Can Mamba Codestral 7B v0.1 run on Intel Arc Pro B60 24GB?

YES — Runs Great

C48Usable

Estimated from fit model

Mamba Codestral 7B v0.1 needs ~8.4 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~66 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) — 8.4 GB, 66.3 tok/s, Runs well

8.4 GB required24.0 GB available

35% VRAM used

Fit status

Runs well

Decode

66.3 tok/s

TTFT

2919 ms

Safe context

320K

Memory

8.4 GB / 24.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsMamba Codestral 7B v0.1 on Intel Arc Pro B60 24GB
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: 66.3 tok/s decode · 2.9s TTFT (warm) · 166 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	66.3 tok/s	1592 ms	320K
Coding	C	Runs well	66.3 tok/s	2919 ms	320K
Agentic Coding	C	Runs well	66.3 tok/s	4246 ms	320K
Reasoning	C	Runs well	66.3 tok/s	3450 ms	320K
RAG	C	Runs well	66.3 tok/s	5308 ms	320K

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C44
Q3_K_S	3	3.4 GB	Low	C44
NVFP4	4	3.9 GB	Medium	C45
Q4_K_M	4	4.3 GB	Medium	C45
Q5_K_M	5	5.0 GB	High	C45
Q6_K	6	5.7 GB	High	C45
Q8_0	8	7.5 GB	Very High	C47
F16Best for your GPU	16	14.3 GB	Maximum	C50

Get started

Copy-paste commands to run Mamba Codestral 7B v0.1 on your machine.

Run

lms load hf-gabriellarson--mamba-codestral-7b-v0-1-gguf && lms server start

Upgrade options

Hardware that runs Mamba Codestral 7B v0.1 well

MacBook Pro M4 Max 36GBBudget pick

36 GB Unified (+12)

This setup is broadly balanced for this model.69.6 tok/s decode

~$2,499 MSRP

MacBook Pro M4 Max 48GBBest value

48 GB Unified (+24)546 GB/s (+90)

Raises estimated decode speed by about 40%.92.6 tok/s decode

Raises estimated decode speed by about 40%.

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

~$2,499 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run Mamba Codestral 7B v0.1?

Yes, Intel Arc Pro B60 24GB can run Mamba Codestral 7B v0.1 with a C grade (Runs well). Expected decode speed: 66.3 tok/s.

How much VRAM does Mamba Codestral 7B v0.1 need?

Mamba Codestral 7B v0.1 (7B parameters) requires approximately 8.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Mamba Codestral 7B v0.1?

The recommended quantization for Mamba Codestral 7B v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Mamba Codestral 7B v0.1 run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Mamba Codestral 7B v0.1 achieves approximately 66.3 tokens per second decode speed with a time-to-first-token of 2919ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on Intel Arc Pro B60 24GB receives a C grade with 66.3 tok/s and 320K context.

What context window can Mamba Codestral 7B v0.1 use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Mamba Codestral 7B v0.1 can safely use up to 320K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Mamba Codestral 7B v0.1 feels slow on Intel Arc Pro B60 24GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Would CUDA be a better path than Intel Arc Pro B60 24GB for Mamba Codestral 7B v0.1?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Arc Pro B60 24GB See all hardware for Mamba Codestral 7B v0.1

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<iframe src="https://willitrunai.com/embed/hf-gabriellarson--mamba-codestral-7b-v0-1-gguf-on-arc-pro-b60-24gb" 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

66.3 tok/s

1592 ms

320K

Coding

Runs well

66.3 tok/s

2919 ms

320K

Agentic Coding

Runs well

66.3 tok/s

4246 ms

320K

Reasoning

Runs well

66.3 tok/s

3450 ms

320K

RAG

Runs well

66.3 tok/s

5308 ms

320K

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C44
Q3_K_S	3	3.4 GB	Low	C44
NVFP4	4	3.9 GB	Medium	C45
Q4_K_M	4	4.3 GB	Medium	C45
Q5_K_M	5	5.0 GB	High	C45
Q6_K	6	5.7 GB	High	C45
Q8_0	8	7.5 GB	Very High	C47
F16Best for your GPU	16	14.3 GB	Maximum	C50