Can Codestral Mamba 7B run on RTX 4050 Laptop 6GB?

YES — With Offload

A75Great

Estimated from fit model

Codestral Mamba 7B needs ~6.3 GB VRAM. RTX 4050 Laptop 6GB has 6.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 6.3 GB, 25.9 tok/s, Runs with offload (needs ~0.2 GB host RAM)

6.3 GB required6.0 GB available

105% VRAM needed

0.3 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

25.9 tok/s

TTFT

7473 ms

Safe context

Memory

6.3 GB / 6.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsCodestral Mamba 7B on RTX 4050 Laptop 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: 25.9 tok/s decode · 7.5s TTFT (warm) · 65 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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.

Best improvement 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	A	Runs with offload (needs ~0 GB host RAM)	28.2 tok/s	3749 ms	8K
Coding	A	Runs with offload (needs ~0.2 GB host RAM)	25.9 tok/s	7473 ms	8K
Agentic Coding	B	Very compromised (needs ~0.5 GB host RAM)	22.1 tok/s	12732 ms	8K
Reasoning	A	Runs with offload (needs ~0.2 GB host RAM)	25.9 tok/s	8831 ms	8K
RAG	B	Very compromised (needs ~0.5 GB host RAM)	22.1 tok/s	15915 ms	8K

Quantization options

How Codestral Mamba 7B (7B params) fits at each quantization level on RTX 4050 Laptop 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A79
Q3_K_SBest for your GPU	3	3.4 GB	Low	A79
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 Codestral Mamba 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "mistralai/Mamba-Codestral-7B-v0.1" \
  --hf-file "Mamba-Codestral-7B-v0.1-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can RTX 4050 Laptop 6GB run Codestral Mamba 7B?

Yes, RTX 4050 Laptop 6GB can run Codestral Mamba 7B with a A grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 25.9 tok/s.

How much VRAM does Codestral Mamba 7B need?

Codestral Mamba 7B (7B parameters) requires approximately 6.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral Mamba 7B?

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

What speed will Codestral Mamba 7B run at on RTX 4050 Laptop 6GB?

On RTX 4050 Laptop 6GB, Codestral Mamba 7B achieves approximately 25.9 tokens per second decode speed with a time-to-first-token of 7473ms using Q4_K_M quantization.

Can RTX 4050 Laptop 6GB run Codestral Mamba 7B for coding?

For coding workloads, Codestral Mamba 7B on RTX 4050 Laptop 6GB receives a A grade with 25.9 tok/s and 8K context.

What context window can Codestral Mamba 7B use on RTX 4050 Laptop 6GB?

On RTX 4050 Laptop 6GB, Codestral Mamba 7B can safely use up to 8K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Codestral Mamba 7B feels slow on RTX 4050 Laptop 6GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 4050 Laptop 6GB See all hardware for Codestral Mamba 7B

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