Can Codestral 22B v0.1 IMat run on Radeon Pro W7800 32GB?

YES — Runs Great

C51Usable

Estimated from fit model

Codestral 22B v0.1 IMat needs ~20.1 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~25 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 20.1 GB, 25.3 tok/s, Runs well

20.1 GB required32.0 GB available

63% VRAM used

Fit status

Runs well

Decode

25.3 tok/s

TTFT

7645 ms

Safe context

90K

Memory

20.1 GB / 32.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 IMat on Radeon Pro W7800 32GB
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: 25.3 tok/s decode · 7.6s TTFT (warm) · 63 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	25.3 tok/s	4170 ms	90K
Coding	C	Runs well	25.3 tok/s	7645 ms	90K
Agentic Coding	C	Runs well	25.3 tok/s	11120 ms	90K
Reasoning	C	Runs well	25.3 tok/s	9035 ms	90K
RAG	C	Runs well	25.3 tok/s	13900 ms	90K

Inference speed

Codestral 22B v0.1 IMat inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Codestral 22B v0.1 IMat at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~90 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	89.5	Fits
RTX 4090 24GB	24 GB	Q4_K_M	57.1	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	51.5	Fits
RTX 3090 24GB	24 GB	Q4_K_M	48.8	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	41.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	34.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	34.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	34.6	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	32.8	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	24.3	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.9	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	17.9	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	16.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	8.6	Too big
RTX 3060 12GB	12 GB	Q4_K_M	5.4	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.2	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Codestral 22B v0.1 IMat (22B params) fits at each quantization level on Radeon Pro W7800 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C45
Q3_K_S	3	10.8 GB	Low	C46
NVFP4	4	12.3 GB	Medium	C47
Q4_K_M	4	13.4 GB	Medium	C47
Q5_K_M	5	15.8 GB	High	C49
Q6_K	6	18.0 GB	High	C49
Q8_0Best for your GPU	8	23.5 GB	Very High	C48
F16	16	45.1 GB	Maximum	F0

Get started

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

Run

lms load hf-legraphista--codestral-22b-v0-1-imat-gguf && lms server start

Upgrade options

Hardware that runs Codestral 22B v0.1 IMat well

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)

Raises estimated decode speed by about 38%.34.8 tok/s decode

Raises estimated decode speed by about 38%.

~$2,499 MSRP

RTX PRO 5000 Blackwell 48GBBest value

48 GB VRAM (+16)1344 GB/s (+768)

Raises estimated decode speed by about 232%.84.1 tok/s decode

Raises estimated decode speed by about 232%.

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

~$4,999 MSRP

Frequently asked questions

Can Radeon Pro W7800 32GB run Codestral 22B v0.1 IMat?

Yes, Radeon Pro W7800 32GB can run Codestral 22B v0.1 IMat with a C grade (Runs well). Expected decode speed: 25.3 tok/s.

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

Codestral 22B v0.1 IMat (22B parameters) requires approximately 20.1 GB of memory with Q4_K_M quantization.

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

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

What speed will Codestral 22B v0.1 IMat run at on Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, Codestral 22B v0.1 IMat achieves approximately 25.3 tokens per second decode speed with a time-to-first-token of 7645ms using Q4_K_M quantization.

Can Radeon Pro W7800 32GB run Codestral 22B v0.1 IMat for coding?

For coding workloads, Codestral 22B v0.1 IMat on Radeon Pro W7800 32GB receives a C grade with 25.3 tok/s and 90K context.

What context window can Codestral 22B v0.1 IMat use on Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, Codestral 22B v0.1 IMat can safely use up to 90K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon Pro W7800 32GB See all hardware for Codestral 22B v0.1 IMat

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-legraphista--codestral-22b-v0-1-imat-gguf-on-radeon-pro-w7800-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

25.3 tok/s

4170 ms

90K

Coding

Runs well

25.3 tok/s

7645 ms

90K

Agentic Coding

Runs well

25.3 tok/s

11120 ms

90K

Reasoning

Runs well

25.3 tok/s

9035 ms

90K

RAG

Runs well

25.3 tok/s

13900 ms

90K

Codestral 22B v0.1 IMat inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

89.5

Fits

RTX 4090 24GB

24 GB

Q4_K_M

57.1

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

51.5

Fits

RTX 3090 24GB

24 GB

Q4_K_M

48.8

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

41.5

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

34.8

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

34.8

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

34.6

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

32.8

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

24.3

Heavy offload

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

21.9

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

17.9

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

16.4

Fits

RTX 4070 12GB

12 GB

Q4_K_M

8.6

Too big

RTX 3060 12GB

12 GB

Q4_K_M

5.4

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.2

Too big

Quantization options

How Codestral 22B v0.1 IMat (22B params) fits at each quantization level on Radeon Pro W7800 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C45
Q3_K_S	3	10.8 GB	Low	C46
NVFP4	4	12.3 GB	Medium	C47
Q4_K_M	4	13.4 GB	Medium	C47
Q5_K_M	5	15.8 GB	High	C49
Q6_K	6	18.0 GB	High	C49
Q8_0Best for your GPU	8	23.5 GB	Very High	C48
F16	16	45.1 GB	Maximum	F0

Frequently asked questions

Can Radeon Pro W7800 32GB run Codestral 22B v0.1 IMat?

Yes, Radeon Pro W7800 32GB can run Codestral 22B v0.1 IMat with a C grade (Runs well). Expected decode speed: 25.3 tok/s.

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

Codestral 22B v0.1 IMat (22B parameters) requires approximately 20.1 GB of memory with Q4_K_M quantization.

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

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

What speed will Codestral 22B v0.1 IMat run at on Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, Codestral 22B v0.1 IMat achieves approximately 25.3 tokens per second decode speed with a time-to-first-token of 7645ms using Q4_K_M quantization.

Can Radeon Pro W7800 32GB run Codestral 22B v0.1 IMat for coding?

For coding workloads, Codestral 22B v0.1 IMat on Radeon Pro W7800 32GB receives a C grade with 25.3 tok/s and 90K context.

What context window can Codestral 22B v0.1 IMat use on Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, Codestral 22B v0.1 IMat can safely use up to 90K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.