Can Command A 111B run on NVIDIA H20 96GB?

YES — Tight Fit

S92Excellent

Estimated from fit model

Command A 111B needs ~82.1 GB VRAM. NVIDIA H20 96GB has 96.0 GB. With Q4_K_M quantization, expect ~48 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: HighStack: StandardBottleneck: 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) — 82.1 GB, 52.2 tok/s, Tight fit

82.1 GB required96.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

52.2 tok/s

TTFT

3706 ms

Safe context

73K

Memory

82.1 GB / 96.0 GB

Memory breakdown

Weights67.7 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsCommand A 111B on NVIDIA H20 96GB
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: 52.2 tok/s decode · 3.7s TTFT (warm) · 131 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	Tight fit	52.2 tok/s	2022 ms	73K
Coding	S	Tight fit	47.9 tok/s	4046 ms	73K
Agentic Coding	S	Tight fit	52.2 tok/s	5391 ms	73K
Reasoning	S	Tight fit	52.2 tok/s	4380 ms	73K
RAG	S	Tight fit	52.2 tok/s	6738 ms	73K

Inference speed

Command A 111B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Command A 111B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~10 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?
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	9.7	Tight

Quantization options

How Command A 111B (111B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	43.3 GB	Low	S87
Q3_K_S	3	54.4 GB	Low	S88
NVFP4	4

Get started

Copy-paste commands to run Command A 111B on your machine.

Run

ollama run command-a

Your hardware

More models your NVIDIA H20 96GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	47 tok/s
Qwen 3.5 122B A10B	122B	S

Frequently asked questions

Can NVIDIA H20 96GB run Command A 111B?

Yes, NVIDIA H20 96GB can run Command A 111B with a S grade (Tight fit). Expected decode speed: 47.9 tok/s.

How much VRAM does Command A 111B need?

Command A 111B (111B parameters) requires approximately 82.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Command A 111B?

The recommended quantization for Command A 111B is Q4_K_M, which balances quality and memory efficiency.

What speed will Command A 111B run at on NVIDIA H20 96GB?

On NVIDIA H20 96GB, Command A 111B achieves approximately 47.9 tokens per second decode speed with a time-to-first-token of 4046ms using Q4_K_M quantization.

Can NVIDIA H20 96GB run Command A 111B for coding?

For coding workloads, Command A 111B on NVIDIA H20 96GB receives a S grade with 47.9 tok/s and 73K context.

What context window can Command A 111B use on NVIDIA H20 96GB?

On NVIDIA H20 96GB, Command A 111B can safely use up to 73K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for NVIDIA H20 96GB See all hardware for Command A 111B

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