Can DeepSeek R1 Distill 7B run on NVIDIA T4 16GB?

YES — Runs Great

B68Good

Estimated from fit model

DeepSeek R1 Distill 7B needs ~7.9 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 7.9 GB, 52.9 tok/s, Runs well

7.9 GB required16.0 GB available

49% VRAM used

Fit status

Runs well

Decode

52.9 tok/s

TTFT

3661 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 T4 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: 52.9 tok/s decode · 3.7s TTFT (warm) · 132 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	52.9 tok/s	1997 ms	33K
Coding	B	Runs well	52.9 tok/s	3661 ms	33K
Agentic Coding	B	Runs well	52.9 tok/s	5325 ms	33K
Reasoning	B	Runs well	52.9 tok/s	4326 ms	33K
RAG	B	Runs well	52.9 tok/s	6656 ms	33K

Quantization options

How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on NVIDIA T4 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

RX 7900 XT 20GBBest value

20 GB VRAM (+4)800 GB/s (+480)

Raises estimated decode speed by about 85%.98 tok/s decode

Raises estimated decode speed by about 85%.

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

~$899 MSRP

RTX A4500 20GBBudget pick

20 GB VRAM (+4)640 GB/s (+320)

Raises estimated decode speed by about 85%.98 tok/s decode

Raises estimated decode speed by about 85%.

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

~$2,000 MSRP

Frequently asked questions

Can NVIDIA T4 16GB run DeepSeek R1 Distill 7B?

Yes, NVIDIA T4 16GB can run DeepSeek R1 Distill 7B with a B grade (Runs well). Expected decode speed: 52.9 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 T4 16GB?

On NVIDIA T4 16GB, DeepSeek R1 Distill 7B achieves approximately 52.9 tokens per second decode speed with a time-to-first-token of 3661ms using Q4_K_M quantization.

Can NVIDIA T4 16GB run DeepSeek R1 Distill 7B for coding?

For coding workloads, DeepSeek R1 Distill 7B on NVIDIA T4 16GB receives a B grade with 52.9 tok/s and 33K context.

What context window can DeepSeek R1 Distill 7B use on NVIDIA T4 16GB?

On NVIDIA T4 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 T4 16GB See all hardware for DeepSeek R1 Distill 7B

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