RTX 3060 12GBコスパ重視12 GB VRAM (+4)360 GB/s (+72)
C
Adds memory headroom for longer context windows and future model growth.64 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$329 MSRP
Can Mamba Codestral 7B v0.1 run on RTX 3000 Ada Laptop 8GB?
YES — Tight Fit
C52Usable
Estimated from fit model
Mamba Codestral 7B v0.1 needs ~6.8 GB VRAM. RTX 3000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~57 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.
Select quantization to explore
Q4_K_M (Medium quality) — 6.8 GB, 56.6 tok/s, Tight fit
6.8 GB required8.0 GB available
85% VRAM used
Fit status
Tight fit
Decode
56.6 tok/s
TTFT
3419 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 3000 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: 56.6 tok/s decode · 3.4s TTFT (warm) · 142 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 | C | Runs well | 56.6 tok/s | 1865 ms | 40K |
| Coding | C | Tight fit | 56.6 tok/s | 3419 ms | 40K |
| Agentic Coding | C | Runs with offload | 56.6 tok/s | 4973 ms | 40K |
| Reasoning | C | Tight fit | 56.6 tok/s | 4041 ms | 40K |
| RAG | C | Runs with offload | 56.6 tok/s | 6217 ms | 40K |
Quantization options
How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on RTX 3000 Ada Laptop 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アップグレードオプション
Mamba Codestral 7B v0.1を快適に動かすハードウェア
RTX 3060 12GBコスパ重視RTX 5070 12GBコスパ最良12 GB VRAM (+4)672 GB/s (+384)
C
Raises estimated decode speed by about 101%.114 tok/sデコード
Raises estimated decode speed by about 101%.
Adds memory headroom for longer context windows and future model growth.
〜$549 MSRP
RTX 4070 12GBNVIDIAアップグレード12 GB VRAM (+4)504 GB/s (+216)
C
Raises estimated decode speed by about 80%.101.8 tok/sデコード
Raises estimated decode speed by about 80%.
Adds memory headroom for longer context windows and future model growth.
〜$599 MSRP
Frequently asked questions
Can RTX 3000 Ada Laptop 8GB run Mamba Codestral 7B v0.1?
Yes, RTX 3000 Ada Laptop 8GB can run Mamba Codestral 7B v0.1 with a C grade (Tight fit). Expected decode speed: 56.6 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 3000 Ada Laptop 8GB?
On RTX 3000 Ada Laptop 8GB, Mamba Codestral 7B v0.1 achieves approximately 56.6 tokens per second decode speed with a time-to-first-token of 3419ms using Q4_K_M quantization.
Can RTX 3000 Ada Laptop 8GB run Mamba Codestral 7B v0.1 for coding?
For coding workloads, Mamba Codestral 7B v0.1 on RTX 3000 Ada Laptop 8GB receives a C grade with 56.6 tok/s and 40K context.
What context window can Mamba Codestral 7B v0.1 use on RTX 3000 Ada Laptop 8GB?
On RTX 3000 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.
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