Can Codestral Mamba 7B run on RTX 4070 Laptop 8GB?

YES — Runs Great

A80Great

Estimated from fit model

Codestral Mamba 7B needs ~6.5 GB VRAM. RTX 4070 Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~52 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 6.5 GB, 52.4 tok/s, Runs well

6.5 GB required8.0 GB available

81% VRAM used

Fit status

Runs well

Decode

52.4 tok/s

TTFT

3695 ms

Safe context

67K

Memory

6.5 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsCodestral Mamba 7B on RTX 4070 Laptop 8GB
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: 52.4 tok/s decode · 3.7s TTFT (warm) · 131 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	A	Runs well	52.4 tok/s	2015 ms	67K
Coding	A	Runs well	52.4 tok/s	3695 ms	67K
Agentic Coding	A	Tight fit	52.4 tok/s	5374 ms	67K
Reasoning	A	Runs well	52.4 tok/s	4366 ms	67K
RAG	A	Tight fit	52.4 tok/s	6718 ms	67K

Quantization options

How Codestral Mamba 7B (7B params) fits at each quantization level on RTX 4070 Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A78
Q3_K_S	3	3.4 GB	Low	A79
NVFP4	4

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

Your hardware

More models your RTX 4070 Laptop 8GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	A	18.8 tok/s
Qwen 3 8B	8B	A	24.3 tok/s

Frequently asked questions

Can RTX 4070 Laptop 8GB run Codestral Mamba 7B?

Yes, RTX 4070 Laptop 8GB can run Codestral Mamba 7B with a A grade (Runs well). Expected decode speed: 52.4 tok/s.

How much VRAM does Codestral Mamba 7B need?

Codestral Mamba 7B (7B parameters) requires approximately 6.5 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 4070 Laptop 8GB?

On RTX 4070 Laptop 8GB, Codestral Mamba 7B achieves approximately 52.4 tokens per second decode speed with a time-to-first-token of 3695ms using Q4_K_M quantization.

Can RTX 4070 Laptop 8GB run Codestral Mamba 7B for coding?

For coding workloads, Codestral Mamba 7B on RTX 4070 Laptop 8GB receives a A grade with 52.4 tok/s and 67K context.

What context window can Codestral Mamba 7B use on RTX 4070 Laptop 8GB?

On RTX 4070 Laptop 8GB, Codestral Mamba 7B can safely use up to 67K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for RTX 4070 Laptop 8GB See all hardware for Codestral Mamba 7B

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