Can DeepSeek R1 1.5B run on NVIDIA L40 48GB?

YES — Runs Great

C51Usable

Estimated from fit model

DeepSeek R1 1.5B needs ~7.0 GB VRAM. NVIDIA L40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 7.0 GB, 24.0 tok/s, Runs well

7.0 GB required48.0 GB available

15% VRAM used

Fit status

Runs well

Decode

24.0 tok/s

TTFT

8067 ms

Safe context

33K

Memory

7.0 GB / 48.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.4 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsDeepSeek R1 1.5B on NVIDIA L40 48GB
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: 24.0 tok/s decode · 8.1s TTFT (warm) · 60 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	C	Runs well	24.0 tok/s	4400 ms	33K
Coding	C	Runs well	24.0 tok/s	8067 ms	33K
Agentic Coding	C	Runs well	24.0 tok/s	11733 ms	33K
Reasoning	C	Runs well	24.0 tok/s	9533 ms	33K
RAG	C	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 NVIDIA L40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C52
Q3_K_S	3	0.7 GB	Low	C52
NVFP4	4

Get started

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

Run

ollama run deepseek-r1:1.5b

Upgrade options

Hardware that runs DeepSeek R1 1.5B well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+48)

Adds memory headroom for longer context windows and future model growth.21 tok/s decode

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

~$2,499 MSRP

MacBook Pro M3 Max 128GBBest value

128 GB Unified (+80)

Adds memory headroom for longer context windows and future model growth.21 tok/s decode

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

~$2,499 MSRP

Frequently asked questions

Can NVIDIA L40 48GB run DeepSeek R1 1.5B?

Yes, NVIDIA L40 48GB can run DeepSeek R1 1.5B with a C grade (Runs well). Expected decode speed: 24.0 tok/s.

How much VRAM does DeepSeek R1 1.5B need?

DeepSeek R1 1.5B (1.5B parameters) requires approximately 7.0 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 NVIDIA L40 48GB?

On NVIDIA L40 48GB, DeepSeek R1 1.5B achieves approximately 24.0 tokens per second decode speed with a time-to-first-token of 8067ms using Q4_K_M quantization.

Can NVIDIA L40 48GB run DeepSeek R1 1.5B for coding?

For coding workloads, DeepSeek R1 1.5B on NVIDIA L40 48GB receives a C grade with 24.0 tok/s and 33K context.

What context window can DeepSeek R1 1.5B use on NVIDIA L40 48GB?

On NVIDIA L40 48GB, 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 NVIDIA L40 48GB See all hardware for DeepSeek R1 1.5B

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