Can Leanstral 119B A6B run on NVIDIA B200 180GB?

YES — Runs Great

S90Excellent

Estimated from fit model

Leanstral 119B A6B needs ~101.8 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~205 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 101.8 GB, 204.7 tok/s, Runs well

101.8 GB required180.0 GB available

57% VRAM used

Fit status

Runs well

Decode

204.7 tok/s

TTFT

946 ms

Safe context

158K

Memory

101.8 GB / 180.0 GB

Memory breakdown

Weights72.6 GB

KV Cache8.8 GB

Runtime2.4 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsLeanstral 119B A6B on NVIDIA B200 180GB
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: 204.7 tok/s decode · 946ms TTFT (warm) · 512 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	Runs well	204.7 tok/s	516 ms	158K
Coding	S	Runs well	204.7 tok/s	946 ms	158K
Agentic Coding	S	Runs well	204.7 tok/s	1376 ms	158K
Reasoning	S	Runs well	204.7 tok/s	1118 ms	158K
RAG	S	Runs well	204.7 tok/s	1720 ms	158K

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 B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	46.4 GB	Low	A77
Q3_K_S	3	58.3 GB	Low	A79
NVFP4	4	66.6 GB	Medium	A80
Q4_K_M	4	72.6 GB	Medium	A81
Q5_K_M	5	85.7 GB	High	A82
Q6_K	6	97.6 GB	High	A83
Q8_0Best for your GPU	8	127.3 GB	Very High	A84
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 B200 180GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	77.9 tok/s
Qwen 3.5 122B A10B	122B	S	205.3 tok/s
Pixtral Large 124B	124B	S	77.3 tok/s
Qwen 3 235B A22B	235B	S	103.9 tok/s
MiniMax M2.7	230B	S	118.2 tok/s

Frequently asked questions

Can NVIDIA B200 180GB run Leanstral 119B A6B?

Yes, NVIDIA B200 180GB can run Leanstral 119B A6B with a S grade (Runs well). Expected decode speed: 204.7 tok/s.

How much VRAM does Leanstral 119B A6B need?

Leanstral 119B A6B (119B parameters) requires approximately 101.8 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 B200 180GB?

On NVIDIA B200 180GB, Leanstral 119B A6B achieves approximately 204.7 tokens per second decode speed with a time-to-first-token of 946ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run Leanstral 119B A6B for coding?

For coding workloads, Leanstral 119B A6B on NVIDIA B200 180GB receives a S grade with 204.7 tok/s and 158K context.

What context window can Leanstral 119B A6B use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Leanstral 119B A6B can safely use up to 158K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA B200 180GB See all hardware for Leanstral 119B A6B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

204.7 tok/s

516 ms

158K

Coding

Runs well

204.7 tok/s

946 ms

158K

Agentic Coding

Runs well

204.7 tok/s

1376 ms

158K

Reasoning

Runs well

204.7 tok/s

1118 ms

158K

RAG

Runs well

204.7 tok/s

1720 ms

158K

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

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

Quantization options

How Leanstral 119B A6B (119B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	46.4 GB	Low	A77
Q3_K_S	3	58.3 GB	Low	A79
NVFP4	4	66.6 GB	Medium	A80
Q4_K_M	4	72.6 GB	Medium	A81
Q5_K_M	5	85.7 GB	High	A82
Q6_K	6	97.6 GB	High	A83
Q8_0Best for your GPU	8	127.3 GB	Very High	A84
F16	16	244.0 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

77.9 tok/s

Qwen 3.5 122B A10B

122B

205.3 tok/s

Pixtral Large 124B

124B

77.3 tok/s

Qwen 3 235B A22B

235B

103.9 tok/s

MiniMax M2.7

230B

118.2 tok/s

Frequently asked questions

Can NVIDIA B200 180GB run Leanstral 119B A6B?

Yes, NVIDIA B200 180GB can run Leanstral 119B A6B with a S grade (Runs well). Expected decode speed: 204.7 tok/s.

How much VRAM does Leanstral 119B A6B need?

Leanstral 119B A6B (119B parameters) requires approximately 101.8 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 B200 180GB?

On NVIDIA B200 180GB, Leanstral 119B A6B achieves approximately 204.7 tokens per second decode speed with a time-to-first-token of 946ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run Leanstral 119B A6B for coding?

For coding workloads, Leanstral 119B A6B on NVIDIA B200 180GB receives a S grade with 204.7 tok/s and 158K context.

What context window can Leanstral 119B A6B use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Leanstral 119B A6B can safely use up to 158K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.