Can Leanstral 119B A6B run on AMD Instinct MI250X 128GB?

YES — Runs Great

S92Excellent

○Estimated from fit model

Leanstral 119B A6B needs ~96.6 GB VRAM. AMD Instinct MI250X 128GB has 128.0 GB. With Q4_K_M quantization, expect ~76 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) — 96.6 GB, 76.0 tok/s, Runs well

96.6 GB required128.0 GB available

75% VRAM used

Fit status

Runs well

Decode

76.0 tok/s

TTFT

2546 ms

Safe context

73K

Memory

96.6 GB / 128.0 GB

Memory breakdown

Weights72.6 GB

KV Cache8.8 GB

Runtime2.4 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsLeanstral 119B A6B on AMD Instinct MI250X 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: 76.0 tok/s decode · 2.5s TTFT (warm) · 190 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	76.0 tok/s	1389 ms	73K
Coding	S	Runs well	76.0 tok/s	2546 ms	73K
Agentic Coding	S	Tight fit	76.0 tok/s	3704 ms	73K
Reasoning	S	Runs well	76.0 tok/s	3009 ms	73K
RAG	S	Tight fit	76.0 tok/s	4630 ms	73K

Quantization options

How Leanstral 119B A6B (119B params) fits at each quantization level on AMD Instinct MI250X 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	46.4 GB	Low	A80
Q3_K_S	3	58.3 GB	Low	A82
NVFP4	4	66.6 GB	Medium	A83
Q4_K_M	4	72.6 GB	Medium	A84
Q5_K_M	5	85.7 GB	High	A84
Q6_KBest for your GPU	6	97.6 GB	High	A84
Q8_0	8	127.3 GB	Very High	F0
F16	16	244.0 GB	Maximum	F0

Get started

Copy-paste commands to run Leanstral 119B A6B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "mistralai/Leanstral-2603" \
 --hf-file "Leanstral-2603-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Your hardware

More models your AMD Instinct MI250X 128GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	S	28.9 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	76.3 tok/s
👁 Mistral AI Pixtral Large 124B	124B	S	28.7 tok/s

Frequently asked questions

See all results for AMD Instinct MI250X 128GB See all hardware for Leanstral 119B A6B

URL: https://willitrunai.com/can-run/leanstral-119b-a6b-on-instinct-mi250x-128gb

⇱ Leanstral 119B A6B on AMD Instinct MI250X 128GB? YES