Can Codestral 22B v0.1 IMat run on AMD Instinct MI350X 288GB?

YES — Runs Great

C45Usable

Estimated from fit model

Codestral 22B v0.1 IMat needs ~45.7 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q4_K_M quantization, expect ~308 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) — 45.7 GB, 308.0 tok/s, Runs well

45.7 GB required288.0 GB available

16% VRAM used

Fit status

Runs well

Decode

308.0 tok/s

TTFT

629 ms

Safe context

1.5M

Memory

45.7 GB / 288.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime0.9 GB

Headroom28.8 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 IMat on AMD Instinct MI350X 288GB
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: 308.0 tok/s decode · 629ms TTFT (warm) · 770 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	308.0 tok/s	350 ms	1.5M
Coding	C	Runs well	308.0 tok/s	629 ms	1.5M
Agentic Coding	C	Runs well	308.0 tok/s	914 ms	1.5M
Reasoning	C	Runs well	308.0 tok/s	743 ms	1.5M
RAG	C	Runs well	308.0 tok/s	1143 ms	1.5M

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 AMD Instinct MI350X 288GB (288.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	D35
Q3_K_S	3	10.8 GB	Low	D36
NVFP4	4	12.3 GB	Medium	D36
Q4_K_M	4	13.4 GB	Medium	D36
Q5_K_M	5	15.8 GB	High	D36
Q6_K	6	18.0 GB	High	D36
Q8_0	8	23.5 GB	Very High	D36
F16Best for your GPU	16	45.1 GB	Maximum	D38

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

Frequently asked questions

Can AMD Instinct MI350X 288GB run Codestral 22B v0.1 IMat?

Yes, AMD Instinct MI350X 288GB can run Codestral 22B v0.1 IMat with a C grade (Runs well). Expected decode speed: 308.0 tok/s.

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

Codestral 22B v0.1 IMat (22B parameters) requires approximately 45.7 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 AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, Codestral 22B v0.1 IMat achieves approximately 308.0 tokens per second decode speed with a time-to-first-token of 629ms using Q4_K_M quantization.

Can AMD Instinct MI350X 288GB run Codestral 22B v0.1 IMat for coding?

For coding workloads, Codestral 22B v0.1 IMat on AMD Instinct MI350X 288GB receives a C grade with 308.0 tok/s and 1.5M context.

What context window can Codestral 22B v0.1 IMat use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, Codestral 22B v0.1 IMat can safely use up to 1.5M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for AMD Instinct MI350X 288GB See all hardware for Codestral 22B v0.1 IMat

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<iframe src="https://willitrunai.com/embed/hf-legraphista--codestral-22b-v0-1-imat-gguf-on-instinct-mi350x-288gb" 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

308.0 tok/s

350 ms

1.5M

Coding

Runs well

308.0 tok/s

629 ms

1.5M

Agentic Coding

Runs well

308.0 tok/s

914 ms

1.5M

Reasoning

Runs well

308.0 tok/s

743 ms

1.5M

RAG

Runs well

308.0 tok/s

1143 ms

1.5M

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 AMD Instinct MI350X 288GB (288.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	D35
Q3_K_S	3	10.8 GB	Low	D36
NVFP4	4	12.3 GB	Medium	D36
Q4_K_M	4	13.4 GB	Medium	D36
Q5_K_M	5	15.8 GB	High	D36
Q6_K	6	18.0 GB	High	D36
Q8_0	8	23.5 GB	Very High	D36
F16Best for your GPU	16	45.1 GB	Maximum	D38

Frequently asked questions

Can AMD Instinct MI350X 288GB run Codestral 22B v0.1 IMat?

Yes, AMD Instinct MI350X 288GB can run Codestral 22B v0.1 IMat with a C grade (Runs well). Expected decode speed: 308.0 tok/s.

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

Codestral 22B v0.1 IMat (22B parameters) requires approximately 45.7 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 AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, Codestral 22B v0.1 IMat achieves approximately 308.0 tokens per second decode speed with a time-to-first-token of 629ms using Q4_K_M quantization.

Can AMD Instinct MI350X 288GB run Codestral 22B v0.1 IMat for coding?

For coding workloads, Codestral 22B v0.1 IMat on AMD Instinct MI350X 288GB receives a C grade with 308.0 tok/s and 1.5M context.

What context window can Codestral 22B v0.1 IMat use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, Codestral 22B v0.1 IMat can safely use up to 1.5M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.