Can Laguna XS 2.1 run on AMD Instinct MI100 32GB?

YES — Runs Great

S89Excellent

Estimated from fit model

Laguna XS 2.1 needs ~25.1 GB VRAM. AMD Instinct MI100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~114 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 25.1 GB, 114.1 tok/s, Runs well

25.1 GB required32.0 GB available

78% VRAM used

Fit status

Runs well

Decode

114.1 tok/s

TTFT

1696 ms

Safe context

197K

Memory

25.1 GB / 32.0 GB

Memory breakdown

Weights20.4 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsLaguna XS 2.1 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: 114.1 tok/s decode · 1.7s TTFT (warm) · 285 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	S	Runs well	114.1 tok/s	925 ms	197K
Coding	S	Runs well	114.1 tok/s	1696 ms	197K
Agentic Coding	S	Runs well	114.1 tok/s	2467 ms	197K
Reasoning	S	Runs well	114.1 tok/s	2005 ms	197K
RAG	S	Runs well	114.1 tok/s	3084 ms	197K

Inference speed

Laguna XS 2.1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Laguna XS 2.1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~144 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	144.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	85.8	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	79.6	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	72.3	Offloads
RTX 3090 24GB	24 GB	Q4_K_M	68.5	Offloads
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	66.3	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	62.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	49.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	49.2	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	37.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	34.6	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	33.1	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	31.3	Too big
RTX 4070 12GB	12 GB	Q4_K_M	10.9	Too big
RTX 3060 12GB	12 GB	Q4_K_M	6.4	Too big
RTX 4060 8GB	8 GB	Q4_K_M	4.6	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Laguna XS 2.1 (33.400001525878906B params) fits at each quantization level on AMD Instinct MI100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	4.8 GB	Very Low	A76
Q2_0_G128	1.71	8.9 GB	Low	A78
Q2_K	2	13.0 GB	Low	A80
Q3_K_S	3	16.4 GB	Low	A81
NVFP4	4	18.7 GB	Medium	A81
Q4_K_M	4	20.4 GB	Medium	A81
Q5_K_MBest for your GPU	5	24.0 GB	High	A80
Q6_K	6	27.4 GB	High	F0
Q8_0	8	35.7 GB	Very High	F0
F16	16	68.5 GB	Maximum	F0

Get started

Copy-paste commands to run Laguna XS 2.1 on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "poolside/Laguna-XS-2.1" \
  --hf-file "Laguna-XS-2.1-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your AMD Instinct MI100 32GB can run

Model	Params	Grade	Decode
Qwen 3.6 35B A3B	35B	S	101.4 tok/s
Qwen 3.5 35B A3B	35B	S	110.3 tok/s
Agents-A1 35B A3B	35.1B	S	110.1 tok/s
Qwen AgentWorld 35B A3B	34.7B	S	111.1 tok/s

Frequently asked questions

Can AMD Instinct MI100 32GB run Laguna XS 2.1?

Yes, AMD Instinct MI100 32GB can run Laguna XS 2.1 with a S grade (Runs well). Expected decode speed: 114.1 tok/s.

How much VRAM does Laguna XS 2.1 need?

Laguna XS 2.1 (33.400001525878906B parameters) requires approximately 25.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Laguna XS 2.1?

The recommended quantization for Laguna XS 2.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Laguna XS 2.1 run at on AMD Instinct MI100 32GB?

On AMD Instinct MI100 32GB, Laguna XS 2.1 achieves approximately 114.1 tokens per second decode speed with a time-to-first-token of 1696ms using Q4_K_M quantization.

Can AMD Instinct MI100 32GB run Laguna XS 2.1 for coding?

For coding workloads, Laguna XS 2.1 on AMD Instinct MI100 32GB receives a S grade with 114.1 tok/s and 197K context.

What context window can Laguna XS 2.1 use on AMD Instinct MI100 32GB?

On AMD Instinct MI100 32GB, Laguna XS 2.1 can safely use up to 197K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for AMD Instinct MI100 32GB See all hardware for Laguna XS 2.1

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<iframe src="https://willitrunai.com/embed/laguna-xs-2.1-on-instinct-mi100-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

114.1 tok/s

925 ms

197K

Coding

Runs well

114.1 tok/s

1696 ms

197K

Agentic Coding

Runs well

114.1 tok/s

2467 ms

197K

Reasoning

Runs well

114.1 tok/s

2005 ms

197K

RAG

Runs well

114.1 tok/s

3084 ms

197K

Laguna XS 2.1 inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

144.2

Fits

RTX 4090 24GB

24 GB

Q4_K_M

85.8

Offloads

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

79.6

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

72.3

Offloads

RTX 3090 24GB

24 GB

Q4_K_M

68.5

Offloads

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

66.3

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

62.9

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

49.2

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

49.2

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

37.7

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

34.6

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

33.1

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

31.3

Too big

RTX 4070 12GB

12 GB

Q4_K_M

10.9

Too big

RTX 3060 12GB

12 GB

Q4_K_M

6.4

Too big

RTX 4060 8GB

8 GB

Q4_K_M

4.6

Too big

Quantization options

How Laguna XS 2.1 (33.400001525878906B params) fits at each quantization level on AMD Instinct MI100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	4.8 GB	Very Low	A76
Q2_0_G128	1.71	8.9 GB	Low	A78
Q2_K	2	13.0 GB	Low	A80
Q3_K_S	3	16.4 GB	Low	A81
NVFP4	4	18.7 GB	Medium	A81
Q4_K_M	4	20.4 GB	Medium	A81
Q5_K_MBest for your GPU	5	24.0 GB	High	A80
Q6_K	6	27.4 GB	High	F0
Q8_0	8	35.7 GB	Very High	F0
F16	16	68.5 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen 3.6 35B A3B

35B

101.4 tok/s

Qwen 3.5 35B A3B

35B

110.3 tok/s

Agents-A1 35B A3B

35.1B

110.1 tok/s

Qwen AgentWorld 35B A3B

34.7B

111.1 tok/s

Frequently asked questions

Can AMD Instinct MI100 32GB run Laguna XS 2.1?

Yes, AMD Instinct MI100 32GB can run Laguna XS 2.1 with a S grade (Runs well). Expected decode speed: 114.1 tok/s.

How much VRAM does Laguna XS 2.1 need?

Laguna XS 2.1 (33.400001525878906B parameters) requires approximately 25.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Laguna XS 2.1?

The recommended quantization for Laguna XS 2.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Laguna XS 2.1 run at on AMD Instinct MI100 32GB?

On AMD Instinct MI100 32GB, Laguna XS 2.1 achieves approximately 114.1 tokens per second decode speed with a time-to-first-token of 1696ms using Q4_K_M quantization.

Can AMD Instinct MI100 32GB run Laguna XS 2.1 for coding?

For coding workloads, Laguna XS 2.1 on AMD Instinct MI100 32GB receives a S grade with 114.1 tok/s and 197K context.

What context window can Laguna XS 2.1 use on AMD Instinct MI100 32GB?

On AMD Instinct MI100 32GB, Laguna XS 2.1 can safely use up to 197K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.