Can Codestral 22B v0.1 run on Tesla P100 16GB?

Q: What should I upgrade first if Codestral 22B v0.1 feels slow on Tesla P100 16GB?

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.

BARELY — Tight on Memory

D38Poor

Estimated from fit model

Codestral 22B v0.1 needs ~18.5 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~17 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 18.5 GB, 17.1 tok/s, Very compromised (needs ~1.8 GB host RAM)

18.5 GB required16.0 GB available

116% VRAM needed

2.5 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.8 GB host RAM)

Decode

17.1 tok/s

TTFT

11342 ms

Safe context

Memory

18.5 GB / 16.0 GB

Offload

10%

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 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: 17.1 tok/s decode · 11.3s TTFT (warm) · 43 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 10% 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 1.8 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	D	Runs with offload (needs ~0.9 GB host RAM)	20.0 tok/s	5279 ms	4K
Coding	D	Very compromised (needs ~1.8 GB host RAM)	17.1 tok/s	11342 ms	4K
Agentic Coding	F	Too heavy	12.8 tok/s	21971 ms	4K
Reasoning	D	Very compromised (needs ~1.8 GB host RAM)	17.1 tok/s	13404 ms	4K
RAG	F	Too heavy	12.8 tok/s	27464 ms

Quantization options

How Codestral 22B v0.1 (22B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C51
Q3_K_SBest for your GPU	3	10.8 GB	Low	C51

Get started

Copy-paste commands to run Codestral 22B v0.1 on your machine.

Run

lms load hf-lmstudio-community--codestral-22b-v0-1-gguf && lms server start

Upgrade options

Hardware that runs Codestral 22B v0.1 well

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.20.9 tok/s decode

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

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

~$1,250 MSRP

RTX 3090 24GBBest value

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

Removes host-memory offload, which is usually the single biggest latency and throughput win.39.8 tok/s decode

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

Raises estimated decode speed by about 133%.

~$1,499 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.42.1 tok/s decode

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

Raises estimated decode speed by about 146%.

~$1,599 MSRP

Frequently asked questions

Can Tesla P100 16GB run Codestral 22B v0.1?

Yes, Tesla P100 16GB can run Codestral 22B v0.1 with a D grade (Very compromised (needs ~1.8 GB host RAM)). Expected decode speed: 17.1 tok/s.

How much VRAM does Codestral 22B v0.1 need?

Codestral 22B v0.1 (22B parameters) requires approximately 18.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1?

The recommended quantization for Codestral 22B v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 run at on Tesla P100 16GB?

On Tesla P100 16GB, Codestral 22B v0.1 achieves approximately 17.1 tokens per second decode speed with a time-to-first-token of 11342ms using Q4_K_M quantization.

Can Tesla P100 16GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on Tesla P100 16GB receives a D grade with 17.1 tok/s and 4K context.

What context window can Codestral 22B v0.1 use on Tesla P100 16GB?

On Tesla P100 16GB, Codestral 22B v0.1 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 22B v0.1 feels slow on Tesla P100 16GB?

See all results for Tesla P100 16GB See all hardware for Codestral 22B v0.1

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