Can Mamba Codestral 7B v0.1 run on Intel Arc A380 6GB?

BARELY — Tight on Memory

D38Poor

Estimated from fit model

Mamba Codestral 7B v0.1 needs ~6.6 GB VRAM. Intel Arc A380 6GB has 6.0 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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) — 6.6 GB, 15.1 tok/s, Very compromised (needs ~0.4 GB host RAM)

6.6 GB required6.0 GB available

110% VRAM needed

0.6 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.4 GB host RAM)

Decode

15.1 tok/s

TTFT

12813 ms

Safe context

Memory

6.6 GB / 6.0 GB

Offload

10%

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsMamba Codestral 7B v0.1 on Intel Arc A380 6GB
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: 15.1 tok/s decode · 12.8s TTFT (warm) · 38 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

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.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload (needs ~0.1 GB host RAM)	17.3 tok/s	6105 ms	4K
Coding	D	Very compromised (needs ~0.4 GB host RAM)	15.1 tok/s	12813 ms	4K
Agentic Coding	F	Too heavy	11.8 tok/s	23858 ms	4K
Reasoning	D	Very compromised (needs ~0.4 GB host RAM)	15.1 tok/s	15143 ms	4K
RAG	F	Too heavy	11.8 tok/s	29822 ms	4K

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on Intel Arc A380 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C54
Q3_K_SBest for your GPU	3	3.4 GB	Low	C53
NVFP4	4	3.9 GB	Medium	F0
Q4_K_M	4	4.3 GB	Medium	F0
Q5_K_M	5	5.0 GB	High	F0
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

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-Optionen

Hardware, die Mamba Codestral 7B v0.1 gut ausführt

Intel Arc A580 8GBGünstige Wahl

8 GB VRAM (+2)512 GB/s (+326)

Removes host-memory offload, which is usually the single biggest latency and throughput win.67.6 tok/s Dekodierung

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 348%.

ca. $179 MSRP

Intel Arc B570 10GBBestes Preis-Leistungs-Verhältnis

10 GB VRAM (+4)380 GB/s (+194)

Removes host-memory offload, which is usually the single biggest latency and throughput win.55.3 tok/s Dekodierung

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 266%.

ca. $219 MSRP

Intel Arc B580 12GBIntel-Upgrade

12 GB VRAM (+6)456 GB/s (+270)

Removes host-memory offload, which is usually the single biggest latency and throughput win.58.9 tok/s Dekodierung

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 290%.

ca. $249 MSRP

Frequently asked questions

Can Intel Arc A380 6GB run Mamba Codestral 7B v0.1?

Yes, Intel Arc A380 6GB can run Mamba Codestral 7B v0.1 with a D grade (Very compromised (needs ~0.4 GB host RAM)). Expected decode speed: 15.1 tok/s.

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

Mamba Codestral 7B v0.1 (7B parameters) requires approximately 6.6 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 A380 6GB?

On Intel Arc A380 6GB, Mamba Codestral 7B v0.1 achieves approximately 15.1 tokens per second decode speed with a time-to-first-token of 12813ms using Q4_K_M quantization.

Can Intel Arc A380 6GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on Intel Arc A380 6GB receives a D grade with 15.1 tok/s and 4K context.

What context window can Mamba Codestral 7B v0.1 use on Intel Arc A380 6GB?

On Intel Arc A380 6GB, Mamba Codestral 7B v0.1 can safely use up to 4K 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 A380 6GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Would CUDA be a better path than Intel Arc A380 6GB 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 A380 6GB See all hardware for Mamba Codestral 7B v0.1

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