Can DeepSeek R1 Distill 7B run on NVIDIA L4 24GB?

YES — Runs Great

B64Good

Estimated from fit model

DeepSeek R1 Distill 7B needs ~8.7 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~46 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) — 8.7 GB, 49.6 tok/s, Runs well

8.7 GB required24.0 GB available

36% VRAM used

Fit status

Runs well

Decode

49.6 tok/s

TTFT

3905 ms

Safe context

33K

Memory

8.7 GB / 24.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 7B on NVIDIA L4 24GB
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: 49.6 tok/s decode · 3.9s TTFT (warm) · 124 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	49.6 tok/s	2130 ms	33K
Coding	B	Runs well	45.7 tok/s	4239 ms	33K
Agentic Coding	B	Runs well	49.6 tok/s	5680 ms	33K
Reasoning	B	Runs well	49.6 tok/s	4615 ms	33K
RAG	B	Runs well	49.6 tok/s	7099 ms	33K

Quantization options

How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B61
Q3_K_S	3	3.4 GB	Low	B62
NVFP4	4	3.9 GB	Medium	B62
Q4_K_M	4	4.3 GB	Medium	B62
Q5_K_M	5	5.0 GB	High	B62
Q6_K	6	5.7 GB	High	B63
Q8_0	8	7.5 GB	Very High	B64
F16Best for your GPU	16	14.3 GB	Maximum	B67

Get started

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

Run

ollama run deepseek-r1:7b

Opciones de mejora

Hardware que ejecuta bien DeepSeek R1 Distill 7B

RTX 5090 32GBOpción económica

32 GB VRAM (+8)1792 GB/s (+1492)

Sube la velocidad estimada de decodificación alrededor de un 98%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 98%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMejor relación calidad-precio

32 GB VRAM (+8)896 GB/s (+596)

Sube la velocidad estimada de decodificación alrededor de un 98%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 98%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

RTX 5000 Ada 32GBMejora NVIDIA

32 GB VRAM (+8)576 GB/s (+276)

Sube la velocidad estimada de decodificación alrededor de un 98%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 98%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run DeepSeek R1 Distill 7B?

Yes, NVIDIA L4 24GB can run DeepSeek R1 Distill 7B with a B grade (Runs well). Expected decode speed: 45.7 tok/s.

How much VRAM does DeepSeek R1 Distill 7B need?

DeepSeek R1 Distill 7B (7B parameters) requires approximately 8.7 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 L4 24GB?

On NVIDIA L4 24GB, DeepSeek R1 Distill 7B achieves approximately 45.7 tokens per second decode speed with a time-to-first-token of 4239ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run DeepSeek R1 Distill 7B for coding?

For coding workloads, DeepSeek R1 Distill 7B on NVIDIA L4 24GB receives a B grade with 45.7 tok/s and 33K context.

What context window can DeepSeek R1 Distill 7B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, 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 L4 24GB See all hardware for DeepSeek R1 Distill 7B

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