Can Leanstral 119B A6B run on NVIDIA H20 96GB?

YES — With NVFP4

S89Excellent

Estimated from fit model

Leanstral 119B A6B needs ~87.4 GB VRAM. NVIDIA H20 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 H20 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 H20 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	71.1 tok/s	1486 ms	21K
Coding	F	Too heavy	71.1 tok/s	2724 ms	21K
Agentic Coding	F	Too heavy	54.7 tok/s	5149 ms	21K
Reasoning	F	Too heavy	71.1 tok/s	3220 ms	21K
RAG	F	Too heavy	54.7 tok/s	6436 ms	21K

Quantization options

How Leanstral 119B A6B (119B params) fits at each quantization level on NVIDIA H20 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	66.6 GB	Medium	A84
Q4_K_MBest for your GPU	4	72.6 GB	Medium	A84
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 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 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 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

Can NVIDIA H20 96GB run Leanstral 119B A6B?

Yes, NVIDIA H20 96GB can run Leanstral 119B A6B at NVFP4 quantization (Tight fit). The recommended Q4_K_M requires 93.4 GB which exceeds available memory, but at NVFP4 it needs only 87.4 GB. Expected decode speed: 81.3 tok/s.

How much VRAM does Leanstral 119B A6B need?

Leanstral 119B A6B (119B parameters) requires approximately 93.4 GB at Q4_K_M quantization. On NVIDIA H20 96GB, it fits at NVFP4 using 87.4 GB.

What is the best quantization for Leanstral 119B A6B?

The recommended quantization is Q4_K_M, but on NVIDIA H20 96GB the best fitting quantization is NVFP4, which uses 87.4 GB.

What speed will Leanstral 119B A6B run at on NVIDIA H20 96GB?

On NVIDIA H20 96GB, Leanstral 119B A6B achieves approximately 81.3 tokens per second decode speed with a time-to-first-token of 2382ms using NVFP4 quantization.

Can NVIDIA H20 96GB run Leanstral 119B A6B for coding?

For coding workloads, Leanstral 119B A6B on NVIDIA H20 96GB receives a F grade with 71.1 tok/s and 21K context.

What context window can Leanstral 119B A6B use on NVIDIA H20 96GB?

On NVIDIA H20 96GB, Leanstral 119B A6B can safely use up to 32K tokens of context at NVFP4 quantization. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA H20 96GB See all hardware for Leanstral 119B A6B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/leanstral-119b-a6b-on-h20-96gb" 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

Tight fit

71.1 tok/s

1486 ms

21K

Coding

Too heavy

71.1 tok/s

2724 ms

21K

Agentic Coding

Too heavy

54.7 tok/s

5149 ms

21K

Reasoning

Too heavy

71.1 tok/s

3220 ms

21K

RAG

Too heavy

54.7 tok/s

6436 ms

21K

Quantization options

How Leanstral 119B A6B (119B params) fits at each quantization level on NVIDIA H20 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	66.6 GB	Medium	A84
Q4_K_MBest for your GPU	4	72.6 GB	Medium	A84
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

Frequently asked questions

Can NVIDIA H20 96GB run Leanstral 119B A6B?

How much VRAM does Leanstral 119B A6B need?

Leanstral 119B A6B (119B parameters) requires approximately 93.4 GB at Q4_K_M quantization. On NVIDIA H20 96GB, it fits at NVFP4 using 87.4 GB.

What is the best quantization for Leanstral 119B A6B?

The recommended quantization is Q4_K_M, but on NVIDIA H20 96GB the best fitting quantization is NVFP4, which uses 87.4 GB.

What speed will Leanstral 119B A6B run at on NVIDIA H20 96GB?

On NVIDIA H20 96GB, Leanstral 119B A6B achieves approximately 81.3 tokens per second decode speed with a time-to-first-token of 2382ms using NVFP4 quantization.

Can NVIDIA H20 96GB run Leanstral 119B A6B for coding?

For coding workloads, Leanstral 119B A6B on NVIDIA H20 96GB receives a F grade with 71.1 tok/s and 21K context.

What context window can Leanstral 119B A6B use on NVIDIA H20 96GB?

On NVIDIA H20 96GB, Leanstral 119B A6B can safely use up to 32K tokens of context at NVFP4 quantization. The model's official context limit is 256K, but available memory constrains the safe maximum.