Can Leanstral 119B A6B run on NVIDIA H100 PCIe 80GB?

YES — With Q2_K

S92Excellent

○Estimated from fit model

Leanstral 119B A6B needs ~65.6 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q2_K quantization, expect ~68 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.

Leanstral 119B A6B at Q4_K_M needs 91.8 GB — too much for NVIDIA H100 PCIe 80GB (80.0 GB). Runs at Q2_K (65.6 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 91.8 GB, exceeds 80.0 GB available

91.8 GB required80.0 GB available

115% VRAM needed

11.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

29.6 tok/s

TTFT

6547 ms

Safe context

Memory

91.8 GB / 80.0 GB

Offload

10%

Memory breakdown

Weights72.6 GB

KV Cache8.8 GB

Runtime2.4 GB

Headroom8.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLeanstral 119B A6B on NVIDIA H100 PCIe 80GB
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: 29.6 tok/s decode · 6.5s TTFT (warm) · 74 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	F	Too heavy	32.7 tok/s	3231 ms	4K
Coding	F	Too heavy	29.6 tok/s	6547 ms	4K
Agentic Coding	F	Too heavy	24.5 tok/s	11476 ms	4K
Reasoning	F	Too heavy	29.6 tok/s	7737 ms	4K
RAG	F	Too heavy	24.5 tok/s	14345 ms	4K

Quantization options

How Leanstral 119B A6B (119B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	46.4 GB	Low	A84
Q3_K_SBest for your GPU	3	58.3 GB	Low	A84
NVFP4	4	66.6 GB	Medium	F0
Q4_K_M	4	72.6 GB	Medium	F0
Q5_K_M	5	85.7 GB	High	F0
Q6_K	6	97.6 GB	High	F0
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

Upgrade options

Hardware that runs Leanstral 119B A6B well

👁 NVIDIA
NVIDIA H200 141GBBudget pick

141 GB VRAM (+61)4800 GB/s (+2800)

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.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$30,000 MSRP

👁 NVIDIA
NVIDIA H200 PCIe 141GBBest value

141 GB VRAM (+61)4800 GB/s (+2800)

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.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$30,000 MSRP

👁 NVIDIA
NVIDIA B200 180GBNVIDIA upgrade

180 GB VRAM (+100)8000 GB/s (+6000)

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.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$30,000 MSRP

Frequently asked questions

See all results for NVIDIA H100 PCIe 80GB See all hardware for Leanstral 119B A6B

URL: https://willitrunai.com/can-run/leanstral-119b-a6b-on-h100-pcie-80gb

⇱ Leanstral 119B A6B on NVIDIA H100 PCIe 80GB? No — Alternati…

Can Leanstral 119B A6B run on NVIDIA H100 PCIe 80GB?

YES — With Q2_K

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