Can Kimi Linear 48B A3B run on NVIDIA DGX Spark 128GB?

YES — Runs Great

A74Great

Estimated from fit model

Kimi Linear 48B A3B needs ~45.7 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~5 tok/s.

Runtime: vLLMCapacity: RoomyBandwidth: LowStack: OptimizedBottleneck: Memory bandwidth

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) — 45.7 GB, 4.5 tok/s, Runs well

45.7 GB required108.8 GB available

42% VRAM used

Fit status

Runs well

Decode

4.5 tok/s

TTFT

43259 ms

Safe context

1.0M

Memory

45.7 GB / 108.8 GB

Memory breakdown

Weights29.3 GB

KV Cache0.9 GB

Runtime2.4 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsKimi Linear 48B A3B on NVIDIA DGX Spark 128GB
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: 4.5 tok/s decode · 43.3s TTFT (warm) · 11 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	4.5 tok/s	23596 ms	1.0M
Coding	A	Runs well	4.5 tok/s	43259 ms	1.0M
Agentic Coding	A	Runs well	4.5 tok/s	62922 ms	1.0M
Reasoning	A	Runs well	4.5 tok/s	51124 ms	1.0M
RAG	A	Runs well	4.5 tok/s	78652 ms	1.0M

Quantization options

How Kimi Linear 48B A3B (48B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	18.7 GB	Low	A73
Q3_K_S	3	23.5 GB	Low	A74
NVFP4	4	26.9 GB	Medium	A74
Q4_K_M	4	29.3 GB	Medium	A75
Q5_K_M	5	34.6 GB	High	A76
Q6_K	6	39.4 GB	High	A77
Q8_0Best for your GPU	8	51.4 GB	Very High	A80
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

Your hardware

More models your NVIDIA DGX Spark 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	2 tok/s
Qwen 3.5 122B A10B	122B	S	5 tok/s
Mistral Small 4 119B	119B	S	5.4 tok/s
GPT-OSS 120B	117B	A	2 tok/s
Command A 111B	111B	S	2.1 tok/s

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Kimi Linear 48B A3B?

Yes, NVIDIA DGX Spark 128GB can run Kimi Linear 48B A3B with a A grade (Runs well). Expected decode speed: 4.5 tok/s.

How much VRAM does Kimi Linear 48B A3B need?

Kimi Linear 48B A3B (48B parameters) requires approximately 45.7 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 DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Kimi Linear 48B A3B achieves approximately 4.5 tokens per second decode speed with a time-to-first-token of 43259ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Kimi Linear 48B A3B for coding?

For coding workloads, Kimi Linear 48B A3B on NVIDIA DGX Spark 128GB receives a A grade with 4.5 tok/s and 1.0M context.

What context window can Kimi Linear 48B A3B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Kimi Linear 48B A3B can safely use up to 1.0M 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 DGX Spark 128GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Kimi Linear 48B A3B?

Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for NVIDIA DGX Spark 128GB See all hardware for Kimi Linear 48B A3B

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