Can DeepSeek R1 1.5B run on RTX 3000 Ada Laptop 8GB?

YES — Runs Great

B57Good

Estimated from fit model

DeepSeek R1 1.5B needs ~3.3 GB VRAM. RTX 3000 Ada Laptop 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 RTX 3000 Ada Laptop 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.

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	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

Inference speed

DeepSeek R1 1.5B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek R1 1.5B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~29 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	28.5	Fits
RTX 4090 24GB	24 GB	Q4_K_M	24.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	24.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	21.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	21.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	21.0	Fits
RTX 4060 8GB	8 GB	Q4_K_M	21.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	21.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	21.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	21.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	21.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	21.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	21.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	21.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	21.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.0	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 1.5B (1.5B params) fits at each quantization level on RTX 3000 Ada Laptop 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 RTX 3000 Ada Laptop 8GB run DeepSeek R1 1.5B?

Yes, RTX 3000 Ada Laptop 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 RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 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 RTX 3000 Ada Laptop 8GB run DeepSeek R1 1.5B for coding?

For coding workloads, DeepSeek R1 1.5B on RTX 3000 Ada Laptop 8GB receives a B grade with 21.0 tok/s and 33K context.

What context window can DeepSeek R1 1.5B use on RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 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 RTX 3000 Ada Laptop 8GB See all hardware for DeepSeek R1 1.5B

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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

DeepSeek R1 1.5B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

28.5

Fits

RTX 4090 24GB

24 GB

Q4_K_M

24.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

24.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

21.0

Fits

RTX 4070 12GB

12 GB

Q4_K_M

21.0

Fits

RTX 3060 12GB

12 GB

Q4_K_M

21.0

Fits

RTX 4060 8GB

8 GB

Q4_K_M

21.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

21.0

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

21.0

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

21.0

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

21.0

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

21.0

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

21.0

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

21.0

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

21.0

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

21.0

Fits

Quantization options

How DeepSeek R1 1.5B (1.5B params) fits at each quantization level on RTX 3000 Ada Laptop 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 RTX 3000 Ada Laptop 8GB run DeepSeek R1 1.5B?

Yes, RTX 3000 Ada Laptop 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 RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 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 RTX 3000 Ada Laptop 8GB run DeepSeek R1 1.5B for coding?

For coding workloads, DeepSeek R1 1.5B on RTX 3000 Ada Laptop 8GB receives a B grade with 21.0 tok/s and 33K context.

What context window can DeepSeek R1 1.5B use on RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 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.