Can Leanstral 119B A6B run on B100 192GB?

YES — Runs Great

S89Excellent

○Estimated from fit model

Leanstral 119B A6B needs ~103.0 GB VRAM. B100 192GB has 192.0 GB. With Q4_K_M quantization, expect ~205 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) — 103.0 GB, 204.7 tok/s, Runs well

103.0 GB required192.0 GB available

54% VRAM used

Fit status

Runs well

Decode

204.7 tok/s

TTFT

946 ms

Safe context

178K

Memory

103.0 GB / 192.0 GB

Memory breakdown

Weights72.6 GB

KV Cache8.8 GB

Runtime2.4 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsLeanstral 119B A6B on B100 192GB
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: 204.7 tok/s decode · 946ms TTFT (warm) · 512 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	204.7 tok/s	516 ms	178K
Coding	S	Runs well	204.7 tok/s	946 ms	178K
Agentic Coding	S	Runs well	204.7 tok/s	1376 ms	178K
Reasoning	S	Runs well	204.7 tok/s	1118 ms	178K
RAG	S	Runs well	204.7 tok/s	1720 ms	178K

Quantization options

How Leanstral 119B A6B (119B params) fits at each quantization level on B100 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	46.4 GB	Low	A77
Q3_K_S	3	58.3 GB	Low	A78
NVFP4	4	66.6 GB	Medium	A79
Q4_K_M	4	72.6 GB	Medium	A80
Q5_K_M	5	85.7 GB	High	A81
Q6_K	6	97.6 GB	High	A83
Q8_0Best for your GPU	8	127.3 GB	Very High	A84
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 B100 192GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	S	77.9 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	205.3 tok/s
👁 DeepSeek DeepSeek V4 Flash	284B	S	110 tok/s
👁 Mistral AI Pixtral Large 124B	124B	S	77.3 tok/s
👁 Alibaba Qwen 3 235B A22B	235B	S	103.9 tok/s

Frequently asked questions

See all results for B100 192GB See all hardware for Leanstral 119B A6B

URL: https://willitrunai.com/can-run/leanstral-119b-a6b-on-b100-192gb

⇱ Leanstral 119B A6B on B100 192GB? YES