Can Command A 111B run on NVIDIA A16 64GB?

YES — With NVFP4

A75Great

○Estimated from fit model

Command A 111B needs ~73.4 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With NVFP4 quantization, expect ~5 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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.

Command A 111B at Q4_K_M needs 78.9 GB — too much for NVIDIA A16 64GB (64.0 GB). Runs at NVFP4 (73.4 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 78.9 GB, exceeds 64.0 GB available

78.9 GB required64.0 GB available

123% VRAM needed

14.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.6 tok/s

TTFT

53173 ms

Safe context

Memory

78.9 GB / 64.0 GB

Offload

20%

Memory breakdown

Weights67.7 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom6.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCommand A 111B on NVIDIA A16 64GB
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: 3.6 tok/s decode · 53.2s TTFT (warm) · 9 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 7.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	3.5 tok/s	30035 ms	4K
Coding	F	Too heavy	3.3 tok/s	58047 ms	4K
Agentic Coding	F	Too heavy	3.0 tok/s	93470 ms	4K
Reasoning	F	Too heavy	3.3 tok/s	68601 ms	4K
RAG	F	Too heavy	3.0 tok/s	116837 ms	4K

Quantization options

How Command A 111B (111B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	43.3 GB	Low	S88
Q3_K_S	3	54.4 GB	Low	F0

Get started

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

Run

ollama run command-a

Upgrade options

Hardware that runs Command A 111B well

👁 NVIDIA
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick

96 GB VRAM (+32)1792 GB/s (+1192)

Makes the model fit on the accelerator instead of staying completely out of reach.24.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$9,999 MSRP

👁 NVIDIA
RTX PRO 6000 Blackwell Server Edition 96GBBest value

96 GB VRAM (+32)1597 GB/s (+997)

Makes the model fit on the accelerator instead of staying completely out of reach.21.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$9,999 MSRP

👁 NVIDIA
NVIDIA H20 96GBNVIDIA upgrade

96 GB VRAM (+32)4000 GB/s (+3400)

Makes the model fit on the accelerator instead of staying completely out of reach.52.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$12,000 MSRP

Frequently asked questions

See all results for NVIDIA A16 64GB See all hardware for Command A 111B

URL: https://willitrunai.com/can-run/command-a-111b-on-a16-64gb

⇱

Can Command A 111B run on NVIDIA A16 64GB?

YES — With NVFP4

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Command A 111B well

Frequently asked questions