Will It Run AI

Can Mamba Codestral 7B v0.1 run on Intel Arc B580 12GB?

YES — Runs Great

C53Usable
Estimated from fit model

Mamba Codestral 7B v0.1 needs ~7.2 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q4_K_M quantization, expect ~59 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
Share:

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) — 7.2 GB, 58.9 tok/s, Runs well
7.2 GB required12.0 GB available
60% VRAM used

Fit status

Runs well

Decode

58.9 tok/s

TTFT

3284 ms

Safe context

110K

Memory

7.2 GB / 12.0 GB

Memory breakdown

Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsMamba Codestral 7B v0.1 on Intel Arc B580 12GB
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: 58.9 tok/s decode · 3.3s TTFT (warm) · 147 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well58.9 tok/s1791 ms110K
CodingCRuns well58.9 tok/s3284 ms110K
Agentic CodingCRuns well58.9 tok/s4777 ms110K
ReasoningCRuns well58.9 tok/s3881 ms110K
RAGCRuns well58.9 tok/s5971 ms110K

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on Intel Arc B580 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowC49
Q3_K_S
3
3.4 GB
LowC49
NVFP4
4
3.9 GB
MediumC50
Q4_K_M
4
4.3 GB
MediumC51
Q5_K_M
5
5.0 GB
HighC52
Q6_K
6
5.7 GB
HighC52
Q8_0Best for your GPU
8
7.5 GB
Very HighC51
F16
16
14.3 GB
MaximumF0

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

Frequently asked questions

Can Intel Arc B580 12GB run Mamba Codestral 7B v0.1?

Yes, Intel Arc B580 12GB can run Mamba Codestral 7B v0.1 with a C grade (Runs well). Expected decode speed: 58.9 tok/s.

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

Mamba Codestral 7B v0.1 (7B parameters) requires approximately 7.2 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 B580 12GB?

On Intel Arc B580 12GB, Mamba Codestral 7B v0.1 achieves approximately 58.9 tokens per second decode speed with a time-to-first-token of 3284ms using Q4_K_M quantization.

Can Intel Arc B580 12GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on Intel Arc B580 12GB receives a C grade with 58.9 tok/s and 110K context.

What context window can Mamba Codestral 7B v0.1 use on Intel Arc B580 12GB?

On Intel Arc B580 12GB, Mamba Codestral 7B v0.1 can safely use up to 110K 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 B580 12GB?

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 B580 12GB 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 B580 12GBSee all hardware for Mamba Codestral 7B v0.1
Embed this result▼

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

<iframe src="https://willitrunai.com/embed/hf-gabriellarson--mamba-codestral-7b-v0-1-gguf-on-arc-b580-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: