Can Leanstral 119B A6B run on RTX A5000 24GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Leanstral 119B A6B needs ~86.2 GB but RTX A5000 24GB only has 24.0 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) — 86.2 GB, exceeds 24.0 GB available

86.2 GB required24.0 GB available

359% VRAM needed

62.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.5 tok/s

TTFT

78812 ms

Safe context

Memory

86.2 GB / 24.0 GB

Offload

70%

Memory breakdown

Weights72.6 GB

KV Cache8.8 GB

Runtime2.4 GB

Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLeanstral 119B A6B on RTX A5000 24GB
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: 2.5 tok/s decode · 78.8s TTFT (warm) · 6 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 86.2 GB, but this setup only exposes 24.0 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	2.5 tok/s	42989 ms	4K
Coding	F	Too heavy	2.5 tok/s	78812 ms	4K
Agentic Coding	F	Too heavy	2.5 tok/s	114636 ms	4K
Reasoning	F	Too heavy	2.5 tok/s	93142 ms	4K
RAG	F	Too heavy	2.5 tok/s	143295 ms	4K

Quantization options

How Leanstral 119B A6B (119B params) fits at each quantization level on RTX A5000 24GB (24.0 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

Upgrade options

Hardware that runs Leanstral 119B A6B well

NVIDIA H200 141GBBudget pick

141 GB VRAM (+117)4800 GB/s (+4032)

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 H200 PCIe 141GBBest value

141 GB VRAM (+117)4800 GB/s (+4032)

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 B200 180GBNVIDIA upgrade

180 GB VRAM (+156)8000 GB/s (+7232)

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

Can RTX A5000 24GB run Leanstral 119B A6B?

No, Leanstral 119B A6B requires more memory than RTX A5000 24GB provides.

How much VRAM does Leanstral 119B A6B need?

Leanstral 119B A6B (119B parameters) requires approximately 86.2 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 A5000 24GB?

On RTX A5000 24GB, Leanstral 119B A6B achieves approximately 2.5 tokens per second decode speed with a time-to-first-token of 78812ms using Q4_K_M quantization.

Can RTX A5000 24GB run Leanstral 119B A6B for coding?

For coding workloads, Leanstral 119B A6B on RTX A5000 24GB receives a F grade with 2.5 tok/s and 4K context.

What context window can Leanstral 119B A6B use on RTX A5000 24GB?

On RTX A5000 24GB, 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 A5000 24GB?

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 A5000 24GB See all hardware for Leanstral 119B A6B

Embed this result▼

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

2.5 tok/s

42989 ms

Coding

Too heavy

2.5 tok/s

78812 ms

Agentic Coding

Too heavy

2.5 tok/s

114636 ms

Reasoning

Too heavy

2.5 tok/s

93142 ms

RAG

Too heavy

2.5 tok/s

143295 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

46.4 GB

Low

Q3_K_S

58.3 GB

Low

NVFP4

66.6 GB

Medium

Q4_K_M

72.6 GB

Medium

Q5_K_M

85.7 GB

High

Q6_K

97.6 GB

High

Q8_0

127.3 GB

Very High

F16

244.0 GB

Maximum

Frequently asked questions

Can RTX A5000 24GB run Leanstral 119B A6B?

No, Leanstral 119B A6B requires more memory than RTX A5000 24GB provides.

How much VRAM does Leanstral 119B A6B need?

Leanstral 119B A6B (119B parameters) requires approximately 86.2 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 A5000 24GB?

On RTX A5000 24GB, Leanstral 119B A6B achieves approximately 2.5 tokens per second decode speed with a time-to-first-token of 78812ms using Q4_K_M quantization.

Can RTX A5000 24GB run Leanstral 119B A6B for coding?

For coding workloads, Leanstral 119B A6B on RTX A5000 24GB receives a F grade with 2.5 tok/s and 4K context.

What context window can Leanstral 119B A6B use on RTX A5000 24GB?

On RTX A5000 24GB, 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 A5000 24GB?

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.