Can DeepSeek R1 Distill 7B run on GTX 1070 Ti 8GB?

YES — Tight Fit

B68Good

Estimated from fit model

DeepSeek R1 Distill 7B needs ~7.1 GB VRAM. GTX 1070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 7.1 GB, 38.4 tok/s, Tight fit

7.1 GB required8.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

38.4 tok/s

TTFT

5041 ms

Safe context

32K

Memory

7.1 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 7B 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: 38.4 tok/s decode · 5.0s TTFT (warm) · 96 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	Tight fit	38.4 tok/s	2750 ms	32K
Coding	B	Tight fit	38.4 tok/s	5041 ms	32K
Agentic Coding	B	Runs with offload	38.4 tok/s	7333 ms	32K
Reasoning	B	Tight fit	38.4 tok/s	5958 ms	32K
RAG	B	Runs with offload	38.4 tok/s	9166 ms	32K

Inference speed

DeepSeek R1 Distill 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek R1 Distill 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	96.1	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	95.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	95.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	61.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	60.4	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	55.9	Fits
RTX 4060 8GB	8 GB	Q4_K_M	50.5	Tight
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	49.2	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on GTX 1070 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A70
Q3_K_S	3	3.4 GB	Low	A71
NVFP4	4	3.9 GB	Medium	A70
Q4_K_M	4	4.3 GB	Medium	A70
Q5_K_MBest for your GPU	5	5.0 GB	High	B70
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

Get started

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

Run

ollama run deepseek-r1:7b

升级选项

能流畅运行 DeepSeek R1 Distill 7B 的硬件

RTX 3060 12GB经济之选

12 GB VRAM (+4)360 GB/s (+104)

Raises estimated decode speed by about 57%.60.4 tok/s 解码

Raises estimated decode speed by about 57%.

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

~$329 MSRP

RTX 5060 Ti 16GB最佳性价比

16 GB VRAM (+8)448 GB/s (+192)

Raises estimated decode speed by about 84%.70.6 tok/s 解码

Raises estimated decode speed by about 84%.

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

~$449 MSRP

RTX 5070 12GBNVIDIA 升级

12 GB VRAM (+4)672 GB/s (+416)

Raises estimated decode speed by about 155%.98 tok/s 解码

Raises estimated decode speed by about 155%.

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

~$549 MSRP

Frequently asked questions

Can GTX 1070 Ti 8GB run DeepSeek R1 Distill 7B?

Yes, GTX 1070 Ti 8GB can run DeepSeek R1 Distill 7B with a B grade (Tight fit). Expected decode speed: 38.4 tok/s.

How much VRAM does DeepSeek R1 Distill 7B need?

DeepSeek R1 Distill 7B (7B parameters) requires approximately 7.1 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 GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, DeepSeek R1 Distill 7B achieves approximately 38.4 tokens per second decode speed with a time-to-first-token of 5041ms using Q4_K_M quantization.

Can GTX 1070 Ti 8GB run DeepSeek R1 Distill 7B for coding?

For coding workloads, DeepSeek R1 Distill 7B on GTX 1070 Ti 8GB receives a B grade with 38.4 tok/s and 32K context.

What context window can DeepSeek R1 Distill 7B use on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, DeepSeek R1 Distill 7B can safely use up to 32K 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 Distill 7B

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