Can DeepSeek R1 Distill 7B run on NVIDIA A2 16GB?

YES — Runs Great

B67Good

Estimated from fit model

DeepSeek R1 Distill 7B needs ~7.9 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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) — 7.9 GB, 39.7 tok/s, Runs well

7.9 GB required16.0 GB available

49% VRAM used

Fit status

Runs well

Decode

39.7 tok/s

TTFT

4881 ms

Safe context

33K

Memory

7.9 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 7B on NVIDIA A2 16GB
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: 39.7 tok/s decode · 4.9s TTFT (warm) · 99 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	B	Runs well	39.7 tok/s	2662 ms	33K
Coding	B	Runs well	39.7 tok/s	4881 ms	33K
Agentic Coding	B	Runs well	39.7 tok/s	7099 ms	33K
Reasoning	B	Runs well	39.7 tok/s	5768 ms	33K
RAG	B	Runs well	39.7 tok/s	8874 ms	33K

Quantization options

How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B64
Q3_K_S	3	3.4 GB	Low	B64
NVFP4	4

Get started

Copy-paste commands to run DeepSeek R1 Distill 7B on your machine.

Run

ollama run deepseek-r1:7b

Upgrade options

Hardware that runs DeepSeek R1 Distill 7B well

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+4)360 GB/s (+160)

Raises estimated decode speed by about 80%.71.4 tok/s decode

Raises estimated decode speed by about 80%.

Adds memory headroom for longer context windows and future model growth.

~$1,250 MSRP

RTX A4500 20GBBest value

20 GB VRAM (+4)640 GB/s (+440)

Raises estimated decode speed by about 147%.98 tok/s decode

Raises estimated decode speed by about 147%.

Adds memory headroom for longer context windows and future model growth.

~$2,000 MSRP

Frequently asked questions

Can NVIDIA A2 16GB run DeepSeek R1 Distill 7B?

Yes, NVIDIA A2 16GB can run DeepSeek R1 Distill 7B with a B grade (Runs well). Expected decode speed: 39.7 tok/s.

How much VRAM does DeepSeek R1 Distill 7B need?

DeepSeek R1 Distill 7B (7B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill 7B?

The recommended quantization for DeepSeek R1 Distill 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill 7B run at on NVIDIA A2 16GB?

On NVIDIA A2 16GB, DeepSeek R1 Distill 7B achieves approximately 39.7 tokens per second decode speed with a time-to-first-token of 4881ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run DeepSeek R1 Distill 7B for coding?

For coding workloads, DeepSeek R1 Distill 7B on NVIDIA A2 16GB receives a B grade with 39.7 tok/s and 33K context.

What context window can DeepSeek R1 Distill 7B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, DeepSeek R1 Distill 7B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for NVIDIA A2 16GB See all hardware for DeepSeek R1 Distill 7B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/deepseek-r1-distill-qwen-7b-on-a2-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: