Can Codestral 21B Pruned i1 run on RTX 2080 Ti 11GB?

YES — With Q2_K

D33Poor

Estimated from fit model

Codestral 21B Pruned i1 needs ~13.0 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q2_K quantization, expect ~21 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload

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.

Codestral 21B Pruned i1 at Q4_K_M needs 17.6 GB — too much for RTX 2080 Ti 11GB (11.0 GB). Runs at Q2_K (13.0 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 17.6 GB, exceeds 11.0 GB available

17.6 GB required11.0 GB available

160% VRAM needed

6.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

8.2 tok/s

TTFT

23750 ms

Safe context

Memory

17.6 GB / 11.0 GB

Offload

40%

Memory breakdown

Weights12.8 GB

KV Cache2.5 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodestral 21B Pruned i1 on RTX 2080 Ti 11GB
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: 8.2 tok/s decode · 23.8s TTFT (warm) · 20 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 1.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	9.6 tok/s	11045 ms	4K
Coding	F	Too heavy	8.2 tok/s	23750 ms	4K
Agentic Coding	F	Too heavy	6.1 tok/s	46068 ms	4K
Reasoning	F	Too heavy	8.2 tok/s	28068 ms	4K
RAG	F	Too heavy	6.1 tok/s	57585 ms	4K

Quantization options

How Codestral 21B Pruned i1 (21B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	F0
Q3_K_S	3	10.3 GB	Low	F0
NVFP4	4	11.8 GB	Medium	F0
Q4_K_M	4	12.8 GB	Medium	F0
Q5_K_M	5	15.1 GB	High	F0
Q6_K	6	17.2 GB	High	F0
Q8_0	8	22.5 GB	Very High	F0
F16	16	43.1 GB	Maximum	F0

Get started

Copy-paste commands to run Codestral 21B Pruned i1 on your machine.

Run

lms load hf-mradermacher--codestral-21b-pruned-i1-gguf && lms server start

Upgrade options

Hardware that runs Codestral 21B Pruned i1 well

RTX 5060 Ti 16GBBest value

16 GB VRAM (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.12.9 tok/s decode

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

Raises estimated decode speed by about 57%.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.9.5 tok/s decode

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

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

~$499 MSRP

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+9)

Makes the model fit on the accelerator instead of staying completely out of reach.21.9 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.

~$1,250 MSRP

RTX 4090 24GBBiggest leap

24 GB VRAM (+13)1008 GB/s (+392)

Makes the model fit on the accelerator instead of staying completely out of reach.59.8 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

Can RTX 2080 Ti 11GB run Codestral 21B Pruned i1?

Yes, RTX 2080 Ti 11GB can run Codestral 21B Pruned i1 at Q2_K quantization (Very compromised (needs ~1.2 GB host RAM)). The recommended Q4_K_M requires 17.6 GB which exceeds available memory, but at Q2_K it needs only 13.0 GB. Expected decode speed: 21.2 tok/s.

How much VRAM does Codestral 21B Pruned i1 need?

Codestral 21B Pruned i1 (21B parameters) requires approximately 17.6 GB at Q4_K_M quantization. On RTX 2080 Ti 11GB, it fits at Q2_K using 13.0 GB.

What is the best quantization for Codestral 21B Pruned i1?

The recommended quantization is Q4_K_M, but on RTX 2080 Ti 11GB the best fitting quantization is Q2_K, which uses 13.0 GB.

What speed will Codestral 21B Pruned i1 run at on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Codestral 21B Pruned i1 achieves approximately 21.2 tokens per second decode speed with a time-to-first-token of 9142ms using Q2_K quantization.

Can RTX 2080 Ti 11GB run Codestral 21B Pruned i1 for coding?

For coding workloads, Codestral 21B Pruned i1 on RTX 2080 Ti 11GB receives a F grade with 8.2 tok/s and 4K context.

What context window can Codestral 21B Pruned i1 use on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Codestral 21B Pruned i1 can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Codestral 21B Pruned i1 feels slow on RTX 2080 Ti 11GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 2080 Ti 11GB See all hardware for Codestral 21B Pruned i1

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Preview:

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 1.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

9.6 tok/s

11045 ms

Coding

Too heavy

8.2 tok/s

23750 ms

Agentic Coding

Too heavy

6.1 tok/s

46068 ms

Reasoning

Too heavy

8.2 tok/s

28068 ms

RAG

Too heavy

6.1 tok/s

57585 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

8.2 GB

Low

Q3_K_S

10.3 GB

Low

NVFP4

11.8 GB

Medium

Q4_K_M

12.8 GB

Medium

Q5_K_M

15.1 GB

High

Q6_K

17.2 GB

High

Q8_0

22.5 GB

Very High

F16

43.1 GB

Maximum