Can Command A 111B run on NVIDIA H200 PCIe 141GB?

YES — Runs Great

S94Excellent

Estimated from fit model

Command A 111B needs ~86.6 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q4_K_M quantization, expect ~65 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 86.6 GB, 65.0 tok/s, Runs well

86.6 GB required141.0 GB available

61% VRAM used

Fit status

Runs well

Decode

65.0 tok/s

TTFT

2978 ms

Safe context

239K

Memory

86.6 GB / 141.0 GB

Memory breakdown

Weights67.7 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsCommand A 111B on NVIDIA H200 PCIe 141GB
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: 65.0 tok/s decode · 3.0s TTFT (warm) · 163 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	65.0 tok/s	1624 ms	239K
Coding	S	Runs well	65.0 tok/s	2978 ms	239K
Agentic Coding	S	Runs well	65.0 tok/s	4332 ms	239K
Reasoning	S	Runs well	65.0 tok/s	3520 ms	239K
RAG	S	Runs well	65.0 tok/s	5415 ms	239K

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 H200 PCIe 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	43.3 GB	Low	A83
Q3_K_S	3	54.4 GB	Low	A85
NVFP4	4	62.2 GB	Medium	S86
Q4_K_M	4	67.7 GB	Medium	S87
Q5_K_M	5	79.9 GB	High	S88
Q6_K	6	91.0 GB	High	S88
Q8_0Best for your GPU	8	118.8 GB	Very High	S88
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 H200 PCIe 141GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	58.4 tok/s
Qwen 3.5 122B A10B	122B	S	162.1 tok/s
Mistral Small 4 119B	119B	S	175.8 tok/s
GPT-OSS 120B	117B	S	61.4 tok/s

Frequently asked questions

Can NVIDIA H200 PCIe 141GB run Command A 111B?

Yes, NVIDIA H200 PCIe 141GB can run Command A 111B with a S grade (Runs well). Expected decode speed: 65.0 tok/s.

How much VRAM does Command A 111B need?

Command A 111B (111B parameters) requires approximately 86.6 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 H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, Command A 111B achieves approximately 65.0 tokens per second decode speed with a time-to-first-token of 2978ms using Q4_K_M quantization.

Can NVIDIA H200 PCIe 141GB run Command A 111B for coding?

For coding workloads, Command A 111B on NVIDIA H200 PCIe 141GB receives a S grade with 65.0 tok/s and 239K context.

What context window can Command A 111B use on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, Command A 111B can safely use up to 239K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for NVIDIA H200 PCIe 141GB See all hardware for Command A 111B

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