Can Kimi Linear 48B A3B run on AMD Instinct MI100 32GB?

Q: What should I upgrade first if Kimi Linear 48B A3B feels slow on AMD Instinct MI100 32GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

A71Great

Estimated from fit model

Kimi Linear 48B A3B needs ~35.2 GB VRAM. AMD Instinct MI100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~17 tok/s.

Runtime: TransformersCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload

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) — 35.2 GB, 16.7 tok/s, Very compromised (needs ~2.7 GB host RAM)

35.2 GB required32.0 GB available

110% VRAM needed

3.2 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.7 GB host RAM)

Decode

16.7 tok/s

TTFT

11578 ms

Safe context

Memory

35.2 GB / 32.0 GB

Offload

10%

Memory breakdown

Weights29.3 GB

KV Cache0.9 GB

Runtime1.8 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsKimi Linear 48B A3B on AMD Instinct MI100 32GB
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: 16.7 tok/s decode · 11.6s TTFT (warm) · 42 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Very compromised (needs ~2.3 GB host RAM)	17.2 tok/s	6141 ms	4K
Coding	A	Very compromised	16.7 tok/s	11578 ms	4K
Agentic Coding	A	Very compromised (needs ~3.3 GB host RAM)	15.8 tok/s	17787 ms	4K
Reasoning	A	Very compromised (needs ~2.7 GB host RAM)	16.7 tok/s	13683 ms	4K
RAG	A	Very compromised (needs ~3.3 GB host RAM)	15.8 tok/s

Quantization options

How Kimi Linear 48B A3B (48B params) fits at each quantization level on AMD Instinct MI100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	18.7 GB	Low	A81
Q3_K_SBest for your GPU	3	23.5 GB	Low	A81

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

Frequently asked questions

Can AMD Instinct MI100 32GB run Kimi Linear 48B A3B?

Yes, AMD Instinct MI100 32GB can run Kimi Linear 48B A3B with a A grade (Very compromised). Expected decode speed: 16.7 tok/s.

How much VRAM does Kimi Linear 48B A3B need?

Kimi Linear 48B A3B (48B parameters) requires approximately 35.2 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 AMD Instinct MI100 32GB?

On AMD Instinct MI100 32GB, Kimi Linear 48B A3B achieves approximately 16.7 tokens per second decode speed with a time-to-first-token of 11578ms using Q4_K_M quantization.

Can AMD Instinct MI100 32GB run Kimi Linear 48B A3B for coding?

For coding workloads, Kimi Linear 48B A3B on AMD Instinct MI100 32GB receives a A grade with 16.7 tok/s and 4K context.

What context window can Kimi Linear 48B A3B use on AMD Instinct MI100 32GB?

On AMD Instinct MI100 32GB, 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 AMD Instinct MI100 32GB See all hardware for Kimi Linear 48B A3B

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