Can Codestral RAG 19B Pruned i1 run on Tesla P40 24GB?

YES — Runs Great

C51Usable

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~17.4 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~18 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) — 17.4 GB, 17.6 tok/s, Runs well

17.4 GB required24.0 GB available

73% VRAM used

Fit status

Runs well

Decode

17.6 tok/s

TTFT

10992 ms

Safe context

63K

Memory

17.4 GB / 24.0 GB

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B Pruned i1 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: 17.6 tok/s decode · 11.0s TTFT (warm) · 44 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	C	Runs well	17.6 tok/s	5995 ms	63K
Coding	C	Runs well	17.6 tok/s	10992 ms	63K
Agentic Coding	C	Runs well	17.6 tok/s	15988 ms	63K
Reasoning	C	Runs well	17.6 tok/s	12990 ms	63K
RAG	C	Runs well	17.6 tok/s	19985 ms	63K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	C47
Q3_K_S	3	9.3 GB	Low	C48
NVFP4	4	10.6 GB	Medium	C49
Q4_K_M	4	11.6 GB	Medium	C49
Q5_K_M	5	13.7 GB	High	C50
Q6_KBest for your GPU	6	15.6 GB	High	C49
Q8_0	8	20.3 GB	Very High	F0
F16	16	38.9 GB	Maximum	F0

Get started

Copy-paste commands to run Codestral RAG 19B Pruned i1 on your machine.

Run

lms load hf-mradermacher--codestral-rag-19b-pruned-i1-gguf && lms server start

アップグレードオプション

Codestral RAG 19B Pruned i1を快適に動かすハードウェア

RTX 5090 32GBコスパ重視

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

Raises estimated decode speed by about 489%.103.6 tok/sデコード

Raises estimated decode speed by about 489%.

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

〜$1,999 MSRP

RTX PRO 4500 Blackwell 32GBコスパ最良

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

Raises estimated decode speed by about 269%.64.9 tok/sデコード

Raises estimated decode speed by about 269%.

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

〜$2,499 MSRP

NVIDIA V100 32GBNVIDIAアップグレード

32 GB VRAM (+8)900 GB/s (+554)

Raises estimated decode speed by about 195%.52 tok/sデコード

Raises estimated decode speed by about 195%.

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

〜$8,999 MSRP

Frequently asked questions

Can Tesla P40 24GB run Codestral RAG 19B Pruned i1?

Yes, Tesla P40 24GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs well). Expected decode speed: 17.6 tok/s.

How much VRAM does Codestral RAG 19B Pruned i1 need?

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 17.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral RAG 19B Pruned i1?

The recommended quantization for Codestral RAG 19B Pruned i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral RAG 19B Pruned i1 run at on Tesla P40 24GB?

On Tesla P40 24GB, Codestral RAG 19B Pruned i1 achieves approximately 17.6 tokens per second decode speed with a time-to-first-token of 10992ms using Q4_K_M quantization.

Can Tesla P40 24GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on Tesla P40 24GB receives a C grade with 17.6 tok/s and 63K context.

What context window can Codestral RAG 19B Pruned i1 use on Tesla P40 24GB?

On Tesla P40 24GB, Codestral RAG 19B Pruned i1 can safely use up to 63K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Tesla P40 24GB See all hardware for Codestral RAG 19B Pruned i1

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