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 ~52 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	52.2 tok/s	3706 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
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	9.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	7.5	Tight
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	7.1	Tight
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	5.2	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	4.8	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	3.7	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	3.1	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	2.8	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.7	Too big
RTX 5090 32GB	32 GB	Q4_K_M	2.0	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	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
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.0	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M1 Max 64GB	64 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 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	62.2 GB	Medium	S88
Q4_K_MBest for your GPU	4	67.7 GB	Medium	S88
Q5_K_M	5	79.9 GB	High	F0
Q6_K	6	91.0 GB	High	F0
Q8_0	8	118.8 GB	Very High	F0
F16	16	227.6 GB	Maximum	F0

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
Devstral 2 123B Instruct	123B	S	47 tok/s
Qwen 3.5 122B A10B	122B	S	130.3 tok/s
Mistral Small 4 119B	119B	S	141.2 tok/s
GPT-OSS 120B	117B	S	49.4 tok/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: 52.2 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 52.2 tokens per second decode speed with a time-to-first-token of 3706ms 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 52.2 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

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/command-a-111b-on-h20-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: