Can Codestral RAG 19B Pruned i1 run on Tesla P100 16GB?

YES — With Offload

C49Usable

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~16.3 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: 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) — 16.3 GB, 26.0 tok/s, Runs with offload (needs ~0.2 GB host RAM)

16.3 GB required16.0 GB available

102% VRAM needed

0.3 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

26.0 tok/s

TTFT

7436 ms

Safe context

14K

Memory

16.3 GB / 16.0 GB

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B Pruned i1 on Tesla P100 16GB
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: 26.0 tok/s decode · 7.4s TTFT (warm) · 65 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload	37.3 tok/s	2834 ms	14K
Coding	C	Runs with offload (needs ~0.2 GB host RAM)	26.0 tok/s	7436 ms	14K
Agentic Coding	D	Very compromised (needs ~1.6 GB host RAM)	19.7 tok/s	14324 ms	14K
Reasoning	C	Runs with offload (needs ~0.2 GB host RAM)	26.0 tok/s	8788 ms	14K
RAG	D	Very compromised (needs ~1.6 GB host RAM)	19.7 tok/s	17905 ms	14K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	C51
Q3_K_S	3	9.3 GB	Low	C51
NVFP4	4	10.6 GB	Medium	C50
Q4_K_MBest for your GPU	4	11.6 GB	Medium	C50
Q5_K_M	5	13.7 GB	High	F0
Q6_K	6	15.6 GB	High	F0
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

Opciones de mejora

Hardware que ejecuta bien Codestral RAG 19B Pruned i1

RTX 3090 24GBOpción económica

24 GB VRAM (+8)936 GB/s (+204)

Sube la velocidad estimada de decodificación alrededor de un 95%.50.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 95%.

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

~$1,499 MSRP

RTX 4090 24GBMejor relación calidad-precio

24 GB VRAM (+8)1008 GB/s (+276)

Sube la velocidad estimada de decodificación alrededor de un 112%.55.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 112%.

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

~$1,599 MSRP

RTX PRO 4000 Blackwell 24GBMejora NVIDIA

24 GB VRAM (+8)

Sube la velocidad estimada de decodificación alrededor de un 87%.48.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 87%.

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

~$1,599 MSRP

Frequently asked questions

Can Tesla P100 16GB run Codestral RAG 19B Pruned i1?

Yes, Tesla P100 16GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 26.0 tok/s.

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

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 16.3 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 P100 16GB?

On Tesla P100 16GB, Codestral RAG 19B Pruned i1 achieves approximately 26.0 tokens per second decode speed with a time-to-first-token of 7436ms using Q4_K_M quantization.

Can Tesla P100 16GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on Tesla P100 16GB receives a C grade with 26.0 tok/s and 14K context.

What context window can Codestral RAG 19B Pruned i1 use on Tesla P100 16GB?

On Tesla P100 16GB, Codestral RAG 19B Pruned i1 can safely use up to 14K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Codestral RAG 19B Pruned i1 feels slow on Tesla P100 16GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for Tesla P100 16GB See all hardware for Codestral RAG 19B Pruned i1

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