Can Command A 111B run on NVIDIA A100 80GB?

YES — With Offload

S90Excellent

Estimated from fit model

Command A 111B needs ~80.5 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~23 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 80.5 GB, 23.3 tok/s, Runs with offload (needs ~0.4 GB host RAM)

80.5 GB required80.0 GB available

101% VRAM needed

0.5 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.4 GB host RAM)

Decode

23.3 tok/s

TTFT

8307 ms

Safe context

14K

Memory

80.5 GB / 80.0 GB

Memory breakdown

Weights67.7 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsCommand A 111B on NVIDIA A100 80GB
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: 23.3 tok/s decode · 8.3s TTFT (warm) · 58 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs with offload	27.6 tok/s	3824 ms	14K
Coding	S	Runs with offload (needs ~0.4 GB host RAM)	23.3 tok/s	8307 ms	14K
Agentic Coding	A	Runs with offload (needs ~3.5 GB host RAM)	21.6 tok/s	13066 ms	14K
Reasoning	S	Runs with offload (needs ~0.4 GB host RAM)	23.3 tok/s	9818 ms	14K
RAG	A	Runs with offload (needs ~3.5 GB host RAM)	21.6 tok/s	16332 ms	14K

Quantization options

How Command A 111B (111B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	43.3 GB	Low	S88
Q3_K_S	3	54.4 GB	Low	S88
NVFP4Best for your GPU	4	62.2 GB	Medium	S88
Q4_K_M	4	67.7 GB	Medium	F0
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 A100 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	17.7 tok/s
Qwen 3.5 122B A10B	122B	A	52.4 tok/s
Mistral Small 4 119B	119B	A	55.6 tok/s
GPT-OSS 120B	117B	A	20.1 tok/s

Frequently asked questions

Can NVIDIA A100 80GB run Command A 111B?

Yes, NVIDIA A100 80GB can run Command A 111B with a S grade (Runs with offload (needs ~0.4 GB host RAM)). Expected decode speed: 23.3 tok/s.

How much VRAM does Command A 111B need?

Command A 111B (111B parameters) requires approximately 80.5 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 A100 80GB?

On NVIDIA A100 80GB, Command A 111B achieves approximately 23.3 tokens per second decode speed with a time-to-first-token of 8307ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Command A 111B for coding?

For coding workloads, Command A 111B on NVIDIA A100 80GB receives a S grade with 23.3 tok/s and 14K context.

What context window can Command A 111B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Command A 111B can safely use up to 14K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Command A 111B feels slow on NVIDIA A100 80GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for NVIDIA A100 80GB See all hardware for Command A 111B

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Preview:

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs with offload	27.6 tok/s	3824 ms	14K
Coding	S	Runs with offload (needs ~0.4 GB host RAM)	23.3 tok/s	8307 ms	14K
Agentic Coding	A	Runs with offload (needs ~3.5 GB host RAM)	21.6 tok/s	13066 ms	14K
Reasoning	S	Runs with offload (needs ~0.4 GB host RAM)	23.3 tok/s	9818 ms	14K
RAG	A	Runs with offload (needs ~3.5 GB host RAM)	21.6 tok/s	16332 ms	14K

Quantization options

How Command A 111B (111B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	43.3 GB	Low	S88
Q3_K_S	3	54.4 GB	Low	S88
NVFP4Best for your GPU	4	62.2 GB	Medium	S88
Q4_K_M	4	67.7 GB	Medium	F0
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

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

17.7 tok/s

Qwen 3.5 122B A10B

122B

52.4 tok/s

Mistral Small 4 119B

119B

55.6 tok/s

GPT-OSS 120B

117B

20.1 tok/s