Can Leanstral 119B A6B run on NVIDIA DGX Spark 128GB?

YES — Tight Fit

A81Great

Estimated from fit model

Leanstral 119B A6B needs ~96.8 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~5 tok/s.

Runtime: vLLMCapacity: TightBandwidth: LowStack: OptimizedBottleneck: Memory bandwidth

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) — 96.8 GB, 5.0 tok/s, Tight fit

96.8 GB required108.8 GB available

89% VRAM used

Fit status

Tight fit

Decode

5.0 tok/s

TTFT

38800 ms

Safe context

38K

Memory

96.8 GB / 108.8 GB

Memory breakdown

Weights72.6 GB

KV Cache8.8 GB

Runtime2.4 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsLeanstral 119B A6B on NVIDIA DGX Spark 128GB
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: 5.0 tok/s decode · 38.8s TTFT (warm) · 13 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	5.0 tok/s	21164 ms	38K
Coding	A	Tight fit	5.0 tok/s	38800 ms	38K
Agentic Coding	F	Too heavy	5.0 tok/s	56436 ms	38K
Reasoning	A	Tight fit	5.0 tok/s	45854 ms	38K
RAG	F	Too heavy	5.0 tok/s	70545 ms	38K

Quantization options

How Leanstral 119B A6B (119B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	46.4 GB	Low	A84
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

Your hardware

More models your NVIDIA DGX Spark 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	2 tok/s
Qwen 3.5 122B A10B	122B	S	5 tok/s
Pixtral Large 124B	124B	A	2 tok/s

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Leanstral 119B A6B?

Yes, NVIDIA DGX Spark 128GB can run Leanstral 119B A6B with a A grade (Tight fit). Expected decode speed: 5.0 tok/s.

How much VRAM does Leanstral 119B A6B need?

Leanstral 119B A6B (119B parameters) requires approximately 96.8 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 NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Leanstral 119B A6B achieves approximately 5.0 tokens per second decode speed with a time-to-first-token of 38800ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Leanstral 119B A6B for coding?

For coding workloads, Leanstral 119B A6B on NVIDIA DGX Spark 128GB receives a A grade with 5.0 tok/s and 38K context.

What context window can Leanstral 119B A6B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Leanstral 119B A6B can safely use up to 38K 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 NVIDIA DGX Spark 128GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Leanstral 119B A6B?

Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for NVIDIA DGX Spark 128GB See all hardware for Leanstral 119B A6B

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Preview:

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Tight fit

5.0 tok/s

21164 ms

38K

Coding

Tight fit

5.0 tok/s

38800 ms

38K

Agentic Coding

Too heavy

5.0 tok/s

56436 ms

38K

Reasoning

Tight fit

5.0 tok/s

45854 ms

38K

RAG

Too heavy

5.0 tok/s

70545 ms

38K

Quantization options

How Leanstral 119B A6B (119B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	46.4 GB	Low	A84
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

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

2 tok/s

Qwen 3.5 122B A10B

122B

5 tok/s

Pixtral Large 124B

124B