Can Leanstral 119B A6B run on RTX 5000 Ada 32GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Leanstral 119B A6B needs ~87.0 GB but RTX 5000 Ada 32GB only has 32.0 GB. Try a smaller quantization or lighter model.

Runtime: vLLMCapacity: No fitBandwidth: MediumStack: OptimizedBottleneck: Memory capacity

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) — 87.0 GB, exceeds 32.0 GB available

87.0 GB required32.0 GB available

272% VRAM needed

55.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.1 tok/s

TTFT

91948 ms

Safe context

Memory

87.0 GB / 32.0 GB

Offload

60%

Memory breakdown

Weights72.6 GB

KV Cache8.8 GB

Runtime2.4 GB

Headroom3.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLeanstral 119B A6B on RTX 5000 Ada 32GB
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: 2.1 tok/s decode · 91.9s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 87.0 GB, but this setup only exposes 32.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.1 tok/s	50153 ms	4K
Coding	F	Too heavy	2.1 tok/s	91948 ms	4K
Agentic Coding	F	Too heavy	2.1 tok/s	133742 ms	4K
Reasoning	F	Too heavy	2.1 tok/s	108666 ms	4K
RAG	F	Too heavy	2.1 tok/s	167178 ms	4K

Quantization options

How Leanstral 119B A6B (119B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	46.4 GB	Low	F0
Q3_K_S	3	58.3 GB	Low	F0
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

升级选项

能流畅运行 Leanstral 119B A6B 的硬件

NVIDIA H200 141GB经济之选

141 GB VRAM (+109)4800 GB/s (+4224)

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

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 H200 PCIe 141GB最佳性价比

141 GB VRAM (+109)4800 GB/s (+4224)

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

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 B200 180GBNVIDIA 升级

180 GB VRAM (+148)8000 GB/s (+7424)

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

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

Can RTX 5000 Ada 32GB run Leanstral 119B A6B?

No, Leanstral 119B A6B requires more memory than RTX 5000 Ada 32GB provides.

How much VRAM does Leanstral 119B A6B need?

Leanstral 119B A6B (119B parameters) requires approximately 87.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Leanstral 119B A6B?

The recommended quantization for Leanstral 119B A6B is Q4_K_M, which balances quality and memory efficiency.

What speed will Leanstral 119B A6B run at on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Leanstral 119B A6B achieves approximately 2.1 tokens per second decode speed with a time-to-first-token of 91948ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run Leanstral 119B A6B for coding?

For coding workloads, Leanstral 119B A6B on RTX 5000 Ada 32GB receives a F grade with 2.1 tok/s and 4K context.

What context window can Leanstral 119B A6B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Leanstral 119B A6B can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Leanstral 119B A6B feels slow on RTX 5000 Ada 32GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX 5000 Ada 32GB See all hardware for Leanstral 119B A6B

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