Can DeepSeek R1 1.5B run on GTX 1070 Ti 8GB?

YES — Runs Great

B57Good

Estimated from fit model

DeepSeek R1 1.5B needs ~3.3 GB VRAM. GTX 1070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~21 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) — 3.3 GB, 21.0 tok/s, Runs well

3.3 GB required8.0 GB available

41% VRAM used

Fit status

Runs well

Decode

21.0 tok/s

TTFT

9219 ms

Safe context

33K

Memory

3.3 GB / 8.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.4 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsDeepSeek R1 1.5B on GTX 1070 Ti 8GB
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: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 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	21.0 tok/s	5029 ms	33K
Coding	B	Runs well	21.0 tok/s	9219 ms	33K
Agentic Coding	B	Runs well	21.0 tok/s	13410 ms	33K
Reasoning	B	Runs well	21.0 tok/s	10895 ms	33K
RAG	B	Runs well	21.0 tok/s	16762 ms	33K

Quantization options

How DeepSeek R1 1.5B (1.5B params) fits at each quantization level on GTX 1070 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	B60
Q3_K_S	3	0.7 GB	Low	B60
NVFP4	4	0.8 GB	Medium	B60
Q4_K_M	4	0.9 GB	Medium	B60
Q5_K_M	5	1.1 GB	High	B61
Q6_K	6	1.2 GB	High	B61
Q8_0	8	1.6 GB	Very High	B62
F16Best for your GPU	16	3.1 GB	Maximum	B64

Get started

Copy-paste commands to run DeepSeek R1 1.5B on your machine.

Run

ollama run deepseek-r1:1.5b

Frequently asked questions

Can GTX 1070 Ti 8GB run DeepSeek R1 1.5B?

Yes, GTX 1070 Ti 8GB can run DeepSeek R1 1.5B with a B grade (Runs well). Expected decode speed: 21.0 tok/s.

How much VRAM does DeepSeek R1 1.5B need?

DeepSeek R1 1.5B (1.5B parameters) requires approximately 3.3 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 1.5B?

The recommended quantization for DeepSeek R1 1.5B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 1.5B run at on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, DeepSeek R1 1.5B achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.

Can GTX 1070 Ti 8GB run DeepSeek R1 1.5B for coding?

For coding workloads, DeepSeek R1 1.5B on GTX 1070 Ti 8GB receives a B grade with 21.0 tok/s and 33K context.

What context window can DeepSeek R1 1.5B use on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, DeepSeek R1 1.5B 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 GTX 1070 Ti 8GB See all hardware for DeepSeek R1 1.5B

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<iframe src="https://willitrunai.com/embed/deepseek-r1-distill-qwen-1.5b-on-gtx-1070-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

21.0 tok/s

5029 ms

33K

Coding

Runs well

21.0 tok/s

9219 ms

33K

Agentic Coding

Runs well

21.0 tok/s

13410 ms

33K

Reasoning

Runs well

21.0 tok/s

10895 ms

33K

RAG

Runs well

21.0 tok/s

16762 ms

33K

Quantization options

How DeepSeek R1 1.5B (1.5B params) fits at each quantization level on GTX 1070 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	B60
Q3_K_S	3	0.7 GB	Low	B60
NVFP4	4	0.8 GB	Medium	B60
Q4_K_M	4	0.9 GB	Medium	B60
Q5_K_M	5	1.1 GB	High	B61
Q6_K	6	1.2 GB	High	B61
Q8_0	8	1.6 GB	Very High	B62
F16Best for your GPU	16	3.1 GB	Maximum	B64

Frequently asked questions

Can GTX 1070 Ti 8GB run DeepSeek R1 1.5B?

Yes, GTX 1070 Ti 8GB can run DeepSeek R1 1.5B with a B grade (Runs well). Expected decode speed: 21.0 tok/s.

How much VRAM does DeepSeek R1 1.5B need?

DeepSeek R1 1.5B (1.5B parameters) requires approximately 3.3 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 1.5B?

The recommended quantization for DeepSeek R1 1.5B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 1.5B run at on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, DeepSeek R1 1.5B achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.

Can GTX 1070 Ti 8GB run DeepSeek R1 1.5B for coding?

For coding workloads, DeepSeek R1 1.5B on GTX 1070 Ti 8GB receives a B grade with 21.0 tok/s and 33K context.

What context window can DeepSeek R1 1.5B use on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, DeepSeek R1 1.5B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.