RTX 3060 12GB经济之选12 GB VRAM (+4)360 GB/s (+104)
C
Adds memory headroom for longer context windows and future model growth.64 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can Mamba Codestral 7B v0.1 run on RTX 4070 Laptop 8GB?
YES — Tight Fit
C52Usable
Estimated from fit model
Mamba Codestral 7B v0.1 needs ~6.8 GB VRAM. RTX 4070 Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~52 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, 52.4 tok/s, Tight fit
6.8 GB required8.0 GB available
85% VRAM used
Fit status
Tight fit
Decode
52.4 tok/s
TTFT
3695 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 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 | C | Runs well | 52.4 tok/s | 2015 ms | 40K |
| Coding | C | Tight fit | 52.4 tok/s | 3695 ms | 40K |
| Agentic Coding | C | Runs with offload | 52.4 tok/s | 5374 ms | 40K |
| Reasoning | C | Tight fit | 52.4 tok/s | 4366 ms | 40K |
| RAG | C | Runs with offload | 52.4 tok/s | 6718 ms | 40K |
Quantization options
How Mamba Codestral 7B v0.1 (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 | 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升级选项
能流畅运行 Mamba Codestral 7B v0.1 的硬件
RTX 3060 12GB经济之选RTX 5070 12GB最佳性价比12 GB VRAM (+4)672 GB/s (+416)
C
Raises estimated decode speed by about 118%.114 tok/s 解码
Raises estimated decode speed by about 118%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 12GBNVIDIA 升级12 GB VRAM (+4)504 GB/s (+248)
C
Raises estimated decode speed by about 94%.101.8 tok/s 解码
Raises estimated decode speed by about 94%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Frequently asked questions
Can RTX 4070 Laptop 8GB run Mamba Codestral 7B v0.1?
Yes, RTX 4070 Laptop 8GB can run Mamba Codestral 7B v0.1 with a C grade (Tight fit). Expected decode speed: 52.4 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 4070 Laptop 8GB?
On RTX 4070 Laptop 8GB, Mamba Codestral 7B v0.1 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 Mamba Codestral 7B v0.1 for coding?
For coding workloads, Mamba Codestral 7B v0.1 on RTX 4070 Laptop 8GB receives a C grade with 52.4 tok/s and 40K context.
What context window can Mamba Codestral 7B v0.1 use on RTX 4070 Laptop 8GB?
On RTX 4070 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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