Can Codestral 22B v0.1 i1 run on Radeon Pro W7900 48GB?

YES — Runs Great

C48Usable

Estimated from fit model

Codestral 22B v0.1 i1 needs ~21.7 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 21.7 GB, 38.0 tok/s, Runs well

21.7 GB required48.0 GB available

45% VRAM used

Fit status

Runs well

Decode

38.0 tok/s

TTFT

5097 ms

Safe context

179K

Memory

21.7 GB / 48.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 i1 on Radeon Pro W7900 48GB
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: 38.0 tok/s decode · 5.1s TTFT (warm) · 95 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	38.0 tok/s	2780 ms	179K
Coding	C	Runs well	38.0 tok/s	5097 ms	179K
Agentic Coding	C	Runs well	38.0 tok/s	7413 ms	179K
Reasoning	C	Runs well	38.0 tok/s	6023 ms	179K
RAG	C	Runs well	38.0 tok/s	9267 ms	179K

Inference speed

Codestral 22B v0.1 i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Codestral 22B v0.1 i1 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 i1 (22B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C42
Q3_K_S	3	10.8 GB	Low	C43
NVFP4	4	12.3 GB	Medium	C43
Q4_K_M	4	13.4 GB	Medium	C43
Q5_K_M	5	15.8 GB	High	C44
Q6_K	6	18.0 GB	High	C45
Q8_0Best for your GPU	8	23.5 GB	Very High	C47
F16	16	45.1 GB	Maximum	F0

Get started

Copy-paste commands to run Codestral 22B v0.1 i1 on your machine.

Run

lms load hf-mradermacher--codestral-22b-v0-1-i1-gguf && lms server start

Upgrade options

Hardware that runs Codestral 22B v0.1 i1 well

AMD Instinct MI210 64GBBudget pick

64 GB VRAM (+16)1638 GB/s (+774)

Raises estimated decode speed by about 118%.83 tok/s decode

Raises estimated decode speed by about 118%.

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

~$10,000 MSRP

Frequently asked questions

Can Radeon Pro W7900 48GB run Codestral 22B v0.1 i1?

Yes, Radeon Pro W7900 48GB can run Codestral 22B v0.1 i1 with a C grade (Runs well). Expected decode speed: 38.0 tok/s.

How much VRAM does Codestral 22B v0.1 i1 need?

Codestral 22B v0.1 i1 (22B parameters) requires approximately 21.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1 i1?

The recommended quantization for Codestral 22B v0.1 i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 i1 run at on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, Codestral 22B v0.1 i1 achieves approximately 38.0 tokens per second decode speed with a time-to-first-token of 5097ms using Q4_K_M quantization.

Can Radeon Pro W7900 48GB run Codestral 22B v0.1 i1 for coding?

For coding workloads, Codestral 22B v0.1 i1 on Radeon Pro W7900 48GB receives a C grade with 38.0 tok/s and 179K context.

What context window can Codestral 22B v0.1 i1 use on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, Codestral 22B v0.1 i1 can safely use up to 179K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon Pro W7900 48GB See all hardware for Codestral 22B v0.1 i1

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

38.0 tok/s

2780 ms

179K

Coding

Runs well

38.0 tok/s

5097 ms

179K

Agentic Coding

Runs well

38.0 tok/s

7413 ms

179K

Reasoning

Runs well

38.0 tok/s

6023 ms

179K

RAG

Runs well

38.0 tok/s

9267 ms

179K

Codestral 22B v0.1 i1 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 i1 (22B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C42
Q3_K_S	3	10.8 GB	Low	C43
NVFP4	4	12.3 GB	Medium	C43
Q4_K_M	4	13.4 GB	Medium	C43
Q5_K_M	5	15.8 GB	High	C44
Q6_K	6	18.0 GB	High	C45
Q8_0Best for your GPU	8	23.5 GB	Very High	C47
F16	16	45.1 GB	Maximum	F0

Frequently asked questions

Can Radeon Pro W7900 48GB run Codestral 22B v0.1 i1?

Yes, Radeon Pro W7900 48GB can run Codestral 22B v0.1 i1 with a C grade (Runs well). Expected decode speed: 38.0 tok/s.

How much VRAM does Codestral 22B v0.1 i1 need?

Codestral 22B v0.1 i1 (22B parameters) requires approximately 21.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1 i1?

The recommended quantization for Codestral 22B v0.1 i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 i1 run at on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, Codestral 22B v0.1 i1 achieves approximately 38.0 tokens per second decode speed with a time-to-first-token of 5097ms using Q4_K_M quantization.

Can Radeon Pro W7900 48GB run Codestral 22B v0.1 i1 for coding?

For coding workloads, Codestral 22B v0.1 i1 on Radeon Pro W7900 48GB receives a C grade with 38.0 tok/s and 179K context.

What context window can Codestral 22B v0.1 i1 use on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, Codestral 22B v0.1 i1 can safely use up to 179K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.