Can Mamba Codestral 7B v0.1 run on RTX 3070 8GB?

YES — Tight Fit

C53Usable

Estimated from fit model

Mamba Codestral 7B v0.1 needs ~6.8 GB VRAM. RTX 3070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~84 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 6.8 GB, 84.0 tok/s, Tight fit

6.8 GB required8.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

84.0 tok/s

TTFT

2305 ms

Safe context

40K

Memory

6.8 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsMamba Codestral 7B v0.1 on RTX 3070 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: 84.0 tok/s decode · 2.3s TTFT (warm) · 210 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	B	Runs well	84.0 tok/s	1257 ms	40K
Coding	C	Tight fit	84.0 tok/s	2305 ms	40K
Agentic Coding	C	Runs with offload	84.0 tok/s	3352 ms	40K
Reasoning	C	Tight fit	84.0 tok/s	2724 ms	40K
RAG	C	Runs with offload	84.0 tok/s	4190 ms	40K

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on RTX 3070 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C53
Q3_K_S	3	3.4 GB	Low	C53
NVFP4	4	3.9 GB	Medium	C53
Q4_K_M	4	4.3 GB	Medium	C53
Q5_K_MBest for your GPU	5	5.0 GB	High	C52
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

Opções de upgrade

Hardware que roda bem Mamba Codestral 7B v0.1

RTX 5070 12GBOpção econômica

12 GB VRAM (+4)672 GB/s (+224)

Raises estimated decode speed by about 36%.114 tok/s decodificação

Raises estimated decode speed by about 36%.

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

~$549 MSRP

RTX 4070 12GBMelhor custo-benefício

12 GB VRAM (+4)504 GB/s (+56)

Adds memory headroom for longer context windows and future model growth.101.8 tok/s decodificação

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

~$599 MSRP

RTX 4070 Super 12GBUpgrade NVIDIA

12 GB VRAM (+4)504 GB/s (+56)

Adds memory headroom for longer context windows and future model growth.104.5 tok/s decodificação

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

~$599 MSRP

Frequently asked questions

Can RTX 3070 8GB run Mamba Codestral 7B v0.1?

Yes, RTX 3070 8GB can run Mamba Codestral 7B v0.1 with a C grade (Tight fit). Expected decode speed: 84.0 tok/s.

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

Mamba Codestral 7B v0.1 (7B parameters) requires approximately 6.8 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 RTX 3070 8GB?

On RTX 3070 8GB, Mamba Codestral 7B v0.1 achieves approximately 84.0 tokens per second decode speed with a time-to-first-token of 2305ms using Q4_K_M quantization.

Can RTX 3070 8GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on RTX 3070 8GB receives a C grade with 84.0 tok/s and 40K context.

What context window can Mamba Codestral 7B v0.1 use on RTX 3070 8GB?

On RTX 3070 8GB, Mamba Codestral 7B v0.1 can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 3070 8GB See all hardware for Mamba Codestral 7B v0.1

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