Can Codestral RAG 19B Pruned i1 run on RTX 3000 Ada Laptop 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~15.8 GB but RTX 3000 Ada Laptop 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

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) — 15.8 GB, exceeds 8.0 GB available

15.8 GB required8.0 GB available

198% VRAM needed

7.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.2 tok/s

TTFT

59750 ms

Safe context

Memory

15.8 GB / 8.0 GB

Offload

50%

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodestral RAG 19B Pruned i1 on RTX 3000 Ada Laptop 8GB
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: 3.2 tok/s decode · 59.8s TTFT (warm) · 8 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 15.8 GB, but this setup only exposes 8.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	3.8 tok/s	27949 ms	4K
Coding	F	Too heavy	3.2 tok/s	59750 ms	4K
Agentic Coding	F	Too heavy	2.7 tok/s	103493 ms	4K
Reasoning	F	Too heavy	3.2 tok/s	70614 ms	4K
RAG	F	Too heavy	2.7 tok/s	129366 ms	4K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on RTX 3000 Ada Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	F0
Q3_K_S	3	9.3 GB	Low	F0
NVFP4	4	10.6 GB	Medium	F0
Q4_K_M	4	11.6 GB	Medium	F0
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

Opciones de mejora

Hardware que ejecuta bien Codestral RAG 19B Pruned i1

RTX 5060 Ti 16GBOpción económica

16 GB VRAM (+8)448 GB/s (+160)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.17 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$449 MSRP

RTX 4060 Ti 16GBMejor relación calidad-precio

16 GB VRAM (+8)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.12.6 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$499 MSRP

RTX 5070 Ti 16GBMejora NVIDIA

16 GB VRAM (+8)896 GB/s (+608)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.35 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$749 MSRP

RTX 4090 24GBMayor salto

24 GB VRAM (+16)1008 GB/s (+720)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.66.1 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,599 MSRP

Frequently asked questions

Can RTX 3000 Ada Laptop 8GB run Codestral RAG 19B Pruned i1?

No, Codestral RAG 19B Pruned i1 requires more memory than RTX 3000 Ada Laptop 8GB provides.

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

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 15.8 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 RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 8GB, Codestral RAG 19B Pruned i1 achieves approximately 3.2 tokens per second decode speed with a time-to-first-token of 59750ms using Q4_K_M quantization.

Can RTX 3000 Ada Laptop 8GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on RTX 3000 Ada Laptop 8GB receives a F grade with 3.2 tok/s and 4K context.

What context window can Codestral RAG 19B Pruned i1 use on RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 8GB, Codestral RAG 19B Pruned i1 can safely use up to 4K 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 RTX 3000 Ada Laptop 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX 3000 Ada Laptop 8GB See all hardware for Codestral RAG 19B Pruned i1

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