0 GB

Offload

100%

Memory breakdown

Weights610.0 GB

KV Cache7.4 GB

Runtime2.4 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsKimi K2.5 on RTX 3000 Ada Laptop 8GB
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 620.6 GB, but this setup only exposes 8.0 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

Inference speed

Kimi K2.5 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Kimi K2.5 at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~2 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	2.0	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0

Quantization options

How Kimi K2.5 (1000B params) fits at each quantization level on RTX 3000 Ada Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	390.0 GB	Low	F0
Q3_K_S	3	490.0 GB	Low	F0
NVFP4	4

Frequently asked questions

Can RTX 3000 Ada Laptop 8GB run Kimi K2.5?

No, Kimi K2.5 requires more memory than RTX 3000 Ada Laptop 8GB provides.

How much VRAM does Kimi K2.5 need?

Kimi K2.5 (1000B parameters) requires approximately 620.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Kimi K2.5?

The recommended quantization for Kimi K2.5 is Q4_K_M, which balances quality and memory efficiency.

What speed will Kimi K2.5 run at on RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 8GB, Kimi K2.5 achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can RTX 3000 Ada Laptop 8GB run Kimi K2.5 for coding?

For coding workloads, Kimi K2.5 on RTX 3000 Ada Laptop 8GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Kimi K2.5 use on RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 8GB, Kimi K2.5 can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Kimi K2.5 feels slow on RTX 3000 Ada Laptop 8GB?

See all results for RTX 3000 Ada Laptop 8GB See all hardware for Kimi K2.5

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