Can Kimi Linear 48B A3B run on NVIDIA H100 80GB?

YES — Runs Great

A85Great

Estimated from fit model

Kimi Linear 48B A3B needs ~40.6 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~77 tok/s.

Runtime: vLLMCapacity: RoomyBandwidth: HighStack: OptimizedBottleneck: Balanced

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) — 40.6 GB, 76.9 tok/s, Runs well

40.6 GB required80.0 GB available

51% VRAM used

Fit status

Runs well

Decode

76.9 tok/s

TTFT

2518 ms

Safe context

696K

Memory

40.6 GB / 80.0 GB

Memory breakdown

Weights29.3 GB

KV Cache0.9 GB

Runtime2.4 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsKimi Linear 48B A3B on NVIDIA H100 80GB
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: 76.9 tok/s decode · 2.5s TTFT (warm) · 192 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	76.9 tok/s	1373 ms	696K
Coding	A	Runs well	76.9 tok/s	2518 ms	696K
Agentic Coding	A	Runs well	76.9 tok/s	3663 ms	696K
Reasoning	A	Runs well	76.9 tok/s	2976 ms	696K
RAG	A	Runs well	76.9 tok/s	4578 ms	696K

Inference speed

Kimi Linear 48B A3B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Kimi Linear 48B A3B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~40 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?
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	40.1	Fits
2× RTX 4090 24GB	48 GB	Q4_K_M	29.3	Fits

Quantization options

How Kimi Linear 48B A3B (48B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	18.7 GB	Low	A74
Q3_K_S	3	23.5 GB	Low	A75
NVFP4	4

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 H100 80GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 2.5 VL 72B	72B	S	55.7 tok/s
Qwen3-Coder-Next	80B	S	107.8 tok/s

Frequently asked questions

Can NVIDIA H100 80GB run Kimi Linear 48B A3B?

Yes, NVIDIA H100 80GB can run Kimi Linear 48B A3B with a A grade (Runs well). Expected decode speed: 76.9 tok/s.

How much VRAM does Kimi Linear 48B A3B need?

Kimi Linear 48B A3B (48B parameters) requires approximately 40.6 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 H100 80GB?

On NVIDIA H100 80GB, Kimi Linear 48B A3B achieves approximately 76.9 tokens per second decode speed with a time-to-first-token of 2518ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run Kimi Linear 48B A3B for coding?

For coding workloads, Kimi Linear 48B A3B on NVIDIA H100 80GB receives a A grade with 76.9 tok/s and 696K context.

What context window can Kimi Linear 48B A3B use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Kimi Linear 48B A3B can safely use up to 696K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.

See all results for NVIDIA H100 80GB See all hardware for Kimi Linear 48B A3B

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