Will It Run AI

Can Mamba Codestral 7B v0.1 run on RTX 2000 Ada Laptop 8GB?

YES — Tight Fit

C52Usable
Estimated from fit model

Mamba Codestral 7B v0.1 needs ~6.8 GB VRAM. RTX 2000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~50 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced
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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, 50.3 tok/s, Tight fit
6.8 GB required8.0 GB available
85% VRAM used

Fit status

Tight fit

Decode

50.3 tok/s

TTFT

3847 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 2000 Ada 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: 50.3 tok/s decode · 3.8s TTFT (warm) · 126 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well50.3 tok/s2098 ms40K
CodingCTight fit50.3 tok/s3847 ms40K
Agentic CodingCRuns with offload50.3 tok/s5595 ms40K
ReasoningCTight fit50.3 tok/s4546 ms40K
RAGCRuns with offload50.3 tok/s6994 ms40K

Quantization options

How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on RTX 2000 Ada Laptop 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowC53
Q3_K_S
3
3.4 GB
LowC53
NVFP4
4
3.9 GB
MediumC53
Q4_K_M
4
4.3 GB
MediumC53
Q5_K_MBest for your GPU
5
5.0 GB
HighC52
Q6_K
6
5.7 GB
HighF0
Q8_0
8
7.5 GB
Very HighF0
F16
16
14.3 GB
MaximumF0

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

Opciones de mejora

Hardware que ejecuta bien Mamba Codestral 7B v0.1

NVIDIARTX 3060 12GBOpción económica
12 GB VRAM (+4)360 GB/s (+104)
C
Sube la velocidad estimada de decodificación alrededor de un 27%.64 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 27%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$329 MSRP

NVIDIARTX 5070 12GBMejor relación calidad-precio
12 GB VRAM (+4)672 GB/s (+416)
C
Sube la velocidad estimada de decodificación alrededor de un 127%.114 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 127%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$549 MSRP

NVIDIARTX 4070 12GBMejora NVIDIA
12 GB VRAM (+4)504 GB/s (+248)
C
Sube la velocidad estimada de decodificación alrededor de un 102%.101.8 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 102%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$599 MSRP

Frequently asked questions

Can RTX 2000 Ada Laptop 8GB run Mamba Codestral 7B v0.1?

Yes, RTX 2000 Ada Laptop 8GB can run Mamba Codestral 7B v0.1 with a C grade (Tight fit). Expected decode speed: 50.3 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 2000 Ada Laptop 8GB?

On RTX 2000 Ada Laptop 8GB, Mamba Codestral 7B v0.1 achieves approximately 50.3 tokens per second decode speed with a time-to-first-token of 3847ms using Q4_K_M quantization.

Can RTX 2000 Ada Laptop 8GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on RTX 2000 Ada Laptop 8GB receives a C grade with 50.3 tok/s and 40K context.

What context window can Mamba Codestral 7B v0.1 use on RTX 2000 Ada Laptop 8GB?

On RTX 2000 Ada Laptop 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 2000 Ada Laptop 8GBSee all hardware for Mamba Codestral 7B v0.1
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