Can DeepSeek R1 Distill 7B run on Tesla P40 24GB?

YES — Runs Great

B65Good

Estimated from fit model

DeepSeek R1 Distill 7B needs ~8.7 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~48 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, 51.9 tok/s, Runs well

8.7 GB required24.0 GB available

36% VRAM used

Fit status

Runs well

Decode

51.9 tok/s

TTFT

3730 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 Tesla P40 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: 51.9 tok/s decode · 3.7s TTFT (warm) · 130 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	51.9 tok/s	2034 ms	33K
Coding	B	Runs well	47.8 tok/s	4050 ms	33K
Agentic Coding	B	Runs well	51.9 tok/s	5425 ms	33K
Reasoning	B	Runs well	51.9 tok/s	4408 ms	33K
RAG	B	Runs well	51.9 tok/s	6782 ms	33K

Quantization options

How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on Tesla P40 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

Opções de upgrade

Hardware que roda bem DeepSeek R1 Distill 7B

RTX 5090 32GBOpção econômica

32 GB VRAM (+8)1792 GB/s (+1446)

Raises estimated decode speed by about 89%.98 tok/s decodificação

Raises estimated decode speed by about 89%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMelhor custo-benefício

32 GB VRAM (+8)896 GB/s (+550)

Raises estimated decode speed by about 89%.98 tok/s decodificação

Raises estimated decode speed by about 89%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBUpgrade NVIDIA

32 GB VRAM (+8)576 GB/s (+230)

Raises estimated decode speed by about 89%.98 tok/s decodificação

Raises estimated decode speed by about 89%.

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

~$4,000 MSRP

Frequently asked questions

Can Tesla P40 24GB run DeepSeek R1 Distill 7B?

Yes, Tesla P40 24GB can run DeepSeek R1 Distill 7B with a B grade (Runs well). Expected decode speed: 47.8 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 Tesla P40 24GB?

On Tesla P40 24GB, DeepSeek R1 Distill 7B achieves approximately 47.8 tokens per second decode speed with a time-to-first-token of 4050ms using Q4_K_M quantization.

Can Tesla P40 24GB run DeepSeek R1 Distill 7B for coding?

For coding workloads, DeepSeek R1 Distill 7B on Tesla P40 24GB receives a B grade with 47.8 tok/s and 33K context.

What context window can DeepSeek R1 Distill 7B use on Tesla P40 24GB?

On Tesla P40 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 Tesla P40 24GB See all hardware for DeepSeek R1 Distill 7B

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