Can Codestral RAG 19B Pruned i1 run on GTX 1660 Ti 6GB?

NO — Won't Fit

F0Won't run

○Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~15.6 GB but GTX 1660 Ti 6GB only has 6.0 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) — 15.6 GB, exceeds 6.0 GB available

15.6 GB required6.0 GB available

260% VRAM needed

9.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.1 tok/s

TTFT

94437 ms

Safe context

Memory

15.6 GB / 6.0 GB

Offload

60%

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodestral RAG 19B Pruned i1 on GTX 1660 Ti 6GB
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: 2.1 tok/s decode · 94.4s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 15.6 GB, but this setup only exposes 6.0 GB of usable VRAM.

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

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	2.1 tok/s	51511 ms	4K
Coding	F	Too heavy	2.1 tok/s	94437 ms	4K
Agentic Coding	F	Too heavy	2.1 tok/s	137363 ms	4K
Reasoning	F	Too heavy	2.1 tok/s	111607 ms	4K
RAG	F	Too heavy	2.1 tok/s	171704 ms	4K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on GTX 1660 Ti 6GB (6.0 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

Upgrade options

Hardware that runs Codestral RAG 19B Pruned i1 well

👁 NVIDIA
RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+10)448 GB/s (+160)

Makes the model fit on the accelerator instead of staying completely out of reach.17 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$449 MSRP

👁 NVIDIA
RTX 4060 Ti 16GBBest value

16 GB VRAM (+10)

Makes the model fit on the accelerator instead of staying completely out of reach.12.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$499 MSRP

👁 NVIDIA
RTX 2000 Ada 16GBNVIDIA upgrade

16 GB VRAM (+10)

Makes the model fit on the accelerator instead of staying completely out of reach.13.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$625 MSRP

👁 NVIDIA
RTX 4090 24GBBiggest leap

24 GB VRAM (+18)1008 GB/s (+720)

Makes the model fit on the accelerator instead of staying completely out of reach.66.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

Frequently asked questions

See all results for GTX 1660 Ti 6GB See all hardware for Codestral RAG 19B Pruned i1

URL: https://willitrunai.com/can-run/hf-mradermacher--codestral-rag-19b-pruned-i1-gguf-on-gtx-1660-ti-6gb

⇱

Can Codestral RAG 19B Pruned i1 run on GTX 1660 Ti 6GB?

NO — Won't Fit

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Hardware that runs Codestral RAG 19B Pruned i1 well

Frequently asked questions