Can Leanstral 119B A6B run on NVIDIA GH200 96GB?

YES — With NVFP4

S89Excellent

○Estimated from fit model

Leanstral 119B A6B needs ~87.4 GB VRAM. NVIDIA GH200 96GB has 96.0 GB. With NVFP4 quantization, expect ~81 tok/s.

Runtime: vLLMCapacity: TightBandwidth: 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.

Leanstral 119B A6B at Q4_K_M needs 93.4 GB — too much for NVIDIA GH200 96GB (96.0 GB). Runs at NVFP4 (87.4 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 93.4 GB, exceeds 96.0 GB available

93.4 GB required96.0 GB available

97% VRAM used

Fit status

Too heavy

Decode

71.1 tok/s

TTFT

2724 ms

Safe context

21K

Memory

93.4 GB / 96.0 GB

Memory breakdown

Weights72.6 GB

KV Cache8.8 GB

Runtime2.4 GB

Headroom9.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLeanstral 119B A6B on NVIDIA GH200 96GB
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: 71.1 tok/s decode · 2.7s TTFT (warm) · 178 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	Tight fit	98.7 tok/s	1070 ms	21K
Coding	F	Too heavy	98.7 tok/s	1962 ms	21K
Agentic Coding	F	Too heavy	76.0 tok/s	3707 ms	21K
Reasoning	F	Too heavy	98.7 tok/s	2318 ms	21K
RAG	F	Too heavy	76.0 tok/s	4634 ms	21K

Quantization options

How Leanstral 119B A6B (119B params) fits at each quantization level on NVIDIA GH200 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	46.4 GB	Low	A83
Q3_K_S	3	58.3 GB	Low	A84
NVFP4	4

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

Upgrade options

Hardware that runs Leanstral 119B A6B well

👁 NVIDIA
NVIDIA H200 141GBBudget pick

141 GB VRAM (+45)4800 GB/s (+800)

Makes the model fit on the accelerator instead of staying completely out of reach.88.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Adds memory headroom for longer context windows and future model growth.

~$30,000 MSRP

👁 NVIDIA
NVIDIA H200 PCIe 141GBBest value

141 GB VRAM (+45)4800 GB/s (+800)

Makes the model fit on the accelerator instead of staying completely out of reach.88.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Adds memory headroom for longer context windows and future model growth.

~$30,000 MSRP

👁 NVIDIA
NVIDIA B200 180GBNVIDIA upgrade

180 GB VRAM (+84)8000 GB/s (+4000)

Makes the model fit on the accelerator instead of staying completely out of reach.204.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 188%.

~$30,000 MSRP

Frequently asked questions

See all results for NVIDIA GH200 96GB See all hardware for Leanstral 119B A6B

URL: https://willitrunai.com/can-run/leanstral-119b-a6b-on-gh200-96gb

⇱

Can Leanstral 119B A6B run on NVIDIA GH200 96GB?

YES — With NVFP4

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Leanstral 119B A6B well

Frequently asked questions