Can Mamba Codestral 7B v0.1 run on RTX 2060 Super 8GB?

YES — Tight Fit

C53Usable

Estimated from fit model

Mamba Codestral 7B v0.1 needs ~6.8 GB VRAM. RTX 2060 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~70 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, 70.0 tok/s, Tight fit

6.8 GB required8.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

70.0 tok/s

TTFT

2766 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 2060 Super 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: 70.0 tok/s decode · 2.8s TTFT (warm) · 175 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	70.0 tok/s	1509 ms	40K
Coding	C	Tight fit	70.0 tok/s	2766 ms	40K
Agentic Coding	C	Runs with offload	70.0 tok/s	4024 ms	40K
Reasoning	C	Tight fit	70.0 tok/s	3269 ms	40K
RAG	C	Runs with offload	70.0 tok/s	5030 ms	40K

Inference speed

Mamba Codestral 7B v0.1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Mamba Codestral 7B v0.1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	92.6	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	92.6	Fits
RTX 4070 12GB	12 GB	Q4_K_M	88.5	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	64.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	59.3	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	56.6	Fits
RTX 3060 12GB	12 GB	Q4_K_M	55.6	Fits
RTX 4060 8GB	8 GB	Q4_K_M	53.5	Tight

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on RTX 2060 Super 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

Upgrade options

Hardware that runs Mamba Codestral 7B v0.1 well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.64 tok/s decode

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

~$329 MSRP

RTX 5070 12GBBest value

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

Raises estimated decode speed by about 63%.114 tok/s decode

Raises estimated decode speed by about 63%.

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

~$549 MSRP

RTX 4070 12GBNVIDIA upgrade

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

Raises estimated decode speed by about 45%.101.8 tok/s decode

Raises estimated decode speed by about 45%.

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

~$599 MSRP

Frequently asked questions

Can RTX 2060 Super 8GB run Mamba Codestral 7B v0.1?

Yes, RTX 2060 Super 8GB can run Mamba Codestral 7B v0.1 with a C grade (Tight fit). Expected decode speed: 70.0 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 2060 Super 8GB?

On RTX 2060 Super 8GB, Mamba Codestral 7B v0.1 achieves approximately 70.0 tokens per second decode speed with a time-to-first-token of 2766ms using Q4_K_M quantization.

Can RTX 2060 Super 8GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on RTX 2060 Super 8GB receives a C grade with 70.0 tok/s and 40K context.

What context window can Mamba Codestral 7B v0.1 use on RTX 2060 Super 8GB?

On RTX 2060 Super 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 2060 Super 8GB See 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-rtx-2060-super-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

70.0 tok/s

1509 ms

40K

Coding

Tight fit

70.0 tok/s

2766 ms

40K

Agentic Coding

Runs with offload

70.0 tok/s

4024 ms

40K

Reasoning

Tight fit

70.0 tok/s

3269 ms

40K

RAG

Runs with offload

70.0 tok/s

5030 ms

40K

Mamba Codestral 7B v0.1 inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

98.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

98.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

98.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

98.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

98.0

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

98.0

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

98.0

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

98.0

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

92.6

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

92.6

Fits

RTX 4070 12GB

12 GB

Q4_K_M

88.5

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

64.6

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

59.3

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

56.6

Fits

RTX 3060 12GB

12 GB

Q4_K_M

55.6

Fits

RTX 4060 8GB

8 GB

Q4_K_M

53.5

Tight

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on RTX 2060 Super 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

Frequently asked questions

Can RTX 2060 Super 8GB run Mamba Codestral 7B v0.1?

Yes, RTX 2060 Super 8GB can run Mamba Codestral 7B v0.1 with a C grade (Tight fit). Expected decode speed: 70.0 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 2060 Super 8GB?

On RTX 2060 Super 8GB, Mamba Codestral 7B v0.1 achieves approximately 70.0 tokens per second decode speed with a time-to-first-token of 2766ms using Q4_K_M quantization.

Can RTX 2060 Super 8GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on RTX 2060 Super 8GB receives a C grade with 70.0 tok/s and 40K context.

What context window can Mamba Codestral 7B v0.1 use on RTX 2060 Super 8GB?

On RTX 2060 Super 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.