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

Inference speed

Leanstral 119B A6B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Leanstral 119B A6B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~17 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	17.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	11.9	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	11.3	Too big
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	8.8	Too big
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	7.4	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	6.7	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	5.8	Too big
RTX 5090 32GB	32 GB	Q4_K_M	5.5	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	5.1	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	3.7	Too big
RTX 4090 24GB	24 GB	Q4_K_M	3.5	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	3.2	Too big
RTX 3090 24GB	24 GB	Q4_K_M	3.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.8	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	2.6	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	2.4	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

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

Opções de upgrade

Hardware que roda bem Leanstral 119B A6B

NVIDIA H200 141GBOpção econômica

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 decodificação

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 141GBMelhor custo-benefício

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 decodificação

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 180GBUpgrade NVIDIA

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 decodificação

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 NVIDIA H100 PCIe 80GB run Leanstral 119B A6B?

Yes, NVIDIA H100 PCIe 80GB can run Leanstral 119B A6B at Q2_K quantization (Runs well). The recommended Q4_K_M requires 91.8 GB which exceeds available memory, but at Q2_K it needs only 65.6 GB. Expected decode speed: 68.0 tok/s.

How much VRAM does Leanstral 119B A6B need?

Leanstral 119B A6B (119B parameters) requires approximately 91.8 GB at Q4_K_M quantization. On NVIDIA H100 PCIe 80GB, it fits at Q2_K using 65.6 GB.

What is the best quantization for Leanstral 119B A6B?

The recommended quantization is Q4_K_M, but on NVIDIA H100 PCIe 80GB the best fitting quantization is Q2_K, which uses 65.6 GB.

What speed will Leanstral 119B A6B run at on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Leanstral 119B A6B achieves approximately 68.0 tokens per second decode speed with a time-to-first-token of 2845ms using Q2_K quantization.

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

For coding workloads, Leanstral 119B A6B on NVIDIA H100 PCIe 80GB receives a F grade with 29.6 tok/s and 4K context.

What context window can Leanstral 119B A6B use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Leanstral 119B A6B can safely use up to 42K tokens of context at Q2_K quantization. The model's official context limit is 256K, but available memory constrains the safe maximum.

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

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